Production risk analysis method, device and equipment based on large model and storage medium

Abstract

The invention discloses a production risk analysis method, device and equipment based on a large model and a storage medium, and relates to the field of production verification and the field of financial science and technology, and the production risk analysis method based on the large model comprises the steps: collecting application change content before a target application is put into production, performing risk assessment on the application change content through a preset first language large model, and determining a to-be-verified object in the target application; according to the verification information configuration template and the object type of the to-be-verified object, determining a verification configuration file of the to-be-verified object, and according to the verification configuration file, performing commissioning verification in commissioning on the target application; and carrying out risk analysis on the to-be-verified object which does not pass the production verification through a preset second big language model, and carrying out risk processing on the to-be-verified object which does not pass the production verification according to a risk analysis result. According to the technical scheme, more accurate production verification prevention and control are realized.

Description

Technical Field

[0001] The present invention relates to the field of production verification and financial technology, and in particular to a large model-based production risk analysis method, device, equipment and storage medium. Background Art

[0002] After the data concentration construction carried out in the past 20 years, the management level of domestic commercial banks has gradually improved, and business innovation and core competitiveness have also been further enhanced. Most of the application systems of commercial banks involve the management of customer funds. When there are problems with the functions of the application systems, it is easy to cause a large range of impacts. Therefore, it is necessary to ensure that the functions of the application systems are normal and available. The functions of commercial bank application systems need to undergo multiple rounds of testing before they are put into production, and the production verification link after the functions are put into production is done well to ensure that the online functions are normal and available.

[0003] Traditional production verification methods mostly rely on manual verification by production personnel, or manually formulate relevant rules / scripts during and after the production process to verify the functions. This cannot ensure that deviations occur during the production verification process. The manual production verification process lacks objectivity, and the verification results are too subjective. The degree of credibility requires further analysis and verification. Summary of the invention

[0004] The present invention provides a large-model-based production risk analysis method, device, equipment and storage medium to achieve more accurate production verification and prevention and control.

[0005] According to one aspect of the present invention, a large model-based production risk analysis method is provided, the method comprising:

[0006] Collect application change content before the target application is put into production, and perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application; wherein the application change content refers to the code change of the application code of the target application;

[0007] Determine the verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform commissioning verification on the target application in commissioning according to the verification configuration file; wherein the verification information configuration template is used to determine the verification information and verification rules of the object to be verified;

[0008] A risk analysis is performed on the objects to be verified that have failed the production verification through a preset second language model, and risk processing is performed on the objects to be verified that have failed the production verification based on the risk analysis results, so that the target application can be put into production.

[0009] According to another aspect of the present invention, a large model-based production risk analysis device is provided, the device comprising:

[0010] A risk assessment module is used to collect application change content before the target application is put into production, and to perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application; wherein the application change content refers to the code change of the application code of the target application;

[0011] A production verification module, used to determine the verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform production verification in production on the target application according to the verification configuration file; wherein the verification information configuration template is used to determine the verification information and verification rules of the object to be verified;

[0012] The risk analysis module is used to perform risk analysis on the objects to be verified that have failed the production verification through a preset second language model, and to perform risk processing on the objects to be verified that have failed the production verification according to the risk analysis results, so that the target application can complete the production.

[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 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 execute the large model-based production risk analysis 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 large model-based production risk analysis method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the large model-based production risk analysis method described in any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention uses a large model to identify the application changes of the target application before the production, determines the objects to be verified in the target application, reduces the errors caused by manual methods, and realizes more accurate production verification and control. It also determines the objects to be verified before the production of the target application, and performs production verification and risk analysis during production to realize full-process control of the production process of the target application.

[0020] 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

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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.

[0022] Figure 1 is a flow chart of a large model-based production risk analysis method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a large model-based production risk analysis method provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a flow chart of a large model-based production risk analysis method provided according to Embodiment 3 of the present invention;

[0025] Figure 4 It is a structural schematic diagram of a large-model-based production risk analysis device provided according to a fourth embodiment of the present invention;

[0026] Figure 5 It is a structural schematic diagram of an electronic device for implementing the large model-based production risk analysis method of an embodiment of the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] It should be noted that the terms "first", "second", 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.

