Model migration method, device, electronic device and computer-readable medium

Abstract

The embodiments of the present disclosure disclose a model migration method, device, electronic device and computer-readable medium. A specific implementation of the method includes: classifying each business user information; generating each business user portrait information; dividing each business user customer group, combining each sub-business user information group set, obtaining each business customer group fusion group, and generating each matching degree information; determining the sub-business warning model that meets the preset matching degree condition as the target sub-business warning model; inputting the detection request information into the target sub-business warning model to obtain warning information; migrating the sub-business warning model that meets the preset model migration condition. This implementation can adaptively migrate the model in a high-concurrency scenario to reduce the server load, and can process one type of detection request information through a separate target server, thereby improving the response speed and reducing the number of server downtimes.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a model migration method, device, electronic device, and computer-readable medium. Background Art

[0002] When using a business warning model that matches user information to detect user warnings, the accuracy of the detected warnings can be improved. Currently, when using individual business warning models to detect user warnings, the common approach is to first train each business warning model directly using business user information samples. The trained business warning models are then integrated into a single server to facilitate user warning detection.

[0003] However, in practice, it is found that when using the above method to use various business warning models to detect user warning information, the following technical problems often occur:

[0004] The trained business warning models are integrated into one server. When a large number of detection request information is input into the trained business warning models, the server load is large, resulting in a long response time of the server and a high number of server downtimes.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0006] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure provide a model migration method, apparatus, electronic device, and computer-readable medium to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In the first aspect, some embodiments of the present disclosure provide a model migration method, which includes: generating each business customer group fusion group based on the acquired business user information; in response to the detection request information of the business warning information detected, generating the matching degree information between the above detection request information and each business customer group fusion group in the above business customer group fusion group based on the above detection request information and the above business customer group fusion group, and obtaining each matching degree information; according to the above matching degree information, the sub-business warning model that meets the preset matching degree condition in each sub-business warning model trained by the above business customer group fusion group is determined as the target sub-business warning model corresponding to the above detection request information, so as to perform adaptation processing on the sub-business warning model, wherein the sub-business warning model in the above-mentioned pre-trained sub-business warning model corresponds to the business customer group fusion group in the above-mentioned business customer group fusion group, and the above-mentioned sub-business warning model meets the preset matching degree condition. The sub-business warning model in the business warning model is obtained through training of the corresponding business customer group fusion group; the above-mentioned detection request information is input into the above-mentioned target sub-business warning model to obtain warning information corresponding to the above-mentioned detection request information; in response to detecting that the above-mentioned warning information meets the preset alarm conditions, the request processing details information of each sub-business warning model in the above-mentioned sub-business warning models is obtained to obtain each request processing details information, wherein the request processing details information in the above-mentioned each request processing details information includes request processing quantity information and request processing time information; based on the above-mentioned each request processing details information, it is determined whether there is a sub-business warning model that meets the preset model migration conditions in the above-mentioned each sub-business warning model; in response to detecting that there is a sub-business warning model that meets the preset model migration conditions in the above-mentioned each sub-business warning model, the sub-business warning model that meets the above-mentioned preset model migration conditions is migrated.

[0009] On the second aspect, some embodiments of the present disclosure provide a model migration device, including a first generation unit, configured to generate each business customer group fusion group based on the acquired business user information; a second generation unit, configured to generate, in response to the detection request information of the detected business warning information, matching information between the above detection request information and each business customer group fusion group in the above business customer group fusion group according to the above detection request information and the above business customer group fusion group, to obtain each matching information; a first determination unit, configured to determine, based on the above matching information, the sub-business warning model that meets the preset matching condition in the each sub-business warning model trained by the above business customer group fusion group as the target sub-business warning model corresponding to the above detection request information, so as to perform adaptation processing on the sub-business warning model, wherein the sub-business warning model in the above-mentioned pre-trained sub-business warning model corresponds to the business customer group fusion group in the above-mentioned each business customer group fusion group, and the above-mentioned each sub-business warning model The sub-business warning model in is obtained by training the corresponding business customer group fusion group; the input unit is configured to input the above-mentioned detection request information into the above-mentioned target sub-business warning model to obtain the warning information corresponding to the above-mentioned detection request information; the acquisition unit is configured to obtain the request processing detail information of each sub-business warning model in the above-mentioned sub-business warning models in response to detecting that the above-mentioned warning information meets the preset alarm condition, and obtain each request processing detail information, wherein the request processing detail information in the above-mentioned each request processing detail information includes request processing quantity information and request processing duration information; the second determination unit is configured to determine whether there is a sub-business warning model that meets the preset model migration condition in the above-mentioned each sub-business warning model according to the above-mentioned each request processing detail information; the migration unit is configured to perform migration processing on the sub-business warning model that meets the above-mentioned preset model migration condition in response to detecting that there is a sub-business warning model that meets the preset model migration condition in the above-mentioned each sub-business warning model.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any one of the implementations of the first or second aspect above is implemented.

