Cross-system function consistency verification method, device, equipment, medium and product

Abstract

The invention discloses a cross-system function consistency verification method, device and equipment, a medium and a product. Preprocessing the message data migrated to the target system to obtain target message data; classifying each piece of target message data by using a target classification model to obtain a message type of each piece of message data; based on each message data, executing a function corresponding to the message type in the target system, and obtaining an execution result of each message data; based on each message type, the execution result of each message data and a preset type result library, determining the consistency of the function in the target system corresponding to each message type and the function in the source system; the preset type result library comprises each preset type result template, each preset type result template comprises a preset message type and an operation result of the preset message type, and the operation result of the preset message type is determined based on to-be-migrated message data belonging to the preset message type in the source system. And the verification efficiency and accuracy are effectively improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of information processing, and in particular, to a method, apparatus, device, medium, and product for cross-system function consistency verification. Background Art

[0002] With the rapid development of Internet technology and the rise of hardware, the financial industry is gradually reducing its dependence on mainframes and turning to new self-controllable systems in order to enhance the performance and security of financial systems. Due to the extremely high requirements for reliability, stability, and accuracy in financial systems, how to ensure the smooth transition of transactions from old systems to new systems has become a key challenge currently faced.

[0003] After years of stable operation, the old system supports tens of millions of business functions. Each business function can trigger multiple application scenarios. Different operations by customers or tellers and different business requirements will result in different parameter inputs when calling the same interface, thereby triggering different logical branches of the program and leading to different transaction results. In the initial stage of the development of the new system, its main goal is to achieve consistency with the functions of the old system, that is, for the same parameter input, it is required that the new system and the old system produce the same transaction result, so as to remain transparent to customers and businesses and smoothly achieve the smooth transition of business functions. Based on this requirement, the verification of the consistency of functions between the new and old systems is particularly important.

[0004] However, currently, for the verification of the consistency of functions between the new and old systems, it mainly relies on manual checking of transaction results, and this method has problems of low efficiency and accuracy. Summary of the Invention

[0005] The present invention provides a method, apparatus, device, medium, and product for cross-system function consistency verification to solve the problems of low efficiency and accuracy existing in the existing cross-system function consistency verification method.

[0006] According to one aspect of the present invention, there is provided a method for cross-system function consistency verification, including:

[0007] Preprocessing each message data migrated to the target system to obtain each target message data;

[0008] Based on each target message data, using a target classification model for classification to obtain the message type of each message data;

[0009] Based on each message data, execute the function corresponding to the message type in the target system to obtain the execution result of each message data;

[0010] Based on the execution results of the respective message types and message data and a preset type result library, determine the consistency between the functions in the target system corresponding to the respective message types and the functions in the source system; wherein, the preset type result library includes various preset type result templates, each preset type result template includes a preset message type and the running result of the preset message type, and the running result of the preset message type is determined based on the message data to be migrated of the preset message type in the source system.

[0011] According to another aspect of the present invention, there is provided a cross-system function consistency verification device, including:

[0012] A data processing module, configured to preprocess each piece of message data migrated to the target system to obtain each target message data;

[0013] A data classification module, configured to classify based on the respective target message data by using a target classification model to obtain the message types of the respective message data;

[0014] A result obtaining module, configured to execute the functions corresponding to the message types of the respective message data in the target system to obtain the execution results of the respective message data;

[0015] A function verification module, configured to determine the consistency between the functions in the target system corresponding to the respective message types and the functions in the source system based on the respective message types, the execution results of the respective message data, and a preset type result library; wherein, the preset type result library includes various preset type result templates, each preset type result template includes a preset message type and the running result of the preset message type, and the running result of the preset message type is determined based on the message data to be migrated of the preset message type in the source system.

[0016] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the cross-system function consistency verification method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and when the computer instructions are used by a processor to execute, the cross-system function consistency verification method according to any embodiment of the present invention is realized.

[0021] According to another aspect of the present invention, there is provided a computer program product including a computer program which, when executed by a processor, implements the cross-system function consistency verification method according to any embodiment of the present invention.