[0029] Embodiment 1

[0030] Figure 1 A flowchart of a large model-based production risk analysis method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of putting an application into production. The method can be executed by a large model-based production risk analysis device. The large model-based production risk analysis device can be implemented in the form of hardware and / or software. The large model-based production risk analysis device can be configured in various general computing devices, such as computing devices integrated with a language large model. Figure 1 As shown, the method includes:

[0031] S110 , collecting application change content before the target application is put into production, and performing risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application.

[0032] The target application may refer to the application system to be put into production; the application change content refers to the code change of the application code of the target application. Optionally, the change types of the application change content can be divided into key code changes, variable changes, database script changes, and document changes. It should be noted that production can refer to the project completion acceptance, transfer to the production department or management department, and put into production or delivery for use.

[0033] Optionally, a risk assessment is performed on the application change content through a preset first language big model to determine the object to be verified in the target application, including: risk identification is performed on the application change content according to the preset first language big model to determine the object to be verified corresponding to the target application under the application change content.

[0034] The first language model presets the mapping relationship between the application change content and the object to be verified. The first language model can be trained according to the historical application change content of the application to determine the mapping relationship between the application change content and the object to be verified. Optionally, the historical application change content is data carrying the label of the object to be verified, and the mapping relationship between the application change content and the object to be verified can be dynamically updated according to the data processing process of the first language model.

[0035] Specifically, the application change content of the target application before production can be collected, the application change content can be identified through the knowledge retrieval capability of the preset first language model, and at least one object to be verified corresponding to the application change content can be output.

[0036] In an optional implementation of an embodiment of the present invention, the target application's application change content can be obtained by monitoring the editing operations of the target application. For example, by monitoring key code changes, it can be determined that the application change content is a front-end code change, a back-end code change, a core function code change, or a cloud environment code change. By monitoring variable changes, it can be determined that the application change content is a dynamic variable change and a cloud environment parameter change, etc. By monitoring database script changes, it can be determined that the application change content is a database table change and a script command change, etc. By monitoring document changes, it can be determined that the application change content is a firewall change, etc. Exemplarily, the mapping relationship between the application change content determined after training the first language large model and the object to be verified can be shown in Table 1:

[0037] Table 1

[0038]

[0039] S120: Determine a verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform production verification on the target application according to the verification configuration file.

[0040] The verification information configuration template can be used to determine the verification information and verification rules of the object to be verified. It should be noted that the verification information configuration template can be regarded as a standardized verification information module, in which multiple standardized verification information configuration fields are set.

[0041] Specifically, the verification information configuration template can be configured with verification information according to the object type of at least one object to be verified, a verification configuration file for at least one object to be verified can be generated, and a production verification can be performed on at least one object to be verified of at least the target application during the production process based on the verification configuration file to ensure that the application changes of the target application before production meet the production standards.

[0042] S130. Perform risk analysis on the objects to be verified that have failed the production verification by using a preset second language model, and perform risk processing on the objects to be verified that have failed the production verification according to the risk analysis results, so that the target application can complete production.

[0043] Specifically, the preset second largest prediction model can be used to perform risk analysis on the objects to be verified that have failed the production verification, and risk emergency processing can be performed on the objects to be verified that have failed the production verification based on the risk analysis results given by the second largest language model, and risk repair can be performed on the objects to be verified so that the target meets the production standards and is ready for production.

[0044] Optionally, in an embodiment of the present invention, after risk processing is performed on the object to be verified that has not passed the production verification according to the risk analysis result, the process further includes: performing production verification again on the object to be verified that has not passed the production verification to determine whether the risk of the object to be verified has been eliminated, and if not, performing version rollback on the object to be verified. If the production verification of the object to be verified passes, the target application can continue to be put into production.

[0045] The availability of the target application is ensured by rolling back the previous version of the objects to be verified that have not passed the production verification and whose risks have not been eliminated by risk treatment.

[0046] Optionally, in the embodiment of the present invention, after the target application is put into production, the object to be verified of the target application may be further subjected to production verification to ensure the accuracy of the target application put into production.

[0047] The technical solution of the embodiment of the present invention uses a large model to identify the application changes of the target application before the production, determines the objects to be verified in the target application, reduces the errors caused by manual methods, and realizes more accurate production verification and control. It also determines the objects to be verified before the production of the target application, and performs production verification and risk analysis during production to realize full-process control of the production process of the target application.