[0012] The above-described embodiments of the present disclosure have the following beneficial effects: A model migration method according to some embodiments of the present disclosure can reduce server load, shorten server response time, and reduce server downtime. The high server load, long server response time, and frequent server downtime are caused by integrating the trained business warning models into a single server. When a large number of detection request messages are input into each trained business warning model, the server load is high, resulting in long server response time and frequent server downtime. Based on this, the model migration method according to some embodiments of the present disclosure first generates business customer group fusion groups based on the acquired business user information. This generates sets of sub-business user information groups, which can be further used to train each business warning model. Then, in response to detection request information for detecting business warning information, matching information is generated between the detection request information and each of the business customer group fusion groups, based on the detection request information and the business customer group fusion groups, to obtain matching information. This allows the matching degree between the detection request information and each of the business customer group fusion groups to be determined. Next, based on the matching information, the pre-trained sub-service warning models that meet preset matching conditions are identified as target sub-service warning models corresponding to the detection request information, thereby adapting the sub-service warning models. The pre-trained sub-service warning models correspond to the service customer group integration groups within the service customer group integration groups, and the sub-service warning models within the pre-trained sub-service warning models are trained using the corresponding service customer group integration groups. Thus, an adapted sub-service warning model is obtained. The detection request information is then input into the target sub-service warning model to obtain warning information corresponding to the detection request information. Thus, warning information for the target user is obtained. Next, in response to detecting that the warning information meets preset alarm conditions, request processing details are obtained for each of the pre-trained sub-service warning models, obtaining respective request processing details. The request processing details within the pre-trained sub-service warning models include information on the number of requests processed and information on the duration of the request processing. Thus, request processing details for each sub-service warning model are obtained. Then, based on the detailed information about each request processing, it is determined whether any of the aforementioned sub-business warning models meets the preset model migration criteria. This allows the determination of whether any business warning models are to be migrated. Finally, in response to detecting that any of the aforementioned sub-business warning models meets the preset model migration criteria, the sub-business warning models meeting the preset model migration criteria are migrated. Thus, the sub-business warning models meeting the migration criteria can be migrated to reduce server load.Also, when there is a business warning model that meets the above-mentioned preset migration conditions in the server, the business warning model that meets the above-mentioned preset migration conditions will be migrated, so that the model can be adaptively migrated in a high-concurrency scenario to reduce the server load, and a type of detection request information can be processed by a separate target server, thereby improving the response speed and reducing the number of server downtimes. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flowchart of some embodiments of the model migration method according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of the model migration device according to the present disclosure;

[0016] Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] With regard to the collection, storage, and use of user personal information (such as borrowing and repayment records, user ID numbers) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subjects, and obtaining the authorization and consent of the personal information subjects in advance.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 The flow chart 100 of some embodiments of the model migration method according to the present disclosure is shown. The model migration method includes the following steps:

[0025] Step 101: Generate various service customer group integration groups based on the acquired service user information.

[0026] In some embodiments, the execution entity of the model migration method (e.g., a computing device) can generate business customer group fusion groups based on the acquired business user information. The business user information can be acquired from a database storing business user information. Each piece of business user information in the business user information can include data information. The data information in each piece of data information included in the business user information can represent a user identifier, user name, or loan (loan) record information. The user identifier can represent the user's ID number. The user name can represent the user's name. The loan (loan) record information can include the user's loan amount (loan amount) and loan (loan) method (loan method). For example, the business user information can include: "Name: xx, ID number: xxxxxxxx, User loan (loan) record information: 50,000, cash loan." The business users represented by the business user information in the business user information can include users applying for value transfer tools (credit card customers), users applying for consumer value loan (cash loan) services (cash loan), users applying for vehicle value-related services (automotive value loan), or users of online service platforms (e-commerce customers).

[0027] In some optional implementations of some embodiments, the execution entity may generate various service customer group integration groups according to the acquired information of various service users through the following steps:

[0028] The first step is to classify the above business user information to obtain each business user customer group.

[0029] The second step is to generate business user profile information corresponding to each of the business user information based on the business user information. The business user profile information in each of the business user profile information corresponds to the business user information in each of the business user information. The business user profile information in each of the business user profile information can represent a profile of the business user information.

[0030] The third step is to divide the above-mentioned business user customer groups according to the above-mentioned business user portrait information, and obtain each sub-business user information group set corresponding to the above-mentioned business user customer groups.

[0031] The fourth step is to determine the business customer group fusion groups corresponding to the obtained sub-business user information groups based on the obtained sub-business user information groups. The business customer group fusion groups in the above-mentioned business customer group fusion groups can be obtained by combining the sub-business user information groups included in the obtained sub-business user information group sets. For example, the sub-business user information group sets may include: "The sub-business user information group set includes Group A1, Group A2, and Group A3; The sub-business user information group set includes Group B1, Group B2, and Group B3; The sub-business user information group set includes Group C1, Group C2, and Group C3; Then the business customer group fusion groups in the business customer group fusion groups may include Group A1, Group B1, and Group C2."

[0032] In some optional implementations of some embodiments, the execution entity may classify the above-mentioned business user information through the following steps to obtain various business user customer groups:

[0033] In the first step, enhancement processing is performed on the above-mentioned individual business user information to obtain the enhanced individual business user information as individual business user information.

[0034] The second step is to classify the aforementioned business user information to obtain various business user customer groups. A business user customer group within each of the aforementioned business user customer groups can represent various business user information items with the same borrowing and returning business method included in the aforementioned business user information. In practice, the execution entity can identify various business user information items with the same borrowing and returning business method included in the aforementioned business user information as business user customer groups to obtain the various business user customer groups.

[0035] In the process of adopting technical solutions to solve the above-mentioned technical problem 1, the following technical problem 2 is often accompanied: when the business user information samples are directly used to train various business warning models, the business user information samples for training the business warning models are not preprocessed. When the business user information samples contain a lot of redundant and erroneous data, the training cycle of the business warning model based on the samples containing a lot of redundant and erroneous data is long, resulting in a lot of wasted computing resources for training the business warning model. In response to these problems, the conventional solution is generally to only delete the duplicate sample user information in the business user information samples. However, the inventors took into account the shortcomings of only deleting the duplicate sample user information in the business user information samples, and combined with the advantages of the inventor's company in machine learning, we decided to adopt the following solution:

[0036] In some optional implementations of some embodiments, the execution entity may perform enhancement processing on the above-mentioned individual service user information through the following steps to obtain the enhanced individual service user information as individual service user information:

[0037] The first step is to convert each piece of data in the business user information that meets a preset conversion condition to obtain the converted business user information. The preset conversion condition may be that the data represents the borrowing and returning business method of the business user. In practice, the execution entity may convert each piece of data in the business user information that meets the preset conversion condition using a one-hot encoding method to obtain the converted business user information.

