Information sending method, device and equipment based on multi-level regional terminal and medium

Abstract

Embodiments of the present disclosure disclose a multi-level regional terminal-based information sending method, device, equipment and medium. A specific implementation of the method comprises: in response to receiving a product value distribution request, obtaining a user information set, a product use level information set, a product generated value information set, a user team information set and a product use anomaly detection information set from a storage module; using a calculation module, determining the product value information set corresponding to the user information set; using a sending module, sending the product value encryption information set to the upper-level regional terminal; in response to determining that the audit information is correct, for each user information, performing the sending step: generating value packaging information; using the sending module, sending the value packaging information to the user terminal. The implementation can be based on the interaction between multi-level regional terminals, and can accurately and efficiently encrypt and send product value distribution-related information to the corresponding user.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular, to an information sending method and device based on multi-level regional terminals, electronic equipment and computer readable medium. BACKGROUND

[0002] At present, with the continuous popularization of various value products, value products are increasingly applied to people's daily life. For the sending of product value information corresponding to value products, the commonly used way is to determine the product value information set corresponding to each user in the region by relevant human personnel using a pre-set computing script, so as to directly send the product value information set to the terminal corresponding to each user.

[0003] However, when the above method is used, the following technical problems often exist:

[0004] When there are a large number of users in the region, the computing script has a large computing duration, and the computing script cannot effectively guarantee the accuracy of the product value information and often lacks relevant steps for auditing through terminal interaction between multiple levels.

[0005] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present inventive concept, and therefore, it can include information that does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0006] The summary section of the present disclosure is used to introduce the concepts in a brief form, which will be described in detail in the specific embodiments section. The summary section of the present disclosure is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of the present disclosure propose an information sending method and device based on multi-level regional terminals, electronic equipment and computer readable medium, to solve one or more of the technical problems mentioned in the background section.

[0008] In a first aspect, some embodiments of this disclosure provide an information transmission method based on multi-level regional terminals, comprising: in response to receiving a product value distribution request corresponding to a target region, obtaining a user information set corresponding to the target region from a storage module, wherein the target region has a corresponding regional terminal, and the regional terminal includes: a calculation module, a storage module, and a transmission module; obtaining a product usage level information set, a product generated value information set, a user team information set, and a product usage anomaly detection information set corresponding to the user information set from the storage module; using the calculation module, determining the product value information set corresponding to the user information set based on the product usage level information set, the product generated value information set, the user team information set, and the product usage anomaly detection information set; and using the transmission module... The module sends the encrypted product value information set corresponding to the aforementioned product value information set to the terminal of the next-level region corresponding to the aforementioned target region. The next-level region terminal includes backup data for each terminal corresponding to each lower-level region terminal. In response to confirming that the verification information sent by the next-level region terminal is correct, for each user information in the aforementioned user information set, the following sending steps are performed: The product usage level information, product value generation information, user team information, the aforementioned user information, and product value distribution rule information corresponding to the aforementioned user information are packaged to generate value package information. Using the aforementioned sending module, the value package information is sent to the user terminal using a sending method determined by the user corresponding to the aforementioned user information. The sending method is one of the following: encrypted SMS link method, encrypted email method.

[0009] Secondly, some embodiments of this disclosure provide an information transmission device based on a multi-level regional terminal, comprising: a first acquisition unit configured to, in response to receiving a product value distribution request corresponding to a target region, acquire a user information set corresponding to the target region from a storage module, wherein the target region has a corresponding regional terminal, and the regional terminal includes: a calculation module, a storage module, and a transmission module; a second acquisition unit configured to acquire from the storage module a product usage level information set, a product generated value information set, a user team information set, and a product usage anomaly detection information set corresponding to the user information set; and a determination unit configured to, using the calculation module, determine the product value information set corresponding to the user information set based on the product usage level information set, the product generated value information set, the user team information set, and the product usage anomaly detection information set. The sending unit is configured to use the sending module to send the encrypted product value information set corresponding to the product value information set to the terminal of the next-level region corresponding to the target region, wherein the next-level region terminal includes the terminal backup data corresponding to each of the lower-level region terminals; the execution unit is configured to, in response to determining that the verification information sent by the next-level region terminal is correct, perform the following sending steps for each piece of information in the user information set: package the product usage level information, product value generation information, user team information, the user information and product value distribution rule information corresponding to the user information to generate value package information; and use the sending module to send the value package information to the user terminal in a sending method determined by the user corresponding to the user information, wherein the sending method is one of the following: encrypted SMS link method, encrypted email method.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, such that when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any implementation of the first aspect.