[0048] Embodiment 2

[0049] Figure 2 The flowchart of a large-model-based production risk analysis method provided in the second embodiment of the present invention is further refined on the basis of the above embodiment, and provides specific steps for determining the verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and performing production verification on the target application according to the verification configuration file. It should be noted that for the undetailed parts of the embodiments of the present invention, please refer to the relevant descriptions of other embodiments, which will not be repeated here. Figure 2 As shown, the method includes:

[0050] S210: Collect application change content before the target application is put into production, and perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application.

[0051] S220: Determine the field content of the configuration field corresponding to the object type of the object to be verified according to a preset mapping relationship between the object type and the field content of the configuration field.

[0052] S230: Fill the configuration fields in the verification information configuration template according to the field contents of the configuration fields corresponding to the object to be verified, and generate a standardized verification configuration file for the object to be verified.

[0053] S240. Perform production verification on the object to be verified according to the verification configuration file.

[0054] Specifically, in an embodiment of the present invention, the verification information configuration template stores the mapping relationship between the object type of each object to be verified integrated according to the historical production verification data and the configuration field in the verification information configuration template. After determining the object type of the object to be verified, the remaining configuration fields in the verification information configuration template can be filled according to the mapping relationship to generate a standardized verification configuration file corresponding to the object to be verified. The production verification module can further identify the standardized verification configuration file and perform production verification on the object to be verified. Optionally, the verification information configuration template provides standardized configuration fields for verification information used for production verification, and when the object type of the object to be verified is different, the field format for the same configuration field is also different.

[0055] Optionally, the configuration fields in the verification information configuration template may be as shown in Table 2.

[0056] The verification object information and the verification object supplementary information can limit the verification content of the object to be verified, that is, the limitation of the verification method or rule. Optionally, for the verification object supplementary information configuration field, the technicians in this field can customize the configuration to limit the verification rules. The comparison value and the expected value can be used to determine the production verification result of the object to be verified. The verification subclass corresponds to the object type of the object to be verified, such as http verification, command verification, log keyword verification, service verification, etc. The verification category and verification type can be used to limit the verification field to which the verification subclass belongs. For example, when the verification subclass is http verification, the verification object is http request, and the verification category and verification type can be interface verification and technical verification, respectively. The production version can be used to represent the version number of the target application corresponding to the object to be verified.

[0057] Table 2

[0058]

[0059] S250, performing risk analysis on the objects to be verified that have failed the production verification through a preset second language model, and performing risk processing on the objects to be verified that have failed the production verification according to the risk analysis results, so that the target application can complete the production.

[0060] The technical solution of the embodiment of the present invention ensures the consistency of the execution effect of the verification work by adopting a standardized verification information configuration template to perform production verification on the verification object, and at the same time solves the problem of inconsistent asset standards of production verification cases of various applications and the inability to well accumulate and reuse production verification case assets.

[0061] Embodiment 3

[0062] Figure 3 This is a flowchart of a large model-based production risk analysis method provided in the third embodiment of the present invention. This embodiment further refines the above embodiment and provides specific steps for performing risk analysis on objects to be verified that have failed the production verification through a preset second large language model, and performing risk handling on objects to be verified that have failed the production verification according to the risk analysis results. It should be noted that for the undetailed parts of the embodiments of the present invention, please refer to the relevant descriptions of other embodiments, which will not be repeated here. Figure 3 As shown, the method includes:

[0063] S310: Collect application change content before the target application is put into production, and perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application.

[0064] S320: Determine a verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform production verification on the target application according to the verification configuration file.

[0065] S330. Perform risk analysis on the verification results of the object to be verified that failed the production verification by using the second largest language model to determine the risk analysis result of the object to be verified.

[0066] S340. Conduct risk screening on the object to be verified based on the risk analysis results.

[0067] Among them, the verification result may include the verification type, the verification result type and the verification result; the risk analysis result may include the risk level and risk response suggestions.