[0038] In the second step, for each piece of data in each business user information after the above conversion processing, perform the following steps:

[0039] The first sub-step involves determining the nearest neighbor node information corresponding to the data information based on a pre-trained business user structure tree. The business user structure tree may be a KD-Tree constructed from a business user information set. The data structure of the business user information in the business user information set is the same as the data structure of the business user information in each of the business user information sets. The business users represented by the business user information in the business user information set may be users applying for value transfer tools (credit card customers), users applying for consumer value lending and repayment services (cash lending and repayment customers), users applying for vehicle value-related services (automobile-related value lending and repayment customers), or users of online service platforms (e-commerce customers). Each piece of business user information in each piece of business user information may include data information, and each piece of data information may include a user's lending and repayment (loan) record. The nearest neighbor node information in each piece of nearest neighbor node information may represent the nearest neighbor nodes of the data information. The nearest neighbor node information in each piece of nearest neighbor node information may represent the nodes in the business user structure tree that have the smallest distance to the node corresponding to the data information. The nodes within each node in the business user structure tree may correspond to the data information in each piece of business user information. In practice, the execution entity may determine the nearest neighbor nodes corresponding to the data information through a pre-trained business user structure tree.

[0040] The second sub-step is to determine the quantity information corresponding to each of the aforementioned nearest neighbor node information, wherein the quantity information may represent the quantity of each of the aforementioned nearest neighbor node information.

[0041] In a third sub-step, in response to determining that the number of adjacent points is greater than a predetermined number of adjacent points, the data point corresponding to the data information is determined as a target data point. The target data point may represent a node in the pre-trained service user structure tree for the data information. The specific value of the predetermined number of adjacent points is not specifically limited.

[0042] In the fourth sub-step, clustering is performed on the transformed business user information based on the determined target data points to obtain clustered business user information. In practice, the execution entity may cluster the transformed business user information using a clustering algorithm to obtain clustered business user information. For example, the clustering algorithm may be DBSCAN.

[0043] The fifth sub-step is to determine the noise points in the clustered business user information as target noise points.

[0044] In the third step, each data information corresponding to each determined target noise point is deleted from the above-mentioned each service user information to update the above-mentioned each service user information and obtain the updated each service user information as each service user information.

[0045] The fourth step is to determine the importance of each piece of data included in each piece of business user information, thereby obtaining each piece of importance information. The importance information in each piece of importance information may represent the importance of the data. In practice, the execution entity may determine the importance of each piece of data included in the business user information using a TF-IDF algorithm.

[0046] In the fifth step, based on the respective importance information, the business user information is subjected to word-memory masking to obtain the business user information after word-memory masking. In practice, the execution entity may perform word-memory masking on the business user information using a word-memory masking method to obtain the business user information after word-memory masking.

[0047] In the sixth step, gram prediction is performed on the business user information after the gram masking process to obtain the business user information after the gram prediction process, thereby obtaining the business user information after data enhancement process as the business user information. In practice, the execution entity may perform gram prediction on the business user information after the gram masking process using the BERT prediction method to obtain the business user information after the gram prediction process.

[0048] The above technical solution, as an inventive point of an embodiment of the present disclosure, solves the technical problem: "The cycle of training the business warning model is long, and more computing resources are wasted in training the business warning model". The factors that lead to a long cycle of training the business warning model and a large amount of computing resources wasted in training the business warning model are often as follows: directly using business user information samples to train each business warning model, without pre-processing the business user information samples for training the business warning model. If the above factors are solved, the effect of reducing the computing resources wasted in training the business warning model can be achieved. In order to achieve this effect, when generating warning information, the present disclosure first performs data cleaning processing on the business user information to remove erroneous data, and then performs data enhancement processing on the business user information after data cleaning, so as to realize pre-processing of the business user information samples for training the business warning model, so as to reduce the redundant and erroneous data included in the business user information used for training the business warning model, thereby shortening the cycle of training the business warning model based on samples containing more redundant and erroneous data and reducing the computing resources wasted in training the business warning model.

[0049] In some optional implementations of some embodiments, the execution entity may generate business user profile information corresponding to each business user information according to each business user information through the following steps:

[0050] In the first step, for each piece of business user information above, perform the following steps:

[0051] The first sub-step is to perform word segmentation processing on the business user information to obtain individual word information. The individual word information may represent individual words included in the business user information. In practice, the execution entity may perform word segmentation processing on the business user information using the Jieba word segmentation method to obtain individual word information.

[0052] In the second sub-step, the obtained word information is subjected to redundancy elimination processing, and the obtained word information after redundancy elimination is used as the target word information. In practice, first, the execution subject can determine the TF-IDF value corresponding to each word information in the above-mentioned word information by the TF-IDF method to obtain each TF-IDF value. Then, the word information whose corresponding TF-IDF value in the obtained word information is less than the preset value is deleted to eliminate redundancy of the obtained word information. Here, the specific value of the above-mentioned preset value is not limited.

[0053] The third sub-step is to generate a vocabulary relationship table corresponding to each target word information based on the target word information. The vocabulary relationship table may represent a document-term matrix. In practice, the execution entity may use the TfidfVectorizer class to create a vocabulary relationship table corresponding to each target word information based on the target word information and the corresponding TF-IDF values.