[0011] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0012] The above embodiments of this disclosure have the following beneficial effects: Through the information sending method based on multi-level regional terminals in some embodiments of this disclosure, based on the interaction between multi-level regional terminals, product value distribution information can be accurately and efficiently encrypted and sent to the corresponding users. Specifically, the reason why the relevant product value distribution information is not accurate and efficient is that: when there are a large number of users in a region, the calculation script takes a long time to compute, and the calculation script cannot effectively guarantee the accuracy of product value information and the prevention of information leakage, and often lacks the relevant steps for review through multi-level terminal interaction. Based on this, the information sending method based on multi-level regional terminals in some embodiments of this disclosure firstly, in response to receiving a product value distribution request corresponding to a target region, retrieves the user information set corresponding to the target region from the storage module. The target region has a corresponding regional terminal, which includes a calculation module, a storage module, and a sending module. Here, through the calculation module, storage module, and sending module included in the regional terminal, the various functions of the terminal can be distinguished, so that the coordination between the various modules can ensure that the regional terminal can efficiently and accurately generate subsequent product value information. Then, the product usage level information set, product value generation information set, user team information set, and product usage anomaly detection information set corresponding to the aforementioned user information set are retrieved from the storage module to facilitate accurate generation of product value information. Next, using the aforementioned calculation module, the product value information set corresponding to the aforementioned user information set can be accurately determined based on the aforementioned product usage level information set, product value generation information set, user team information set, and product usage anomaly detection information set. Then, using the aforementioned sending module, the encrypted product value information set corresponding to the aforementioned product value information set is sent to the upper-level regional terminal corresponding to the aforementioned target region. The upper-level regional terminal includes the terminal backup data corresponding to each lower-level regional terminal. Here, by setting up multi-level regional terminals, the generation and review of product value information can be separated, avoiding the waste of excessive computing resources and the possibility of inaccurate and unobjective review of information that would otherwise occur on the same terminal. In addition, by setting up multi-level regional terminals, multi-level backup of terminal data can be ensured, which can reduce the storage pressure on lower-level regional terminals and ensure the information integrity of higher-level regional terminals. This enables more accurate verification of product value information and generation of product usage anomaly detection information, and maintains the target product for each user.Finally, in response to the confirmation that the verification information sent by the aforementioned higher-level regional terminal is correct, the following sending steps are executed for each user information in the aforementioned user information set: First, the product usage level information, product value generation information, user team information, the aforementioned user information, and product value distribution rule information corresponding to the aforementioned user information are packaged to generate value package information. Here, after the higher-level regional terminal confirms the verification is correct, packaging is used to facilitate subsequent delivery to users, allowing them to see complete information related to product value distribution. Second, using the aforementioned sending module, the value package information is sent to the user terminal using the sending method determined by the user corresponding to the aforementioned user information. The sending method is one of the following: encrypted SMS link or encrypted email. Here, based on the user's chosen sending method, encryption is used during the sending process to ensure the information security of the value package information and prevent information leakage. In summary, through the deployment of multi-level regional terminals, each process of product value information can be executed in multiple modules across multiple terminals, ensuring the efficiency, accuracy, and security of product value information generation. Attached Figure Description

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

[0014] Figure 1 This is a flowchart of some embodiments of the information transmission method based on multi-level regional terminals according to the present disclosure;

[0015] Figure 2 This is a schematic diagram of the structure of some embodiments of the information transmission device based on a multi-level regional terminal according to the present disclosure;

[0016] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

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

[0020] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

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

[0022] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] refer to Figure 1 The diagram illustrates a flow 100 of some embodiments of the information transmission method based on a multi-level regional terminal according to the present disclosure. This information transmission method based on a multi-level regional terminal includes the following steps:

[0024] Step 101: In response to receiving the product value distribution request corresponding to the target area, retrieve the user information set corresponding to the target area from the storage module.

[0025] In some embodiments, in response to receiving a product value distribution request corresponding to a target area, the executing entity of the information sending method based on multi-level regional terminals (e.g., a control system corresponding to the multi-level regional terminals) can obtain the user information set corresponding to the target area from the storage module. The target area can be an area where product value distribution processing is to be performed. Product value distribution can be an operation of distributing product value to users. In practice, for credit scoring scenarios, product value distribution can be paying a referral fee to users who recommend the product. For example, if user A recommends a target product to user B, and user B invests in the target product, the owner of the target product will pay user A a referral fee. The product value distribution request can be a request to determine the product value distribution information corresponding to each user within the target area. The storage module can be a module for storing various product-related data. The user information in the user information set can be the user identifier corresponding to the user using the target product within the target area. The target area has a corresponding regional terminal, which includes a calculation module, a storage module, and a sending module. The calculation module can support various value calculation-related modules. The calculation module includes a hardware calculation module and a software calculation module. Hardware computing modules typically consist of a processor, memory, storage, power management, and a circuit board, requiring an operating system such as Linux, WinCE, or QNX to form a minimal computer system. For example, the Intel NUC Element computing module is a core board designed by the target manufacturer based on the x86 architecture, available in two series: Element U and Element H. Element U uses a U-series CPU, integrates onboard memory, and connects to the I / O board using Intel's newly defined 300-pin connector; while Element H uses an H-series standard-voltage CPU, offering rich expansion capabilities, supporting 16X PCIe, and facilitating the expansion of high-performance discrete graphics cards. Computing modules usually consist of one or more classes, each responsible for implementing a specific computational function. For example, a Java computing module can encapsulate a series of computational operations, accepting input data, performing data validation and processing, executing specific computational operations (such as addition, subtraction, multiplication, division, logical operations, etc.), and finally returning the calculation result. This modular design makes software development more flexible and efficient. Sending modules can be modules that send various types of data.

[0026] Step 102: Obtain the product usage level information set, product generation value information set, user team information set, and product usage anomaly detection information set corresponding to the above user information set from the above storage module.

[0027] In some embodiments, the aforementioned executing entity can obtain from the aforementioned storage module the product usage level information set, product value generation information set, user team information set, and product usage anomaly detection information set corresponding to the aforementioned user information set. The product usage level information can represent the user's value in the target product, or it can represent the duration of the user's use of the target product. That is, the higher the corresponding product usage level information, the more value the corresponding user can bring to the target product. Each user information set contains corresponding product usage level information, product value generation information, user team information, and product usage anomaly detection information. The product value generation information can be the direct value value brought or generated by the user in the target product. The user team information can be the team information of the team to which the user belongs. For example, the user team information can be a team identifier. The product usage anomaly detection information can be risk information that may exist when the user uses the target product. The product usage anomaly detection information can be in numerical form. The higher the value of the product usage anomaly detection information, the higher the degree of risk represented by the user using the target product.

[0028] Step 103: Using the above calculation module, determine the product value information set corresponding to the above user information set based on the above product usage level information set, the above product generation value information set, the above user team information set, and the above product usage anomaly detection information set.

[0029] In some embodiments, the executing entity may utilize the aforementioned calculation module to determine the product value information set corresponding to the aforementioned user information set, based on the aforementioned product usage level information set, the aforementioned product generation value information set, the aforementioned user team information set, and the aforementioned product usage anomaly detection information set. The product value information may be the numerical value of the product's distribution.