[0022] The technical solution provided by the embodiments of the present invention preprocesses each message data migrated to a target system to obtain each target message data; classifies each target message data by using a target classification model to obtain the message type of each message data; based on each message data, executes the function corresponding to the message type in the target system to obtain the execution result of each message data; and determines the consistency between the function in the target system corresponding to each message type and the function in the source system based on each message type, the execution result of each message data, and a preset type result library, where the preset type result library includes each preset type result template, each preset type result template includes a preset message type and the operation result of the preset message type, and the operation result of the preset message type is determined based on the message data to be migrated of the preset message type in the source system. Through the above technical solution, the target classification model is used to determine each message type, and based on each message data, the function corresponding to the message type is executed in the target system to obtain the execution result of each message data. Furthermore, the function consistency verification is performed in combination with the preset type result library including the preset type result template, and the entire verification process does not require manual intervention, effectively improving the verification efficiency and accuracy.

[0023] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used 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

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0025] Figure 1 is a flowchart of a cross-system function consistency verification method provided by Embodiment 1 of the present invention;

[0026] Figure 2 is a schematic structural diagram of a cross-system function consistency verification device provided by Embodiment 2 of the present invention;

[0027] Figure 3It is a schematic structural diagram of an electronic device provided in Embodiment 3 of the present invention. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances 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 "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 It is a flowchart of a cross-system function consistency verification method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of verifying the consistency of the functions of an old system and a new system. This method can be executed by a cross-system function consistency verification device, which can be implemented in the form of hardware and / or software, and the cross-system function consistency verification device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0032] S110. Perform preprocessing on each message data migrated to the target system to obtain each target message data.

[0033] In this embodiment, the target system can be understood as the system to which the message data needs to be migrated, that is, the new system. Preprocessing can be understood as the processing operations performed on the message data after each message data is migrated to the target system. Preprocessing can include format conversion, data cleaning operations, and data desensitization operations, etc.

[0034] Specifically, after each message data is migrated to the target system, preprocessing is performed on these message data to obtain the target message data corresponding to each message data.

[0035] S120. Classify the target message data using a target classification model to obtain the message types of the message data.

[0036] In this embodiment, the target classification model can be understood as a pre-trained model for classifying message data. The target classification model can be a neural network model or a machine learning model. The message types include, but are not limited to, customer information query types, transaction detail query types, historical activity query types, account management types, rights and interests management types, points management types, etc.

[0037] Specifically, for each target message data in the target message data, use the target classification model to classify the current target message data to obtain the message type of the message data corresponding to the current target message data.

[0038] It should be noted that if the target classification model is a neural network model, the current target message data can be directly input into the target classification model to output the message type; if the target classification model is a machine learning model, feature extraction is performed on the current target message data. For example, extract the transaction type, amount, timestamp, source address, and target address in the current target message data, as well as features that can identify data status changes or sensitivity, etc. Then, input the extracted features into the target classification model to output the message type.

[0039] S130. Based on the message data, execute the function corresponding to the message type in the target system to obtain the execution results of the message data.

[0040] In this embodiment, each message type corresponds to a function, and this function is used to achieve the business goals in the system.

[0041] Specifically, for each message data in the message data, execute the function corresponding to the message type to which the current message data belongs in the target system to obtain the execution result of the current message data.

[0042] S140. Based on the message types, the execution results of the message data, and a preset type result library, determine the consistency between the functions in the target system corresponding to the message types and the functions in the source system; where the preset type result library includes various preset type result templates, and each preset type result template includes a preset message type and the running result of the preset message type, and the running result of the preset message type is determined based on the message data to be migrated belonging to the preset message type in the source system.

[0043] In this embodiment, the source system can be understood as the system that needs to migrate message data to the target system, that is, the legacy system. The preset message type can be understood as the category obtained after classifying the message data in the source system. The preset message types include but are not limited to customer information query types, transaction detail query types, historical activity query types, account management types, rights and interests management types, points management types, etc.

[0044] The preset type result library can be understood as a library that is pre-set and used to perform consistency verification on the functions in the source system and the functions in the target system. The preset type result library can be a Redis library. The preset type result library includes multiple preset type result templates, and each preset type result template includes a preset message type and the running result of the preset message type. For example, in the Redis library, the key is the preset message type, and the value is the running result of the preset message type. Among them, the running result of the preset message type is determined based on the message data belonging to the preset message type in the source system.