[0054] In a fourth sub-step, the vocabulary relationship table and the preset number of topics are input into a pre-trained topic information generation model to obtain topic information corresponding to each target word information. The topic information in each topic information may include a topic name and a topic probability. The topic probability may represent the probability that the topic information is the target word information. The preset number of topics may represent the preset number of topics. For example, the preset number of topics may be five. For example, the subject information may be: "Topic Name: Automotive Value Lending and Returning Business; Topic Probability: 50%." The pre-trained topic information generation model may be a model that takes the vocabulary relationship table and the preset number of topics as input and outputs each topic information. For example, the pre-trained topic information generation model may be an LDA model. The training data for the pre-trained topic information generation model may be each target vocabulary relationship table and the number of topics and each topic information corresponding to each target vocabulary relationship table in each target vocabulary relationship table. The target vocabulary relationship table in each target vocabulary relationship table has the same data structure as the vocabulary relationship table. The training method for the pre-trained topic information generation model may be batch training.

[0055] The fifth sub-step is to generate business user profile information corresponding to the business user information based on the above-mentioned subject information and the above-mentioned target word information. In practice, the above-mentioned execution entity can generate business user profile information corresponding to the above-mentioned business user information based on the above-mentioned subject information and the above-mentioned target word information using a support vector machine (SVM).

[0056] In some optional implementations of some embodiments, the execution entity may divide the above-mentioned business user customer groups according to the above-mentioned business user portrait information through the following steps to obtain each sub-business user information group set corresponding to each of the above-mentioned business user customer groups:

[0057] The first step is to perform feature extraction on each of the business user profile information to obtain respective profile feature information. The profile feature information in each of the profile feature information may correspond to the business user profile information in each of the business user profile information. In practice, the execution entity may perform feature extraction on each of the business user profile information using a feature extraction algorithm to obtain respective profile feature information. For example, the feature extraction algorithm may be a principal component analysis (PCA) algorithm.

[0058] The second step is to perform standardization processing on each of the above-mentioned portrait feature information to obtain the standardized portrait feature information as each target portrait feature information. In practice, the execution entity can perform standardization processing on each of the above-mentioned portrait feature information using a Min-Max standardization method to obtain the standardized portrait feature information as each target portrait feature information.

[0059] Step 3: For each of the above business user groups, perform the following steps:

[0060] The first step is to perform the following steps for each target profile feature information corresponding to the above business user customer group:

[0061] In the first sub-step, each target portrait feature information corresponding to the above-mentioned business user customer group and different from the above-mentioned target portrait feature information is determined as a target portrait feature information set.

[0062] The second sub-step is to determine the similarity information between the target portrait feature information and each target portrait feature information in the determined target portrait feature information set, thereby obtaining each piece of similarity information. The similarity information in each piece of similarity information may represent the similarity between the target portrait feature information and the target portrait feature information in the target portrait feature information set. For example, the similarity information may be 50%. In practice, the execution entity may determine the similarity information between the target portrait feature information and each piece of target portrait feature information in the determined target portrait feature information set using a cosine similarity method.

[0063] In a third sub-step, based on the aforementioned similarity information, each piece of target portrait feature information in the determined target portrait feature information set that satisfies a preset selection condition and the aforementioned target portrait feature information are combined to form a target portrait feature information group. The preset selection condition may be that the similarity between the target portrait feature information in the determined target portrait feature information set and the aforementioned target portrait feature information is greater than a preset similarity value. The specific value of the preset similarity value is not limited herein.

[0064] The fourth sub-step is to determine each business user information corresponding to the determined target portrait feature information group as a sub-business user information group.

[0065] The second step is to determine the determined sub-service user information groups as a sub-service user information group set.

[0066] However, in practice, it is found that in the process of adopting technical solutions to solve the above technical problem 1, the following technical problems are often accompanied:

[0067] When training various business warning models directly using business user information samples as is commonly done, the following technical problems often arise: directly using business user information samples to train various business warning models without classifying and storing the business user information samples used to train the various business warning models, resulting in a relatively complex type of business user information samples used to train the business warning models, and the resulting low pertinence of the trained business warning models, as well as low accuracy of the business warning models that match the corresponding detection request information from the trained business warning models. To address these issues, conventional solutions are generally to classify and store only based on the type of business user information samples. The inventors, taking into account the shortcomings of classifying and storing only based on the type of business user information samples, and in combination with the advantages of the inventor's company in machine learning, decided to adopt the following solution:

[0068] In some optional implementations of some embodiments, the execution entity may determine each service customer group integration group corresponding to each obtained sub-service user information group according to the obtained sub-service user information group through the following steps:

[0069] The first step is to determine the fused portrait feature information corresponding to each sub-service user information group in the above-mentioned sub-service user information group sets to obtain each fused portrait feature information. The fused portrait feature information in each fused portrait feature information can represent the features obtained by performing feature splicing processing on the individual portrait feature information of the service user information group. In practice, the above-mentioned execution entity can perform feature splicing processing on the individual target portrait feature information corresponding to each sub-service user information group in the above-mentioned sub-service user information group sets to obtain the fused portrait feature information.

[0070] In the second step, for each sub-service user information group set in the above sub-service user information group sets, perform the following steps:

[0071] In the first sub-step, based on the aforementioned fusion profile feature information, each sub-service user information group in the sub-service user information group set that meets the preset fusion condition is determined as a fused sub-service user information group, thereby obtaining each fused sub-service user information group. The preset fusion condition may be that the similarity between the fusion profile feature information corresponding to two sub-service user information groups is greater than a preset similarity. The specific value of the similarity is not limited herein. In practice, the execution entity may determine the sub-service user information groups in the sub-service user information group set that meet the preset fusion condition using a cosine similarity method.

[0072] The second sub-step is to perform the following steps for each of the obtained fusion sub-service user information groups:

[0073] The first execution step is to determine the above-mentioned sub-service user information group sets and the sub-service user information group sets that are different from the above-mentioned sub-service user information group sets as different sub-service user information group sets.

[0074] The second execution step is to determine, within each of the aforementioned distinct sub-service user information group sets, each distinct sub-service user information group that meets a preset selection condition as a target distinct sub-service user information group set, thereby obtaining each target distinct sub-service user information group set. The preset selection condition may be that the similarity between the distinct sub-service user information group and the converged sub-service user information group is greater than a preset similarity. The specific value of the preset similarity is not limited herein.