[0030] In some optional implementations of certain embodiments, the execution entity may utilize the aforementioned calculation module to determine the product value information set corresponding to the aforementioned user information set based on the aforementioned product usage level information set, the aforementioned product generation value information set, the aforementioned user team information set, and the aforementioned product usage anomaly detection information set, including the following steps:

[0031] For each piece of user information in the aforementioned user information set, the following first generation step is performed using the aforementioned calculation module:

[0032] Sub-step 1: Based on the aforementioned product usage level information, determine the product value distribution rule information corresponding to the aforementioned user information. This product value distribution rule information can be a rule identifier used to determine the distribution rules for product value information. In practice, different product usage level information is used to set corresponding product value distribution rules. For example, for product usage level information A, the corresponding product value distribution rule is rule A. For product usage level information B, the corresponding product value distribution rule is rule B.

[0033] Sub-step 2: Based on the aforementioned product value distribution rules and product generation value information, determine the basic product value information corresponding to the aforementioned user information. The basic product value information may be the product reward value issued by the target product to the user for their product work.

[0034] As an example, firstly, the aforementioned implementing entity can determine the value-sharing ratio corresponding to the product-generated value information based on the rule calculation method corresponding to the product value distribution rule information. Then, the value-sharing ratio is multiplied by the aforementioned product-generated value information to generate the basic product value information.

[0035] Sub-step 3 involves determining the upstream and downstream user information sets corresponding to the aforementioned user team information. The upstream user information can be the upstream users of the user corresponding to the user information. Upstream users are the superior users who guide the user corresponding to the user information in various aspects of product value processing and operation. Downstream users are users who can be guided by the user corresponding to the user information in various aspects of product value processing and operation.

[0036] Sub-step 4: Based on the aforementioned online user information set, offline user information set, and product usage anomaly detection information set, and utilizing the aforementioned product value distribution rule information, determine the team value change information. This team value change information can be the value reward the team brings to users.

[0037] Sub-step 5: Generate the product value information based on the basic product value information and the team value change information.

[0038] As an example, the aforementioned implementing entity can add the basic product value information and the aforementioned team value change information to generate product value information.

[0039] In some optional implementations of certain embodiments, the aforementioned executing entity may determine team value change information based on the aforementioned online user information set, the aforementioned offline user information set, and the aforementioned product usage anomaly detection information set, utilizing the aforementioned product value distribution rule information, including the following steps:

[0040] The first step is to determine the team value deduction information for each online user in the aforementioned set of online user information, based on the corresponding value information and the product value distribution rules. This team value deduction information can be the deduction amount resulting from a user distributing value to an online user. The team value deduction information can be a negative value.

[0041] As an example, the aforementioned implementing entity can filter out the product value distribution sub-rules corresponding to the value information of the online user information from the product value distribution rule information. Then, based on the product value distribution sub-rules and the value rewards generated by the user, the team value deduction information is determined.

[0042] The second step is to perform the following second generation step for each piece of information about a downstream user in the above downstream user information set:

[0043] Sub-step 1: Based on the value information corresponding to the aforementioned downstream user information, and using the aforementioned product value distribution rules, determine the initial team value increase information corresponding to the aforementioned downstream user information. The team value increase information can be the value increase resulting from distributing the team value generated by the downstream users to the users. The initial team value increase information can be an integer value.

[0044] As an example, the aforementioned implementing entity can filter out the product value distribution sub-rules that should be rewarded under the value information corresponding to user information from the product value distribution rule information. Then, based on the product value distribution sub-rules and the value rewards generated by the downstream users, the team value increase information can be determined.

[0045] Sub-step 2 involves obtaining the product usage anomaly detection information corresponding to the aforementioned offline user information. The product usage anomaly detection information for each offline user in the aforementioned offline user information set is pre-determined based on the funnel detection model in the risk control process. The product usage anomaly detection information can represent the magnitude of anomalies in the product usage of the lower limit user.

[0046] Sub-step 3: Based on the above product usage anomaly detection information and the above initial team value increase information, generate team value increase information.

[0047] As an example, the aforementioned implementing entity can normalize the product usage anomaly detection information to generate a normalized value. Then, it can multiply the product usage anomaly detection information with the aforementioned initial team value enhancement information to generate team value enhancement information.

[0048] The third step is to add the obtained team value deduction information set and the obtained team value increase information set together to generate team value change information.

[0049] In some optional implementations of certain embodiments, the execution entity may utilize the aforementioned calculation module to determine the product value information set corresponding to the aforementioned user information set based on the product usage level information set, the product generated value information set, and the user team information set, including the following steps:

[0050] The first step is to use the large model call interface to retrieve the large language model from the aforementioned calculation module.

[0051] The second step involves generating value generation prompts based on the product usage level information set, the product value generation information set, and the user team information set. These value generation prompts can be keywords used to generate product value information.

[0052] The third step is to send the aforementioned value generation prompt information to the aforementioned large language model to generate the product value information set corresponding to the aforementioned user information set.

[0053] Step 104: Using the above-mentioned sending module, send the encrypted product value information set corresponding to the above-mentioned product value information set to the terminal of the next higher level area corresponding to the above-mentioned target area.

[0054] In some embodiments, the executing entity may use the sending module to send the encrypted product value information set corresponding to the product value information set to the upper-level regional terminal corresponding to the target region. The upper-level regional terminal includes the backup data of each lower-level regional terminal.

[0055] Step 105: In response to confirming that the verification information sent by the aforementioned higher-level regional terminal is correct, for each user information in the aforementioned user information set, the following sending steps are executed:

[0056] Step 1051: Package the product usage level information, product value generation information, user team information, the above user information, and product value distribution rule information corresponding to the above user information to generate value packaging information.

[0057] In some embodiments, the aforementioned executing entity may package the product usage level information, product value generation information, user team information, the aforementioned user information, and product value distribution rule information corresponding to the aforementioned user information to generate value packaging information.

[0058] Step 1052: Using the aforementioned sending module, the aforementioned value package information is sent to the user terminal in the sending method determined by the user corresponding to the aforementioned user information.