[0068] In an embodiment of the present invention, the second largest language model can be regarded as a pre-trained knowledge question-answering model, and the data format of the input data of the second largest language model can be verification type + verification result type + verification result content. For example, for the verification item of business indicator-business success rate, the verification result type is indicator configuration error, and the verification result content is that the ES interface returns empty traffic information. Please analyze the risk content and evaluate its risk level. The second largest language model can perform risk analysis on the above problems and give risk levels and troubleshooting suggestions. For example, if the risk level is low, it may be that the template name or the time configuration of the verification object is wrong, resulting in incorrect data extraction. It is recommended to check the template name and configuration to ensure that the time setting is correct; and in the cloud management platform log search, check whether there is an abnormality in the sent log, such as the application is not connected to the log center, which needs to be located and repaired; confirm whether the log exists, and enter relevant information to search, such as application name, date and template name. Optionally, after the risk of the object to be verified is checked according to the risk analysis results of the second language model, the risk analysis results of the second language model can be evaluated and fed back to form knowledge precipitation to further optimize the second language model.

[0069] The technical solution of the embodiment of the present invention uses a large language model to perform risk analysis on the objects to be verified that have failed the production verification, and provides risk resolution ideas in the first place, thereby improving the risk handling efficiency and the production efficiency of the target application.

[0070] Embodiment 4

[0071] Figure 4 This is a schematic diagram of the structure of a large model-based production risk analysis device provided in Example 4 of the present invention. Figure 4 As shown, the device comprises:

[0072] The risk assessment module 410 is used to collect application change content of the target application before it is put into production, and to perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application; wherein the application change content refers to the code change of the application code of the target application;

[0073] The production verification module 420 is used to determine the verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform production verification in production on the target application according to the verification configuration file; wherein the verification information configuration template is used to determine the verification information and verification rules of the object to be verified;

[0074] The risk analysis module 430 is used to perform risk analysis on the objects to be verified that have failed the production verification through a preset second language model, and perform risk processing on the objects to be verified that have failed the production verification according to the risk analysis results, so that the target application can complete the production.

[0075] The technical solution of the embodiment of the present invention uses a large model to identify the application changes of the target application before the production, determines the objects to be verified in the target application, reduces the errors caused by manual methods, and realizes more accurate production verification and control. It also determines the objects to be verified before the production of the target application, and performs production verification and risk analysis during production to realize full-process control of the production process of the target application.

[0076] Optionally, the production verification module 420 includes:

[0077] A field determination unit, used to determine the field content of the configuration field corresponding to the object type of the object to be verified according to a preset mapping relationship between the object type and the field content of the configuration field;

[0078] A field filling unit, used to fill the configuration fields in the verification information configuration template according to the field content of the configuration fields corresponding to the object to be verified, and generate a standardized verification configuration file for the object to be verified;

[0079] A production verification unit is used to perform production verification on the object to be verified according to the verification configuration file.

[0080] Optionally, the configuration field includes verification object information, verification object supplementary information, comparison value and expected value; the verification object information and verification object supplementary information are used to limit the verification content of the object to be verified, and the comparison value and expected value are used to determine the production verification result of the object to be verified.

[0081] Optionally, the risk analysis module 430 can be specifically used to: perform risk analysis on the verification results of the object to be verified that failed the production verification through the second largest language model, and determine the risk analysis results of the object to be verified; wherein the risk analysis results include risk levels and risk response suggestions, and the verification results include verification types, verification result types and verification results; and perform risk screening on the object to be verified based on the risk analysis results.

[0082] Optionally, the device further comprises:

[0083] The rollback module is used to conduct production verification again for the object to be verified that has failed the production verification, to determine whether the risk of the object to be verified has been eliminated. If not, the version of the object to be verified is rolled back.

[0084] Optionally, the risk assessment module 410 can be specifically used to: identify risks of the application change content according to a preset first language big model, and determine the object to be verified corresponding to the target application under the application change content; wherein the first language big model presets the mapping relationship between the application change content and the object to be verified.

[0085] The large-model-based production risk analysis device provided in the embodiment of the present invention can execute the large-model-based production risk analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0086] Embodiment 5

[0087] Figure 5 A schematic diagram of an electronic device 510 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.

[0088] like Figure 5 As shown, the electronic device 510 includes at least one processor 511, and a memory connected to the at least one processor 511 in communication, such as a read-only memory (ROM) 512, a random access memory (RAM) 513, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 511 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 512 or the computer program loaded from the storage unit 518 to the random access memory (RAM) 513. In the RAM 513, various programs and data required for the operation of the electronic device 510 can also be stored. The processor 511, the ROM 512, and the RAM 513 are connected to each other via a bus 514. An input / output (I / O) interface 515 is also connected to the bus 514.