[0075] The third execution step is to randomly select a target-different sub-service user information group set from the above-mentioned target-different sub-service user information group sets.

[0076] The fourth execution step is to delete the randomly selected target different sub-service user information group set from the above target different sub-service user information group sets to obtain updated target different sub-service user information group sets.

[0077] a fifth execution step of determining each target different sub-service user information group in the randomly selected target different sub-service user information group set and the fused sub-service user information group as a first service user information group to obtain each first service user information group;

[0078] The sixth execution step is to randomly select a preset number of target different sub-service user information from each target different sub-service user information group set in each updated target different sub-service user information group set to obtain randomly selected target different sub-service user information groups.

[0079] The seventh execution step is to determine each randomly selected target different sub-service user information group and each first service user information in each first service user information group as a second service user information group to obtain each second service user information group.

[0080] In an eighth execution step, redundancy is eliminated on each of the obtained second service user information groups, and the obtained redundancy-eliminated second service user information groups are used as the fusion groups for each service customer group. In practice, the execution entity may eliminate redundancy on each of the obtained second service user information groups using a distributed deduplication algorithm, and the obtained redundancy-eliminated second service user information groups are used as the fusion groups for each service customer group.

[0081] The ninth execution step is to store the obtained fusion groups of various business customer groups in a related database.

[0082] The above technical solution, as an inventive feature of an embodiment of the present disclosure, solves the technical problem of "low specificity of each trained business warning model and low accuracy of the business warning model that matches the corresponding detection request information from each trained business warning model." The factors that lead to the low specificity of each trained business warning model and the low accuracy of the business warning model that matches the corresponding detection request information from each trained business warning model are often as follows: directly using business user information samples to train each business warning model without dividing the business user information samples used to train each business warning model. If the above factors are resolved, the specificity of each trained business warning model and the accuracy of the business warning model that matches the corresponding detection request information from each trained business warning model can be improved. To achieve this effect, the present disclosure first classifies the business user information according to the profile features when generating warning information. Then, the business user information with high similarity is combined, so that the business user information samples can be divided into fine-grained groups, further improving the specificity of each trained business warning model and the accuracy of the business warning model that matches the corresponding detection request information from each trained business warning model.

[0083] Step 102 , in response to the detection request information of the business warning information, generates matching degree information between the detection request information and each business customer group fusion group according to the detection request information and each business customer group fusion group to obtain each matching degree information.

[0084] In some embodiments, in response to detecting a detection request message for business warning information, the execution entity may generate matching information between the detection request message and each of the business customer group fusion groups based on the detection request message and the business customer group fusion groups, thereby obtaining matching information. The matching information in each matching information may represent the matching degree between the detection request message and the business customer group fusion group. The detection request message may be a request for detecting warning information for a target user. The detection request message may include business user information of the target user. The warning information may represent the risk level of the target user. For example, the warning information may indicate that the risk level of the business user is 50%.

[0085] In some optional implementations of some embodiments, for each of the aforementioned business customer groups, the execution entity may generate a model based on the business customer group, the business user profile information corresponding to the detection request information, and the preset fitness information, to obtain matching information between the business customer group and the detection request information. The preset fitness information generation model may be a model that takes the fused profile feature information corresponding to the business customer group and the business user profile information corresponding to the detection request information as input, and outputs the matching information between the business customer group and the detection request information. For example, the preset fitness information generation model may be a collaborative filtering model. It should be noted that the training data for training the preset fitness information generation model may be individual sets of training data. Each set of training data may include the business user profile information corresponding to the detection request information, the fused profile feature information corresponding to the business customer group, and matching information. The included matching information may represent the matching information between the business customer group and the business user profile information. The number of training data sets is not limited herein. The method for training the preset fitness information generation model may be a batch training method. In practice, the execution entity may first determine the business user profile information of the target user represented by the detection request information. Then, for each of the aforementioned business customer groups, the fused profile feature information corresponding to the business customer group and the business user profile information corresponding to the detection request information are input into the preset adaptability information generation model to obtain the matching degree for each business customer group.

[0086] Step 103 , based on the matching degree information, the sub-business warning model that meets the preset matching degree condition among the sub-business warning models trained by the business customer group fusion group is determined as the target sub-business warning model corresponding to the detection request information, so as to perform adaptation processing on the sub-business warning model.

[0087] In some embodiments, based on the aforementioned matching degree information, the execution entity may determine, among the various sub-business warning models trained by the various business customer group fusion groups, a sub-business warning model that meets a preset matching degree condition as the target sub-business warning model corresponding to the detection request information, thereby adapting the sub-business warning model. The sub-business warning model in each of the pre-trained sub-business warning models corresponds to a business customer group fusion group in each of the aforementioned business customer group fusion groups. The sub-business warning model in each of the aforementioned sub-business warning models is trained by the corresponding business customer group fusion group. The preset matching degree condition may be that the matching degree between the business customer group fusion group corresponding to the sub-business warning model and the detection request information is the highest matching degree among the aforementioned matching degree information. The sub-business warning model may be a model that takes detection request information as input and outputs warning information. For example, the sub-business warning model may be a logistic regression model. The training data for the sub-business warning model in each of the aforementioned sub-business warning models may be the detection request information corresponding to the business customer group fusion group and the warning information corresponding to each detection request information. Each of the above sub-business early warning models may be trained by a batch training method.

[0088] Step 104: input the detection request information into the target sub-business warning model to obtain warning information corresponding to the detection request information.

[0089] In some embodiments, the execution entity may input the detection request information into the target sub-business warning model to obtain warning information corresponding to the detection request information.

[0090] Step 105 : in response to detecting that the warning information meets the preset warning condition, obtaining request processing detail information of each sub-business warning model in each sub-business warning model, and obtaining each request processing detail information.