[0045] Specifically, a preset type result library is constructed in advance; based on the execution results of each piece of message data, the target execution result of each message type is determined; furthermore, based on each message type, the target execution result of each message type, and the preset type result library, the consistency between the functions in the target system and the functions in the source system corresponding to each message type is determined.

[0046] Among them, the preset type result library can be constructed in the following way:

[0047] Preprocess each piece of message data to be migrated to obtain each piece of processed message data; based on each piece of processed message data, use the target classification model for classification to obtain the preset message types of each piece of message data to be migrated; based on each piece of message data to be migrated, execute the functions corresponding to the preset message types in the source system to obtain the execution results of each piece of message data to be migrated; for each preset message type, splice the execution results of each piece of message data to be migrated belonging to the current preset message type to be used as the running result of the current preset message type. Furthermore, based on the current preset message type and the running result of the current preset message type, determine the preset type result template; furthermore, based on each preset type result template, determine the preset type result library.

[0048] The technical solution provided in the first embodiment of the present invention preprocesses each message data migrated to the target system to obtain each target message data; classifies the target message data by using a target classification model to obtain the message types of the message data; based on each message data, executes the function corresponding to the message type in the target system to obtain the execution results of each message data; based on the message types, the execution results of each message data, and a preset type result library, determines the consistency between the functions in the target system corresponding to the message types and the functions in the source system; where the preset type result library includes each preset type result template, and each preset type result template includes a preset message type and the operation result of the preset message type, and the operation result of the preset message type is determined based on the message data to be migrated of the preset message type in the source system. Through the above technical solution, the target classification model is used to determine each message type, and based on each message data, the function corresponding to the message type is executed in the target system to obtain the execution results of each message data. Furthermore, the consistency verification of the function is performed in combination with the preset type result library including the preset type result template, and the entire verification process does not require manual intervention, effectively improving the verification efficiency and accuracy.

[0049] In some embodiments, the operation result of the preset message type is a preset hash value;

[0050] The determining the consistency between the functions in the target system corresponding to the message types and the functions in the source system based on the message types, the execution results of each message data, and the preset type result library includes: for each message type among the message types, based on the execution results of each message data of the current message type, determines the target execution result of the current message type, and converts the target execution result into a hash value; based on the current message type, searches for a target preset type result template from the preset type result library; in the case where the hash value is consistent with the preset hash value of the target preset type result template, determines that the function in the target system corresponding to the current message type is consistent with the function in the source system; in the case where the hash value is inconsistent with the preset hash value of the target preset type result template, determines that the function in the target system corresponding to the current message type is inconsistent with the function in the source system. Through the above technical solution,

[0051] In this embodiment, the target execution result of the current message type is formed by splicing the execution results of each message data of the current message type. The target execution result can be converted into a hash value through a preset hash algorithm.

[0052] Through the above technical solution, the execution result of each message type is converted into a hash value and compared with the preset hash value in the preset type result library, realizing the consistency verification of the target system function. Moreover, by using hash values for comparison, there is no need to manually compare the results item by item, significantly improving the verification efficiency, saving time and labor costs, and effectively ensuring the stability of the system.

[0053] In some embodiments, the preprocessing includes data cleaning operations and / or data desensitization operations.

[0054] In this embodiment, the data cleaning operation includes removing duplicate, abnormal or irrelevant data from the message data; the data desensitization operation includes performing desensitization processing on the data to ensure that sensitive information is not leaked.

[0055] In some embodiments, the target classification model is trained in the following manner: Obtain a labeled data set and an unlabeled data set, where the labeled data set includes first sample data with class labels, and the unlabeled data set includes second sample data without assigned class labels. The sample data is historical transaction message data in the source system; preprocess the labeled data set and the unlabeled data set to obtain a target labeled data set and a target unlabeled data set; use the target labeled data set to train a preset classification model to obtain an initial classification model; based on the target unlabeled data set, use the initial classification model to determine the pseudo-class labels of each second sample data; based on the second sample data with pseudo-class labels, expand the labeled data set; use the expanded labeled data set to train the initial classification model to obtain the target classification model.