[0059] In some embodiments, the executing entity may utilize the sending module to send the packaged value information to the user terminal using a sending method determined by the user corresponding to the user information. The sending method may be one of the following: an encrypted SMS link, or an encrypted email. The encrypted SMS link method may involve sending an encrypted link via SMS. The encrypted email method may involve sending an encrypted link via email.

[0060] In some optional implementations of certain embodiments, after step 105, the steps further include:

[0061] The first step, in response to the confirmation that the verification information sent by the aforementioned higher-level regional terminal is correct, is to store the obtained value package information set in the aforementioned storage module, and to set the storage duration for the value package information set in the aforementioned storage module. Specifically, after the storage duration for the value package information set in the storage module has been reached, the value package information set in the aforementioned storage module will be deleted.

[0062] The second step involves using the aforementioned sending module to send the packaged value information set to the aforementioned higher-level regional terminal.

[0063] The aforementioned higher-level regional terminal performs the following processing steps on the aforementioned value-packaged information set:

[0064] Sub-step 1: Obtain the value verification record corresponding to the aforementioned value package information set. This value verification record can be the process record information of the entire verification process by the higher-level regional terminal to confirm the accuracy of the value package information set.

[0065] Sub-step 2 involves storing the aforementioned value package information set, the aforementioned value audit record, and the regional information corresponding to the aforementioned target region into the storage module included in the aforementioned upper-level regional terminal.

[0066] In some optional implementations of certain embodiments, after step 105, the steps further include:

[0067] The system periodically receives product usage anomaly detection information sets for the aforementioned user information set from the terminals in the aforementioned higher-level regions. The periodicity can be set by relevant technical personnel based on practical experience.

[0068] The product usage anomaly detection information set corresponding to the aforementioned offline user information set in the aforementioned higher-level regional terminals is generated through the following steps:

[0069] Sub-step 1 involves filtering target user information from the target user information set in the aforementioned storage module, selecting those with corresponding user anomaly tags, to obtain at least one abnormal user information. The target anomaly tag can represent a tag indicating abnormal value usage. For example, the target anomaly tag can be "1" or "0". The target user information set can be a set of user information registered in at least one value transfer application. In practice, for credit reporting scenarios, the value transfer application can be an application related to credit reporting business. For example, the value transfer application can be a target bank application. User information can be user-related information. For example, user information can be a user identifier. There is a one-to-one correspondence between the historical value product call data sequence in the historical value product call data sequence set and the target user information in the target user information set. The historical value product call data sequence can be the value product call data sequence corresponding to a user within a historical time period. Value product call data can be data on calling value products. For example, value product call data can include, but is not limited to, at least one of the following: the number of times the value product is called, the time the value product is called, the method of calling the value product, the value borrowed by the value product, the value returned by the value product, the time the value product is borrowed, and the time the value product is returned. The target anomaly label can be a high anomaly label. User anomaly labels can be information representing the degree of value anomaly of a user. For example, user anomaly labels can be, but are not limited to, at least one of the following: low anomaly label, medium anomaly label, and high anomaly label. Anomaly user information can be user information of users with high anomaly value. The degree of value anomaly can be the degree of anomaly in value transfer. A low anomaly label can represent a user's value transfer being abnormally low. A medium anomaly label can represent a user's value transfer being abnormally moderate. A high anomaly label can represent a user's value transfer being abnormally high. In practice, for credit scoring scenarios, value transfer can be, but is not limited to, at least one of the following: loans, repayments, and transfers.

[0070] Sub-step 2 involves generating at least one target anomaly indicator based on at least one historical value product call data sequence corresponding to at least one abnormal user information. This target anomaly indicator can be at least one indicator used to determine the anomaly in the user's corresponding value. For example, the target anomaly indicator may include, but is not limited to, at least one of the following: number of borrowings, changes in loan frequency, changes in repayment frequency, and overdue status.

[0071] Sub-step 3 involves generating a first-stage risk control funnel detection model based on at least one of the aforementioned target anomaly indicators, targeting historical call data. This first-stage risk control funnel detection model can be a model generated from historical call data, representing various stages and performing funnel-shaped risk control detection. The first-stage risk control funnel detection model may include detection rules for each indicator.

[0072] As an example, the aforementioned execution entity can sort each of the at least one target anomaly indicators sequentially based on the frequency of each target anomaly indicator, thus obtaining a target anomaly indicator sequence. Then, it generates indicator detection rules for each target anomaly indicator in the aforementioned target anomaly indicator sequence, resulting in an indicator detection rule sequence. Finally, the indicator detection rule sequence is determined as the vulnerability detection model for the first risk control stage.

[0073] Sub-step 4: Based on the funnel detection model of the first risk control stage described above, perform user anomaly detection on the aforementioned offline user information set to obtain a first user anomaly detection information set. The aforementioned offline user information set is a subset of user information from the aforementioned target user information set after removing at least one of the aforementioned anomalous user information. There is a one-to-one correspondence between the first user anomaly detection information in the first user anomaly detection information set and the offline user information in the offline user information set. The first user anomaly detection information can be in numerical form or in label form. Higher values ​​indicate greater anomalies for the corresponding user. The first user anomaly detection information can represent the level of value anomaly corresponding to the user.

[0074] As an example, the aforementioned executing entity can use the funnel detection model of the first risk control link as the anomaly detection model to perform user anomaly detection on the offline user information set, thereby obtaining the first user anomaly detection information set.

[0075] Sub-step 5: For each piece of information about a downstream user in the aforementioned downstream user information set, determine the team anomaly detection information corresponding to that downstream user information based on the user team information. This team anomaly detection information can be detection information indicating that the team corresponding to the downstream user is experiencing anomalies. In practice, the team anomaly detection information can be numerical information. The larger the corresponding value, the greater the probability of the team being anomaly.

[0076] As an example, the aforementioned implementing entity can determine team anomaly detection information based on changes in team value. Specifically, changes in team value can represent the overall change in the total value of the entire team.