[0091] In some embodiments, in response to detecting that the above-mentioned warning information meets the preset warning condition, the above-mentioned execution entity can obtain the request processing detail information of each sub-business warning model in the above-mentioned sub-business warning model to obtain the request processing detail information of each sub-business warning model. Among them, the above-mentioned preset warning condition can represent that the order of the above-mentioned warning information is a preset order. The order of the above-mentioned preset information can represent the order of the above-mentioned warning information in the various warning information generated by each sub-business warning model. For example, the order of the warning information can be the tenth. The request processing detail information in the above-mentioned each request processing detail information can include request processing quantity information and request processing duration information. The above-mentioned request processing detail information can represent the details of the detection request information processed by the request sub-business warning model. The above-mentioned request processing quantity information can represent the number of detection request information processed by the request sub-business warning model. The above-mentioned request processing duration information can represent the time consumed by the sub-business warning model to process the detection request information. In practice, the above-mentioned execution entity can obtain the various request processing detail information from a database storing the various request processing detail information.

[0092] Step 106 : Determine whether there is a sub-business early warning model that meets the preset model migration condition among the sub-business early warning models according to the detailed information of each request processing.

[0093] In some embodiments, the execution entity may determine whether there is a sub-business warning model that meets the preset model migration conditions in each of the sub-business warning models based on the detailed information of each request processing. The preset model migration condition may be that the request processing quantity information corresponding to the sub-business warning model is greater than the preset request processing quantity and the request processing duration corresponding to the sub-business warning model is greater than the preset request processing duration. Here, there is no specific limitation on the value of the preset request processing quantity. There is no specific limitation on the value of the preset request processing duration.

[0094] Step 107 : In response to detecting that there is a sub-business warning model that meets the preset model migration condition among the sub-business warning models, migration processing is performed on the sub-business warning model that meets the preset model migration condition.

[0095] In some embodiments, in response to detecting that a sub-business warning model that meets the preset model migration conditions exists among the above-mentioned sub-business warning models, the above-mentioned execution entity may migrate the sub-business warning model that meets the above-mentioned preset model migration conditions. In practice, the above-mentioned execution entity may send the model file corresponding to the sub-business warning model that meets the above-mentioned preset model migration conditions to the target server to store the model file on the above-mentioned target server. The above-mentioned target server may be a pre-set server for receiving the migrated sub-business warning model.

[0096] Optionally, after step 107, the execution entity may first determine the warning level information corresponding to the warning information based on the warning information. In practice, the execution entity may determine the warning level information corresponding to the warning information based on the preset level information in the preset level information set. The preset level information in the preset level information set may represent the correspondence between the warning information and the warning level information.

[0097] Afterwards, the warning method corresponding to the warning level information can be determined based on the warning level information. In practice, the execution entity can determine the preset warning method information corresponding to the preset level information from the preset warning method information set. The preset warning method information in the preset warning method information set can represent the correspondence between the warning level information and the preset warning method. For example, the preset warning method information can be a warning level 1 warning, and the preset warning method can be a telephone warning.

[0098] Secondly, the above-mentioned detection request information can be processed with early warning according to the above-mentioned early warning method.

[0099] Afterwards, the warning information, the warning level information, and the warning method corresponding to the detection request information may be determined as warning record information corresponding to the detection request information.

[0100] Then, the warning record information can be serialized to obtain the serialized warning record information. In practice, the execution entity can serialize the warning record information using a serialization algorithm to obtain the serialized warning record information. For example, the serialization algorithm can be a JSON serialization algorithm.

[0101] Finally, the serialized warning record information is stored in the relevant database.

[0102] The above technical solution, as an inventive point of an embodiment of the present disclosure, solves the technical problem: "Directly storing the warning record information in an associated database to record the warning information without pre-processing the warning record information. When a large amount of stored warning record information is stored, the stored warning record information occupies a large amount of storage space." The factors that cause the stored warning record information to occupy a large amount of storage space are often as follows: directly storing the warning record information in an associated database without pre-processing the warning record information. If the above factors are solved, the effect of reducing the storage space occupied by the stored warning record information can be achieved. In order to achieve this effect, when adapting the business model, the present disclosure first serializes the warning record information to be stored, and then stores the serialized warning record information in an associated database. This reduces the storage space occupied by the stored warning record information.

[0103] The above-described embodiments of the present disclosure have the following beneficial effects: A model migration method according to some embodiments of the present disclosure can reduce server load, shorten server response time, and reduce server downtime. The high server load, long server response time, and frequent server downtime are caused by integrating the trained business warning models into a single server. When a large number of detection request messages are input into each trained business warning model, the server load is high, resulting in long server response time and frequent server downtime. Based on this, the model migration method according to some embodiments of the present disclosure first generates business customer group fusion groups based on the acquired business user information. This generates sets of sub-business user information groups, which can be further used to train each business warning model. Then, in response to detection request information for detecting business warning information, matching information is generated between the detection request information and each of the business customer group fusion groups, based on the detection request information and the business customer group fusion groups, to obtain matching information. This allows the matching degree between the detection request information and each of the business customer group fusion groups to be determined. Next, based on the matching information, the pre-trained sub-service warning models that meet preset matching conditions are identified as target sub-service warning models corresponding to the detection request information, thereby adapting the sub-service warning models. The pre-trained sub-service warning models correspond to the service customer group integration groups within the service customer group integration groups, and the sub-service warning models within the pre-trained sub-service warning models are trained using the corresponding service customer group integration groups. Thus, an adapted sub-service warning model is obtained. The detection request information is then input into the target sub-service warning model to obtain warning information corresponding to the detection request information. Thus, warning information for the target user is obtained. Next, in response to detecting that the warning information meets preset alarm conditions, request processing details are obtained for each of the pre-trained sub-service warning models, obtaining respective request processing details. The request processing details within the pre-trained sub-service warning models include information on the number of requests processed and information on the duration of the request processing. Thus, request processing details for each sub-service warning model are obtained. Then, based on the detailed information about each request processing, it is determined whether any of the aforementioned sub-business warning models meets the preset model migration criteria. This allows the determination of whether any business warning models are to be migrated. Finally, in response to detecting that any of the aforementioned sub-business warning models meets the preset model migration criteria, the sub-business warning models meeting the preset model migration criteria are migrated. Thus, the sub-business warning models meeting the migration criteria can be migrated to reduce server load.Also, when there is a business warning model that meets the above-mentioned preset migration conditions in the server, the business warning model that meets the above-mentioned preset migration conditions will be migrated, so that the model can be adaptively migrated in a high-concurrency scenario to reduce the server load, and a type of detection request information can be processed by a separate target server, thereby improving the response speed and reducing the number of server downtimes.