[0089] A number of components in the electronic device 510 are connected to the I / O interface 515, including: an input unit 516, such as a keyboard, a mouse, etc.; an output unit 517, such as various types of displays, speakers, etc.; a storage unit 518, such as a disk, an optical disk, etc.; and a communication unit 519, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 519 allows the electronic device 510 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0090] The processor 511 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 511 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 511 executes the various methods and processes described above, such as a large model-based production risk analysis method.

[0091] In some embodiments, the large model-based production risk analysis method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 518. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 510 via the ROM 512 and / or the communication unit 519. When the computer program is loaded into the RAM 513 and executed by the processor 511, one or more steps of the large model-based production risk analysis method described above may be performed. Alternatively, in other embodiments, the processor 511 may be configured to execute the large model-based production risk analysis method in any other appropriate manner (e.g., by means of firmware).

[0092] 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), complex 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 production risk analysis method based on a large model, characterized in that: include: Collect application change content before the target application is put into production, and perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application; wherein the application change content refers to the code change of the application code of the target application; Determine the verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform commissioning verification on the target application in commissioning according to the verification configuration file; wherein the verification information configuration template is used to determine the verification information and verification rules of the object to be verified; A risk analysis is performed on the objects to be verified that have failed the production verification through a preset second language model, and risk processing is performed on the objects to be verified that have failed the production verification based on the risk analysis results, so that the target application can be put into production.

2. The method according to claim 1, characterized in that Determining a verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and performing production verification in production on the target application according to the verification configuration file, including: Determine the field content of the configuration field corresponding to the object type of the object to be verified according to the mapping relationship between the preset object type and the field content of the configuration field; Filling the configuration fields in the verification information configuration template according to the field contents of the configuration fields corresponding to the object to be verified, and generating a standardized verification configuration file for the object to be verified; According to the verification configuration file, the object to be verified is subjected to production verification.

3. The method according to claim 2, characterized in that The configuration fields include verification object information, verification object supplementary information, comparison value and expected value; the verification object information and verification object supplementary information are used to limit the verification content of the object to be verified, and the comparison value and expected value are used to determine the production verification result of the object to be verified.

4. The method according to claim 1, characterized in that: The pre-set second language model is used to perform risk analysis on the objects to be verified that have not passed the production verification, and risk treatment is performed on the objects to be verified that have not passed the production verification according to the risk analysis results, including: Performing risk analysis on the verification results of the object to be verified that failed the production verification through the second language model, and determining the risk analysis result of the object to be verified; wherein the risk analysis result includes the risk level and risk response suggestions, and the verification result includes the verification type, the verification result type and the verification result; Perform risk screening on the object to be verified based on the risk analysis results.

5. The method according to claim 1, characterized in that After risk processing is performed on the objects to be verified that have not passed the production verification according to the risk analysis results, it also includes: For the objects to be verified that have failed the production verification, the production verification is carried out again to determine whether the risks of the objects to be verified have been eliminated. If not, the version of the objects to be verified is rolled back.

6. The method according to claim 1, characterized in that Performing a risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application, including: The application change content is identified for risk according to a preset first language model, and the object to be verified corresponding to the target application under the application change content is determined; wherein the first language model presets a mapping relationship between the application change content and the object to be verified.

7. A production risk analysis device based on a large model, characterized in that: include: A risk assessment module is used to collect application change content of the target application before it goes into production, and to perform risk assessment on the application change content through a preset first language large model to determine the object to be verified in the target application; wherein the application change content refers to the code change of the application code of the target application; A production verification module, used to determine the verification configuration file of the object to be verified according to the verification information configuration template and the object type of the object to be verified, and perform production verification in production on the target application according to the verification configuration file; wherein the verification information configuration template is used to determine the verification information and verification rules of the object to be verified; The risk analysis module is used to perform risk analysis on the objects to be verified that have failed the production verification through a preset second language model, and to perform risk processing on the objects to be verified that have failed the production verification according to the risk analysis results, so that the target application can complete the production.

8. 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 execute the large model-based production risk analysis method according to any one of claims 1 to 6.

9. 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 large model-based production risk analysis method described in any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the large model-based production risk analysis method according to any one of claims 1 to 6.

Images