[0077] Sub-step 6: Based on the first user anomaly detection information set and the team anomaly detection information set mentioned above, generate a product usage anomaly detection information set for the offline user information set mentioned above.

[0078] As an example, the aforementioned executing entity can perform corresponding weighted summation processing on the first user anomaly detection information set and the team anomaly detection information set to generate a product usage anomaly detection information set for the aforementioned offline user information set.

[0079] In some optional implementations of certain embodiments, the aforementioned execution entity may generate a product usage anomaly detection information set for the aforementioned offline user information set based on the aforementioned first user anomaly detection information set and team anomaly detection information set, including the following steps:

[0080] The first step involves obtaining a future anomaly prediction model for each of the at least one target anomaly indicator from the target terminal, thus obtaining at least one future anomaly prediction model. This future anomaly prediction model can be a neural network model that predicts future anomaly information for the corresponding target anomaly indicator. In practice, the future anomaly prediction model can be a recurrent neural network model. The future anomaly prediction model can be a pre-trained model. Specific training methods will not be elaborated further. The at least one future anomaly prediction model is obtained through terminal retrieval based on the target terminal.

[0081] The second step is to generate a second risk control funnel detection model for the prediction model of at least one of the aforementioned future anomaly indicators. This second risk control funnel detection model can be a model generated based on future data, representing the funnel-shaped risk control detection at each stage.

[0082] As an example, the aforementioned implementing entity can combine at least one future abnormal indicator prediction model based on the number of times each target abnormal indicator corresponds to the indicator to generate a funnel detection model for the second risk control link.

[0083] The third step involves performing user anomaly detection on the aforementioned offline user information set, based on the funnel detection model of the second risk control stage, to obtain a second user anomaly detection information set. There is a one-to-one correspondence between the second user anomaly detection information in the second user anomaly detection information set and the offline user information in the offline user information set. The second user anomaly detection information can characterize the level of value anomaly of the corresponding user.

[0084] As an example, the aforementioned implementing entity can use the second risk control funnel detection model as the anomaly detection model to perform user anomaly detection on the offline user information set, thereby obtaining the second user anomaly detection information set.

[0085] The fourth step is to obtain the current value product call dataset for the aforementioned target user information set. This current value product call data can be the value product call data for the corresponding target user information at the current time.

[0086] The fifth step is to filter out the feature information related to at least one of the above-mentioned target anomaly indicators from the current value product call dataset to obtain the filtered call dataset.

[0087] The sixth step involves calling the computing resources corresponding to the current graphics processor to perform clustering processing on the filtered call dataset, thereby obtaining the first user information cluster.

[0088] Step 7: Utilize the computing resources corresponding to the current graphics processor to perform clustering processing on the aforementioned current value product call dataset, obtaining a second user information cluster. As an example, the executing entity can use the K-means algorithm to perform clustering processing on the aforementioned current value product call dataset to obtain the second user information cluster. Each user information in the second user information cluster shares at least one common feature. For example, at least one feature includes, but is not limited to, at least one of the following: user's region, user indicator information, and user profile features.

[0089] Step 8: For each offline user in the aforementioned offline user information set, call upon the computing resources corresponding to the current central processing unit to execute the following first generation step:

[0090] Sub-step 1: Determine the first user information cluster in the first user information cluster set corresponding to the above-mentioned offline user information, and use it as the first target user information cluster.

[0091] Sub-step 2: Determine the number of first user information entries that include abnormal user information in the aforementioned first target user information cluster.

[0092] Sub-step 3: Determine the first ratio between the number of first user information items and the number of first user information clusters, wherein the number of first user information clusters is the number of user information items corresponding to the first target user information cluster.

[0093] Sub-step 4: Determine the second user information cluster in the second user information cluster set corresponding to the above-mentioned offline user information, and use it as the second target user information cluster.

[0094] Sub-step 5: Determine the number of second user information entries that include abnormal user information in the aforementioned second target user information cluster.

[0095] Sub-step 6, in response to determining a second ratio between the number of second user information items and the number of second user information clusters, wherein the number of second user information clusters is the number of user information items corresponding to the second target user information cluster.

[0096] Sub-step 7: Generate the third user anomaly detection information based on the first ratio and the second ratio.

[0097] As an example, the aforementioned executing entity can add the first ratio and the second ratio to generate a summed value, which can then be used as the third user anomaly detection information.

[0098] Step 10: Based on the above-mentioned first user anomaly detection information set, the above-mentioned second user anomaly detection information set, the obtained third user anomaly detection information set, and the above-mentioned team anomaly detection information set, generate the above-mentioned product usage anomaly detection information set.

[0099] As an example, the aforementioned executing entity can generate the aforementioned product usage anomaly detection information set by using a weighted summation method based on the aforementioned first user anomaly detection information set, the aforementioned second user anomaly detection information set, the obtained third user anomaly detection information set, and the aforementioned team anomaly detection information set.

[0100] Optionally, generating the third user anomaly detection information based on the first ratio and the second ratio may include the following steps:

[0101] The first step is to set the indicator importance value corresponding to at least one of the above-mentioned target anomaly indicators as a preset value. For example, the preset value could be the value "1". The indicator importance value can characterize the degree of influence of at least one target anomaly indicator on the detection of value risk control anomalies.

[0102] The second step is to generate the key numerical values ​​of the indicators corresponding to the indicator set of the current value product call dataset, which will serve as the target indicator key values. The indicator set includes at least one target anomaly indicator. The indicator set can be any of the indicators involved in the current value product call dataset. The target indicator key value characterizes the degree of influence of the indicator in the indicator set on the detection of value risk control anomalies. The higher the target indicator key value, the greater its impact on responding to value risk control anomaly detection.

[0103] As an example, the aforementioned execution entity can utilize a self-attention mechanism to generate important values ​​for the indicator set corresponding to the current value product call dataset, which can then be used as important values ​​for the target indicator.

[0104] The third step involves using the preset values ​​as the weights corresponding to the first ratio and the important values ​​of the target indicators as the weights of the second ratio, performing weighted processing on the first and second ratios, and generating third user anomaly detection information.