[0104] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a model migration method. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0105] like Figure 2As shown, the model migration device 200 of some embodiments includes: a first generating unit 201 , a second generating unit 202 , a first determining unit 203 , an input unit 204 , an acquiring unit 205 , a second determining unit 206 and a migration unit 207 . Among them, the first generation unit 201 is configured to generate each business customer group fusion group based on the obtained business user information; the second generation unit 202 is configured to respond to the detection request information of the detected business warning information, and generate the matching degree information between the above-mentioned detection request information and each business customer group fusion group in the above-mentioned business customer group fusion group according to the above-mentioned detection request information and the above-mentioned business customer group fusion group, to obtain each matching degree information; the first determination unit 203 is configured to determine the sub-business warning model that meets the preset matching degree condition in the each sub-business warning model trained by the above-mentioned each business customer group fusion group as the target sub-business warning model corresponding to the above-mentioned detection request information according to the above-mentioned each matching degree information, so as to adapt the sub-business warning model, wherein the sub-business warning model in the above-mentioned each pre-trained sub-business warning model corresponds to the business customer group fusion group in the above-mentioned each business customer group fusion group, and the sub-business warning model in the above-mentioned each sub-business warning model is determined by corresponding obtained by training the business customer group fusion group; the input unit 204 is configured to input the above-mentioned detection request information into the above-mentioned target sub-business warning model to obtain warning information corresponding to the above-mentioned detection request information; the acquisition unit 205 is configured to obtain the request processing detail information of each sub-business warning model in the above-mentioned sub-business warning models in response to detecting that the above-mentioned warning information meets the preset alarm condition, and obtain each request processing detail information, wherein the request processing detail information in the above-mentioned each request processing detail information includes request processing quantity information and request processing duration information; the second determination unit 206 is configured to determine whether there is a sub-business warning model that meets the preset model migration condition in the above-mentioned each sub-business warning model according to the above-mentioned each request processing detail information; the migration unit 207 is configured to perform migration processing on the sub-business warning model that meets the above-mentioned preset model migration condition in response to detecting that there is a sub-business warning model that meets the preset model migration condition in the above-mentioned each sub-business warning model.

[0106] It is understood that the units described in the device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units included therein, and will not be repeated here.

[0107] Reference below Figure 3 , which shows a structural diagram of an electronic device 300 (eg, a computing device) suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0108] like Figure 3 As shown, the electronic device 300 may include a processing device 301 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0109] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0110] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0111] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0112] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0113] The above-mentioned computer-readable medium may be contained in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: generates each business customer group fusion group according to the obtained business user information; in response to the detection request information of the business warning information detected, generates the matching degree information between the above-mentioned detection request information and each business customer group fusion group in the above-mentioned business customer group fusion group according to the above-mentioned detection request information and the above-mentioned business customer group fusion group, and obtains each matching degree information; according to the above-mentioned matching degree information, determines the sub-business warning model that meets the preset matching degree condition in each sub-business warning model trained by the above-mentioned business customer group fusion group as the target sub-business warning model corresponding to the above-mentioned detection request information, so as to adapt the sub-business warning model, wherein the sub-business warning model in the above-mentioned pre-trained sub-business warning model corresponds to the business customer group fusion in the above-mentioned business customer group fusion group. The sub-business warning models in the above-mentioned sub-business warning models are obtained through training of the corresponding business customer group fusion group; the above-mentioned detection request information is input into the above-mentioned target sub-business warning model to obtain warning information corresponding to the above-mentioned detection request information; in response to detecting that the above-mentioned warning information meets the preset alarm conditions, the request processing details information of each sub-business warning model in the above-mentioned sub-business warning models is obtained to obtain each request processing details information, wherein the request processing details information in the above-mentioned each request processing details information includes request processing quantity information and request processing duration information; based on the above-mentioned each request processing details information, it is determined whether there is a sub-business warning model that meets the preset model migration conditions in the above-mentioned sub-business warning models; in response to detecting that there is a sub-business warning model that meets the preset model migration conditions in the above-mentioned sub-business warning models, the sub-business warning model that meets the above-mentioned preset model migration conditions is migrated.

[0114] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0116] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, they may be described as: a first generation unit, a second generation unit, a first determination unit, an input unit, an acquisition unit, a second determination unit, and a migration unit. The names of these units do not, in certain cases, constitute limitations on the units themselves. For example, the migration unit may also be described as "a unit that performs migration processing on the sub-business warning models that meet the preset model migration conditions in response to detecting that there are sub-business warning models that meet the preset model migration conditions in the above-mentioned sub-business warning models."