[0056] In this embodiment, the preset classification model can be understood as a pre-constructed model for classifying message data. The preset classification model can be a neural network model or a machine learning model. This embodiment takes a neural network model as an example for illustration. The pseudo-class label can be understood as the class label assigned to these second sample data according to the prediction result after the initial classification model predicts the second sample data in the unlabeled data set.

[0057] Specifically, in order to improve the adaptability of the model, this embodiment uses a semi-supervised learning algorithm to train the classification model:

[0058] First, obtain the historical transaction message data in the source system, label part of the historical transaction message data to obtain first sample data with class labels, form a labeled data set, and the remaining data is second sample data without assigned class labels, forming an unlabeled data set; preprocess the labeled data set and the unlabeled data set to obtain a target labeled data set and a target unlabeled data set; where the preprocessing can include data cleaning operations and / or data desensitization operations.

[0059] Furthermore, the preset classification model is trained using the target labeled dataset to obtain an initial classification model; based on the target unlabeled dataset, the initial classification model is used to determine the pseudo-class labels of each second sample data. It should be noted that the initial classification model can directly output the pseudo-class labels of the second sample data, or can output the confidence levels of each second sample data belonging to each class label. Furthermore, based on the confidence levels of each class label, the pseudo-class labels of the second sample data are determined. Among them, the confidence level is expressed as the probability distribution of the class label.

[0060] Furthermore, based on the second sample data with pseudo-class labels, the labeled dataset is expanded; the initial classification model is trained using the expanded labeled dataset to obtain a target classification model.

[0061] Through the above technical solution, the dependence on a large number of labeled data is effectively reduced, the manual labeling cost is reduced, and the generalization ability of the classification model and the accuracy of classification are improved.

[0062] In some embodiments, the determining the pseudo-class labels of each second sample data based on the target unlabeled dataset using the initial classification model includes: predicting using the initial classification model based on the target unlabeled dataset to obtain the confidence levels of each second sample data belonging to each class label; based on the confidence levels of each second sample data belonging to each class label, determining the pseudo-class labels of each second sample data.

[0063] In this embodiment, the confidence level can be understood as the probability distribution corresponding to the class label.

[0064] Specifically, predicting using the initial classification model based on the target unlabeled dataset to obtain the confidence levels of each second sample data belonging to each class label. Furthermore, for each second sample data, based on the confidence levels of the current second sample data belonging to each class label, the pseudo-class label of the current second sample data is determined. It should be noted that this embodiment does not limit the method for determining the pseudo-class label. For example, it can be determined by the following methods:

[0065] The class label with the maximum confidence level can be used as the pseudo-class label of the current second sample data; or a confidence level threshold can be set, and the class labels with confidence levels greater than the confidence level threshold can be used as the pseudo-class labels of the current second sample data.

[0066] Through the above technical solution, by combining the judgment of the confidence level, more accurate pseudo-class labels can be assigned to the unlabeled data, reducing the interference of label errors on model training, and laying a foundation for further improving the accuracy of message classification data and function consistency verification.

[0067] In some embodiments, determining the pseudo-class labels of the second sample data based on the confidence levels of the second sample data belonging to various class labels includes: for each second sample data among the second sample data, determining the class label with the highest confidence level as the pseudo-class label of the current second sample data. Through this solution, the efficiency of determining the pseudo-class labels is effectively improved, laying a foundation for further improving the efficiency of message classification data and function consistency verification.

[0068] Optionally, after the model training is completed, to improve its performance and reliability, the initial classification model can also be evaluated and optimized. That is, a validation set is constructed, and the performance of the model is evaluated through evaluation metrics such as accuracy, recall rate, F1 value, etc.