[0105] Optionally, generating the product usage anomaly detection information set based on the first user anomaly detection information set, the second user anomaly detection information set, the obtained third user anomaly detection information set, and the team anomaly detection information set may include the following steps:

[0106] For each piece of first user anomaly detection information in the aforementioned first user anomaly detection information set, the following third generation step is performed:

[0107] Sub-step 1: Determine the second user anomaly detection information in the set of the second user anomaly detection information corresponding to the first user anomaly detection information, as the second target user anomaly detection information; determine the third user anomaly detection information in the set of the third user anomaly detection information corresponding to the first user anomaly detection information, as the third target user anomaly detection information; and determine the team anomaly detection information in the set of the team anomaly detection information corresponding to the first user anomaly detection information, as the target team anomaly detection information.

[0108] Sub-step 2 involves determining the risk control detection weight information set, which includes: risk control detection weight information corresponding to the first user anomaly detection information set, the second user anomaly detection information set, the third user anomaly detection information set, and the team anomaly detection information set. This risk control detection weight information set can represent the importance of the first user anomaly detection information set, the second user anomaly detection information set, the third user anomaly detection information set, and the team anomaly detection information set. Furthermore, the anomaly detection weight information set can also indirectly represent the importance attached to historical data, current data, and future data. In practice, the anomaly detection weight information set can be pre-set weight values.

[0109] Sub-step 3: Based on the aforementioned risk control detection weight information set, the aforementioned first user anomaly detection information, the aforementioned second target user anomaly detection information, the aforementioned third target user anomaly detection information, and the target team anomaly detection information are fused to generate fused anomaly detection information, which is used as the initial product anomaly detection information.

[0110] As an example, the aforementioned executing entity performs weighted summation processing on the aforementioned first user anomaly detection information, the aforementioned second target user anomaly detection information, the aforementioned third target user anomaly detection information, and the target team anomaly detection information based on the aforementioned risk control detection weight information set, in order to generate initial product usage anomaly detection information.

[0111] As another example, the aforementioned executing entity can integrate the tag information of the aforementioned first user anomaly detection information, the aforementioned second target user anomaly detection information, the aforementioned third target user anomaly detection information, and the target team anomaly detection information based on the aforementioned risk control detection weight information set, in order to generate initial product usage anomaly detection information.

[0112] Optionally, generating a product usage anomaly detection information set for the offline user information set based on the first user anomaly detection information set and the team anomaly detection information set may include the following steps:

[0113] The first step is to determine the indicator importance information corresponding to each of the at least one target anomaly indicator, thereby obtaining at least one indicator importance information. The information corresponding to the at least one indicator importance information is then aggregated into a preset value. The indicator importance information characterizes the importance of the target anomaly indicator. The indicator importance information can be a value between 0 and 1. The at least one indicator importance information can be manually preset or generated through a self-attention mechanism. Information aggregation can be performed after considering the value corresponding to the at least one indicator importance information. The preset value can be the value "1".

[0114] The second step is to obtain a set of indicator combination information for at least one target abnormal indicator. Each indicator combination information sequence includes a target abnormal indicator group, where the sum of the important information of the indicators corresponding to the target abnormal indicator group is greater than the target value. The target abnormal indicator group sequence corresponding to the indicator combination information sequence is sorted according to the sum of the important information of the indicators. In practice, the target value can be the value "0.5".

[0115] The third step involves classifying the offline user information in the aforementioned first user anomaly detection information set into hierarchical categories to generate offline user information groups, resulting in a sequence of offline user information groups. These offline user information groups can be sorted according to their user information hierarchy from highest to lowest. Higher user information hierarchies indicate lower anomaly value for the corresponding offline user information group.

[0116] As an example, the aforementioned execution entity can determine the detection information interval where each first user anomaly detection information is located, in order to generate the user information hierarchy corresponding to the detection interval. Based on the obtained user information hierarchy set, the user information of each offline user in the aforementioned offline user information set is divided into user information hierarchical divisions to generate offline user information groups, resulting in an offline user information group sequence.

[0117] The fourth step is to determine each offline user information group in the above sequence of offline user information groups and perform the following fourth generation step:

[0118] Sub-step 1: Determine the target abnormal indicator group in the target abnormal indicator group sequence corresponding to the above-mentioned offline user information group, and use it as the matching abnormal indicator group.

[0119] Sub-step 2 involves generating a third-stage risk control funnel detection model for the aforementioned group of abnormal matching indicators. This third-stage risk control funnel detection model can be a pre-defined model for detecting risk control funnel patterns based on each abnormal matching indicator in the group. The third-stage risk control funnel detection model may include indicator rules corresponding to each abnormal matching indicator in the group.

[0120] Sub-step 3, in response to determining that a predetermined time period has been reached, utilizes the funnel detection model of the third risk control step described above to perform user anomaly detection on the aforementioned offline user information set, obtaining the first initial product usage anomaly detection information set. The predetermined time period can be a pre-set periodicity. The predetermined time period can also be the duration for updating the corresponding anomaly detection information for a user.

[0121] As an example, the aforementioned implementing entity can use the third risk control funnel detection model as the anomaly detection model to perform user anomaly detection on the offline user information set, thereby obtaining the first initial product usage anomaly detection information set.

[0122] Sub-step 4: Determine the team anomaly detection information corresponding to the offline user information group, and use it as the target team anomaly detection information.

[0123] Sub-step 5: Using a pre-defined association table between team anomalies and product usage anomalies, determine the product usage anomaly information corresponding to the aforementioned target team anomaly detection information, as the second initial product usage anomaly information.

[0124] Sub-step 6 involves performing a weighted summation of the first initial product usage anomaly detection information set and the second initial product usage anomaly information set to obtain the product usage anomaly detection information set.