[0117] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0118] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A model migration method, comprising: Generate integrated groups of various business customer groups based on the acquired business user information; In response to detecting the detection request information of the service warning information, generating, based on the detection request information and the respective service customer group fusion groups, matching degree information between the detection request information and each of the service customer group fusion groups to obtain respective matching degree information; According to the respective matching degree information, a sub-business warning model that meets a preset matching degree condition among the respective sub-business warning models trained by the respective business customer group fusion groups is determined as a target sub-business warning model corresponding to the detection request information, so as to perform adaptation processing on the sub-business warning model, wherein the sub-business warning model in each pre-trained sub-business warning model corresponds to a business customer group fusion group in each business customer group fusion group, and the sub-business warning model in each sub-business warning model is obtained by training the corresponding business customer group fusion group; Inputting the detection request information into the target sub-business warning model to obtain warning information corresponding to the detection request information; In response to detecting that the warning information meets a preset warning condition, obtaining request processing detail information of each sub-business warning model in each sub-business warning model to obtain each request processing detail information, wherein the request processing detail information in each request processing detail information includes request processing quantity information and request processing duration information; Determining, based on the detailed information of each request processing, whether there is a sub-business warning model among the sub-business warning models that meets the preset model migration conditions; In response to detecting that there is a sub-business warning model that meets the preset model migration condition among the sub-business warning models, migration processing is performed on the sub-business warning model that meets the preset model migration condition.

2. The method according to claim 1, wherein The method further comprises: Determining, based on the warning information, warning level information corresponding to the warning information; Determining a warning method corresponding to the warning level information according to the warning level information; Performing early warning processing on the detection request information according to the early warning method; Determining the warning information, the warning level information, and the warning method corresponding to the detection request information as warning record information corresponding to the detection request information; Serializing the warning record information to obtain serialized warning record information; The serialized warning record information is stored in the associated database.

3. The method according to claim 1, wherein The migrating of the sub-business warning model that meets the preset model migration conditions includes: The model file corresponding to the sub-business early warning model that meets the preset model migration condition is sent to the target server to store the model file in the target server.

4. The method according to claim 1, wherein Generating each business customer group integration group based on the acquired business user information includes: Classify the information of each business user to obtain each business user customer group; Generating, based on the business user information, business user portrait information corresponding to the business user information, wherein the business user portrait information in the business user portrait information corresponds to the business user information in the business user information; According to the business user portrait information, the business user customer groups are divided and processed to obtain sets of sub-business user information groups corresponding to the business user customer groups; According to the obtained sub-service user information groups, each service customer group integration group corresponding to each obtained sub-service user information group is determined.

5. The method according to claim 4, wherein The classification processing of the information of each business user to obtain each business user customer group includes: Performing enhancement processing on the individual service user information to obtain the enhanced individual service user information as the individual service user information; The information of each business user is classified and processed to obtain each business user customer group.

6. The method according to claim 4, wherein: Generating business user portrait information corresponding to each business user information according to each business user information includes: For each piece of business user information, perform the following steps: Perform word segmentation processing on the business user information to obtain information about each word; Performing redundancy elimination processing on each word information obtained, and obtaining each word information after redundancy elimination as each target word information; Generating a vocabulary relationship table corresponding to each target word information according to each target word information; Inputting the vocabulary relationship table and the preset topic quantity information into a pre-trained topic information generation model to obtain each topic information corresponding to each target word information; Business user portrait information corresponding to the business user information is generated based on the various subject information and the various target word information.

7. The method according to claim 4, wherein: The process of dividing each business user customer group according to the business user portrait information to obtain each sub-business user information group set corresponding to each business user customer group includes: Performing feature extraction processing on each of the business user portrait information to obtain each piece of portrait feature information, wherein the portrait feature information in each piece of portrait feature information corresponds to the business user portrait information in each piece of the business user portrait information; Performing standardization on each of the portrait feature information to obtain the standardized portrait feature information as each target portrait feature information; For each of the business user groups, perform the following steps: For each target profile feature information corresponding to the business user customer group, perform the following steps: Determine each target portrait feature information that is different from the target portrait feature information in each target portrait feature information corresponding to the business user customer group as a target portrait feature information set; Determine similarity information between the target portrait feature information and each target portrait feature information in the determined target portrait feature information set to obtain respective similarity information; According to the respective similarity information, each target portrait feature information satisfying a preset selection condition in the determined target portrait feature information set and the target portrait feature information are determined as a target portrait feature information group; Determine each business user information corresponding to the determined target portrait feature information group as a sub-business user information group; The determined sub-service user information groups are determined as a sub-service user information group set.

8. A model migration device, comprising: The first generating unit is configured to generate each service customer group fusion group according to the acquired information of each service user; The second generating unit is configured to, in response to the detection request information of the business warning information being detected, generate, based on the detection request information and the respective business customer group fusion groups, matching degree information between the detection request information and each of the respective business customer group fusion groups, to obtain respective matching degree information; a first determining unit configured to determine, based on the respective matching degree information, a sub-business warning model that satisfies a preset matching degree condition among the respective sub-business warning models trained by the respective business customer group fusion groups as a target sub-business warning model corresponding to the detection request information, so as to perform adaptation processing on the sub-business warning model, wherein the sub-business warning model in each pre-trained sub-business warning model corresponds to a business customer group fusion group in each business customer group fusion group, and the sub-business warning model in each sub-business warning model is obtained by training the corresponding business customer group fusion group; an input unit configured to input the detection request information into the target sub-service warning model to obtain warning information corresponding to the detection request information; an acquiring unit configured to, in response to detecting that the warning information satisfies a preset warning condition, acquire request processing detail information of each sub-business warning model in each sub-business warning model, thereby obtaining each request processing detail information, wherein the request processing detail information in each request processing detail information includes request processing quantity information and request processing duration information; A second determining unit is configured to determine whether there is a sub-business early warning model that meets a preset model migration condition among the sub-business early warning models according to the detailed information of each request processing; The migration unit is configured to, in response to detecting that there is a sub-business warning model that meets the preset model migration condition among the sub-business warning models, perform migration processing on the sub-business warning model that meets the preset model migration condition.

9. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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