[0069] Embodiment 2

[0070] Figure 2 is a schematic structural diagram of a cross-system function consistency verification device provided in Embodiment 2 of the present invention. As Figure 2 shown, the device includes:

[0071] A data processing module 21, configured to preprocess each message data migrated to the target system to obtain each target message data;

[0072] A data classification module 22, configured to classify each message data based on the target classification model to obtain the message types of each message data;

[0073] A result obtaining module 23, configured to execute the functions corresponding to the message types of each message data in the target system based on each message data to obtain the execution results of each message data;

[0074] A function verification module 24, configured to determine the consistency between the functions in the target system and the functions in the source system corresponding to each message type based on each message type, the execution results of each message data, and a preset type result library; wherein, the preset type result library includes each preset type result template, and each preset type result template includes a preset message type and the operation result of the preset message type, and the operation result of the preset message type is determined based on the message data to be migrated of the preset message type in the source system.

[0075] The technical solution provided in Embodiment 2 of the present invention uses a target classification model to determine each message type, executes the functions corresponding to the message types of each message data in the target system based on each message data to obtain the execution results of each message data, and further, combines the preset type result library including the preset type result templates to perform function consistency verification. The entire verification process does not require manual intervention, effectively improving the verification efficiency and accuracy.

[0076] Optionally, the operation result of the preset message type is a preset hash value;

[0077] Optionally, the function verification module 24 includes:

[0078] A result determination unit, configured to, for each message type in each message type, determine a target execution result of the current message type based on execution results of message data belonging to the current message type, and convert the target execution result into a hash value;

[0079] A template search unit, configured to search for a target preset type result template from the preset type result library based on the current message type;

[0080] A first determination unit, configured to determine that functions in the target system corresponding to the current message type are the same as functions in the source system when the hash value is the same as a preset hash value of the target preset type result template;

[0081] A second determination unit, configured to determine that functions in the target system corresponding to the current message type are different from functions in the source system when the hash value is different from a preset hash value of the target preset type result template.

[0082] Optionally, the target classification model is trained in the following manner:

[0083] Obtain a labeled data set and an unlabeled data set, where the labeled data set includes first sample data with class labels, and the unlabeled data set includes second sample data without assigned class labels, and the sample data is historical transaction message data in the source system;

[0084] Preprocess the labeled data set and the unlabeled data set to obtain a target labeled data set and a target unlabeled data set;

[0085] Train a preset classification model using the target labeled data set to obtain an initial classification model;

[0086] Based on the target unlabeled data set, use the initial classification model to determine pseudo-class labels of each second sample data;

[0087] Expand the labeled data set based on the second sample data with pseudo-class labels;

[0088] Train the initial classification model using the expanded labeled data set to obtain a target classification model.

[0089] Optionally, the determining, based on the target unlabeled data set, of pseudo-class labels of each second sample data using the initial classification model includes:

[0090] Based on the target unlabeled dataset, use the initial classification model to make predictions to obtain the confidence levels of each second sample data belonging to various category labels;

[0091] Based on the confidence levels of each second sample data belonging to various category labels, determine the pseudo-category labels of each second sample data.

[0092] Optionally, the determining the pseudo-category labels of each second sample data based on the confidence levels of each second sample data belonging to various category labels includes:

[0093] For each second sample data in each second sample data, determine the category label with the highest confidence level as the pseudo-category label of the current second sample data.

[0094] Optionally, the preprocessing includes a data cleaning operation and / or a data desensitization operation.

[0095] The cross-system function consistency verification device provided by the embodiments of the present invention can execute the cross-system function consistency verification method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0096] Embodiment III

[0097] Figure 3 It is a schematic structural diagram of an electronic device provided by Embodiment III of the present invention. This electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, 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 only examples and are not intended to limit the implementation of the present invention described herein and / or claimed.

[0098] Such as Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or the computer programs loaded from the storage unit 18 into 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 via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0099] Multiple 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 disc, 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 via a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 11 can be various general-purpose and / or special-purpose 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 dedicated 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 cross-system function consistency verification method.

[0101] In some embodiments, the cross-system function consistency verification method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto 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 cross-system function consistency verification method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the cross-system function consistency verification method by any other appropriate means (e.g., by means of firmware).

[0102] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments 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 receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0104] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0107] The computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0108] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0109] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0110] An embodiment of the present invention further provides a computer program product, including a computer program and / or instructions, where the computer program, when executed by a processor, implements the cross-system functional consistency verification method provided in any embodiment of the present application.