[0125] The aforementioned "in some optional implementations of some embodiments," as an inventive point, solves the problem of "how to accurately generate a product usage anomaly detection information set based on the aforementioned first user anomaly detection information set and team anomaly detection information set while invoking various computing resources." Based on this, this disclosure, through at least one future anomaly indicator prediction model, a second risk control environment vulnerability detection model, and a clustering algorithm, utilizes the computing resources corresponding to the graphics processing unit and the central processing unit to accurately generate a product usage anomaly detection information set while appropriately invoking the corresponding computing resources.

[0126] The above embodiments of this disclosure have the following beneficial effects: Through the information sending method based on multi-level regional terminals in some embodiments of this disclosure, based on the interaction between multi-level regional terminals, product value distribution information can be accurately and efficiently encrypted and sent to the corresponding users. Specifically, the reason why the relevant product value distribution information is not accurate and efficient is that: when there are a large number of users in a region, the calculation script takes a long time to compute, and the calculation script cannot effectively guarantee the accuracy of product value information and the prevention of information leakage, and often lacks the relevant steps for review through multi-level terminal interaction. Based on this, the information sending method based on multi-level regional terminals in some embodiments of this disclosure firstly, in response to receiving a product value distribution request corresponding to a target region, retrieves the user information set corresponding to the target region from the storage module. The target region has a corresponding regional terminal, which includes a calculation module, a storage module, and a sending module. Here, through the calculation module, storage module, and sending module included in the regional terminal, the various functions of the terminal can be distinguished, so that the coordination between the various modules can ensure that the regional terminal can efficiently and accurately generate subsequent product value information. Then, the product usage level information set, product value generation information set, user team information set, and product usage anomaly detection information set corresponding to the aforementioned user information set are retrieved from the storage module to facilitate accurate generation of product value information. Next, using the aforementioned calculation module, the product value information set corresponding to the aforementioned user information set can be accurately determined based on the aforementioned product usage level information set, product value generation information set, user team information set, and product usage anomaly detection information set. Then, using the aforementioned sending module, the encrypted product value information set corresponding to the aforementioned product value information set is sent to the upper-level regional terminal corresponding to the aforementioned target region. The upper-level regional terminal includes the terminal backup data corresponding to each lower-level regional terminal. Here, by setting up multi-level regional terminals, the generation and review of product value information can be separated, avoiding the waste of excessive computing resources and the possibility of inaccurate and unobjective review of information that would otherwise occur on the same terminal. In addition, by setting up multi-level regional terminals, multi-level backup of terminal data can be ensured, which can reduce the storage pressure on lower-level regional terminals and ensure the information integrity of higher-level regional terminals. This enables more accurate verification of product value information and generation of product usage anomaly detection information, and maintains the target product for each user.Finally, in response to the confirmation that the verification information sent by the aforementioned higher-level regional terminal is correct, the following sending steps are executed for each user information in the aforementioned user information set: First, the product usage level information, product value generation information, user team information, the aforementioned user information, and product value distribution rule information corresponding to the aforementioned user information are packaged to generate value package information. Here, after the higher-level regional terminal confirms the verification is correct, packaging is used to facilitate subsequent delivery to users, allowing them to see complete information related to product value distribution. Second, using the aforementioned sending module, the value package information is sent to the user terminal using the sending method determined by the user corresponding to the aforementioned user information. The sending method is one of the following: encrypted SMS link or encrypted email. Here, based on the user's chosen sending method, encryption is used during the sending process to ensure the information security of the value package information and prevent information leakage. In summary, through the deployment of multi-level regional terminals, each process of product value information can be executed in multiple modules across multiple terminals, ensuring the efficiency, accuracy, and security of product value information generation.

[0127] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of an information transmission device based on a multi-level regional terminal. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this information transmission device based on multi-level regional terminals can be specifically applied to various electronic devices.

[0128] like Figure 2As shown, an information transmission device 200 based on multi-level regional terminals includes: a first acquisition unit 201, a second acquisition unit 202, a determination unit 203, a transmission unit 204, and an execution unit 205. The first acquisition unit 201 is configured to, in response to receiving a product value distribution request corresponding to a target region, acquire a user information set corresponding to the target region from a storage module. The target region has a corresponding regional terminal, which includes a calculation module, a storage module, and a transmission module. The second acquisition unit 202 is configured to acquire from the storage module a product usage level information set, a product generated value information set, a user team information set, and a product usage anomaly detection information set corresponding to the user information set. The determination unit 203 is configured to, using the calculation module, determine the product value information set corresponding to the user information set based on the product usage level information set, the product generated value information set, the user team information set, and the product usage anomaly detection information set. The transmission unit 204 is configured to, using the calculation module, determine the product value information set corresponding to the user information set. The sending module sends the encrypted product value information set corresponding to the aforementioned product value information set to the terminal of the next-level region corresponding to the target region, wherein the next-level region terminal includes the terminal backup data corresponding to each of the lower-level region terminals; the execution unit 205 is configured to, in response to determining that the verification information sent by the next-level region terminal is correct, perform the following sending steps for each piece of user information in the aforementioned user information set: package the product usage level information, product value generation information, user team information, the aforementioned user information, and product value distribution rule information corresponding to the aforementioned user information to generate value package information; using the aforementioned sending module, send the aforementioned value package information to the user terminal in a sending method determined by the user corresponding to the aforementioned user information, wherein the aforementioned sending method is one of the following: encrypted SMS link method, encrypted email method.

[0129] It is understandable that the units described in the information transmission device 200 based on multi-level regional terminals are related to the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the information transmission device 200 based on multi-level regional terminals and the units contained therein, and will not be repeated here.