[0111] In the process of implementing the computer program product, computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0112] Note that the above is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments here. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A cross-system functional consistency verification method, characterized in that Including: Preprocessing each piece of message data migrated to the target system to obtain each target message data; Based on the respective target message data, using a target classification model for classification to obtain the message types of the respective message data; Based on each piece of message data, execute the function corresponding to the message type in the target system to obtain the execution results of each piece of message data; Based on the respective message types, the execution results of each piece of message data, and a preset type result library, determine the consistency between the functions in the target system corresponding to the respective message types and the functions in the source system; wherein, the preset type result library includes each preset type result template, and each preset type result template includes a preset message type and the operation result of the preset message type, and the operation result of the preset message type is determined based on the message data to be migrated of the preset message type in the source system.

2. The method according to claim 1, characterized in that, The operation result of the preset message type is a preset hash value; The determining the consistency between the functions in the target system corresponding to the respective message types and the functions in the source system based on the respective message types, the execution results of each piece of message data, and the preset type result library includes: For each message type in the respective message types, based on the execution results of each piece of message data of the current message type, determine the target execution result of the current message type, and convert the target execution result into a hash value; Based on the current message type, search for a target preset type result template from the preset type result library; In the case where the hash value is consistent with the preset hash value of the target preset type result template, determine that the function in the target system corresponding to the current message type is consistent with the function in the source system; In the case where the hash value is inconsistent with the preset hash value of the target preset type result template, determine that the function in the target system corresponding to the current message type is inconsistent with the function in the source system.

3. The method according to claim 1, wherein The target classification model is trained through the following method: Obtain a labeled data set and an unlabeled data set, wherein the labeled data set includes first sample data with category labels, and the unlabeled data set includes second sample data without assigned category labels, and the sample data is historical transaction message data in the source system; Preprocess the labeled data set and the unlabeled data set to obtain a target labeled data set and a target unlabeled data set; Use the target labeled data set to train a preset classification model to obtain an initial classification model; Based on the target unlabeled data set, use the initial classification model to determine the pseudo-category labels of each second sample data; Based on the second sample data with pseudo-category labels, expand the labeled data set; Use the expanded labeled data set to train the initial classification model to obtain a target classification model.

4. The method according to claim 3, wherein The determining the pseudo-category labels of each second sample data based on the target unlabeled data set and using the initial classification model includes: Based on the target unlabeled data set, use the initial classification model for prediction to obtain the confidence levels of each second sample data belonging to various category labels; Determine the pseudo-class labels of the second sample data based on the confidence levels of the second sample data belonging to various class labels.

5. The method according to claim 4, wherein The determining the pseudo-class labels of the second sample data based on the confidence levels of the second sample data belonging to various class labels includes: For each second sample data in the second sample data, determine the class label with the highest confidence level as the pseudo-class label of the current second sample data.

6. The method according to claim 1, wherein The preprocessing includes a data cleaning operation and / or a data desensitization operation.

7. A cross-system function consistency verification device, characterized in that, It includes: A data processing module, configured to preprocess each message data migrated to the target system to obtain each target message data; A data classification module, configured to classify based on the target message data by using a target classification model to obtain the message types of the message data; A result obtaining module, configured to execute the functions corresponding to the message types in the target system based on each message data to obtain the execution results of each message data; A function verification module, configured to determine the consistency between the functions in the target system corresponding to the message types and the functions in the source system based on the message types, the execution results of the message data, and a preset type result library; wherein, the preset type result library includes each preset type result template, and each preset type result template includes a preset message type and the running result of the preset message type, and the running result of the preset message type is determined based on the message data to be migrated belonging to the preset message type in the source system.

8. An electronic device, characterized in that, The electronic device includes: 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 cross-system function consistency verification method according to any one of claims 1-6.

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 the processor to implement the cross-system function consistency verification method according to any one of claims 1-6 when executed.

10. A computer program product, characterized in that, The computer program product includes a computer program, and the computer program implements the cross-system function consistency verification method according to any one of claims 1-6 when executed by the processor.

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