[0130] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (e.g., an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0131] like Figure 3As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, 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. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0132] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, 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 alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0133] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0134] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0135] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0136] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: in response to receiving a product value distribution request corresponding to a target region, retrieve a user information set corresponding to the target region from a storage module, wherein the target region has a corresponding regional terminal, and the regional terminal includes: a computing module, a storage module, and a sending module; retrieve a product usage level information set, a product generated value information set, a user team information set, and a product usage anomaly detection information set corresponding to the aforementioned user information set from the storage module; and, using the computing module, determine a product value information set corresponding to the aforementioned user information set based on the aforementioned product usage level information set, the aforementioned product generated value information set, the aforementioned user team information set, and the aforementioned product usage anomaly detection information set. Using the aforementioned sending module, the encrypted product value information set corresponding to the aforementioned product value information set is sent to the terminal of the next-level region corresponding to the aforementioned target region. The next-level region terminal includes backup data for each terminal corresponding to each lower-level region terminal. In response to the determination that the verification information sent by the next-level region terminal is correct, for each piece of user information in the aforementioned user information set, the following sending steps are performed: The product usage level information, product value generation information, user team information, the aforementioned user information, and product value distribution rule information corresponding to the aforementioned user information are packaged to generate value package information. Using the aforementioned sending module, the value package information is sent to the user terminal using a sending method determined by the user corresponding to the aforementioned user information. The sending method is one of the following: encrypted SMS link method, encrypted email method.

[0137] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and 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, as a standalone 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 remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0139] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a first acquisition unit, a second acquisition unit, a determination unit, a sending unit, and an execution unit. The names of these units do not necessarily limit the specific unit itself; for example, the first acquisition unit may also be described as "a unit that, in response to receiving a product value distribution request corresponding to a target area, acquires a set of user information corresponding to the target area from a storage module."

[0140] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0141] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for transmitting information based on multi-level regional terminals, comprising: In response to receiving a product value distribution request corresponding to a target area, the system retrieves a set of user information corresponding to the target area from the storage module. The target area has a corresponding regional terminal, and the regional terminal includes a calculation module, a storage module, and a sending module. The storage module retrieves the product usage level information set, product generated value information set, user team information set, and product usage anomaly detection information set corresponding to the user information set. Using the calculation module, the product value information set corresponding to the user information set is determined based on the product usage level information set, the product generated value information set, the user team information set, and the product usage anomaly detection information set; Using the sending module, the encrypted product value information set corresponding to the product value information set is sent to the upper-level regional terminal corresponding to the target area, wherein the upper-level regional terminal includes the terminal backup data corresponding to each lower-level regional terminal; In response to the determination that the verification information sent by the higher-level regional terminal is correct, for each user information in the user information set, the following sending steps are performed: The product usage level information, product value generation information, user team information, user information, and product value distribution rules information corresponding to the user information are packaged together to generate value packaging information; Using the sending module, the value package information is sent to the user terminal in a sending method determined by the user corresponding to the user information, wherein the sending method is one of the following: encrypted SMS link method, encrypted email method; Periodically receive product usage anomaly detection information sets for the user information set sent by the upper-level regional terminal; The product usage anomaly detection information set corresponding to the offline user information set in the aforementioned higher-level regional terminal is generated through the following steps: Target user information whose corresponding user abnormality tag is the target abnormality tag is filtered from the target user information set in the storage module to obtain at least one abnormal user information; Based on at least one historical value product call data sequence corresponding to the at least one abnormal user information, generate at least one target abnormal indicator; Based on the at least one target anomaly indicator, generate a first risk control loop funnel detection model for historical call data; Based on the first risk control funnel detection model, perform user anomaly detection on the offline user information set to obtain the first user anomaly detection information set; For each offline user information in the offline user information set, the team anomaly detection information corresponding to the offline user information is determined based on the user team information corresponding to the offline user information. Based on the first user anomaly detection information set and the team anomaly detection information set, a product usage anomaly detection information set is generated for the offline user information set.

2. The method according to claim 1, wherein, The method further includes: In response to determining that the verification information sent by the upper-level regional terminal is correct, the obtained value package information set is stored in the storage module, and the storage duration of the value package information set is set in the storage module; Using the aforementioned sending module, the value-packed information set is sent to the next-level regional terminal; and The higher-level regional terminal performs the following processing steps on the value-packaged information set: Obtain the value verification records corresponding to the value package information set; The value package information set, the value verification record, and the regional information corresponding to the target region are stored in the storage module included in the upper-level regional terminal.

3. The method according to claim 1, wherein, The step of using the calculation module to determine the product value information set corresponding to the user information set based on the product usage level information set, the product generated value information set, the user team information set, and the product usage anomaly detection information set includes: For each piece of user information in the user information set, the following first generation step is performed using the calculation module: Based on the product usage level information, determine the product value distribution rule information corresponding to the user information; Based on the product value distribution rules and the product value generation information, the basic product value information corresponding to the user information is determined; Determine the online user information set and offline user information set corresponding to the user team information; Based on the online user information set, the offline user information set, and the product usage anomaly detection information set, the team value change information is determined using the product value distribution rule information; The product value information is generated based on the basic product value information and the team value change information.

4. The method according to claim 3, wherein, The step of determining team value change information based on the online user information set, the offline user information set, and the product usage anomaly detection information set, using the product value distribution rule information, includes: For each piece of information about an online user in the set of online user information, the team value deduction information corresponding to the online user information is determined based on the value information corresponding to the online user information and using the product value distribution rule information. For each piece of offline user information in the offline user information set, perform the following second generation step: Based on the value information corresponding to the downstream user information, and using the product value distribution rule information, the initial team value increase information corresponding to the downstream user information is determined; Obtain the product usage anomaly detection information corresponding to the offline user information, wherein the product usage anomaly detection information corresponding to each offline user information in the offline user information set is predetermined based on the risk control funnel detection model; Based on the product usage anomaly detection information and the initial team value increase information, generate team value increase information; The team value deduction information set and the team value increase information set are added together to generate team value change information.

5. The method according to claim 1, wherein, The step of using the calculation module to determine the product value information set corresponding to the user information set based on the product usage level information set, the product generated value information set, and the user team information set includes: The large language model is retrieved from the computing module using the large model call interface. The generation representation generates value generation prompts for the product value information set based on the product usage level information set, the product generated value information set, and the user team information set; The value generation prompt information is sent to the large language model to generate the product value information set corresponding to the user information set.

6. An information transmission device based on a multi-level regional terminal, comprising: The first acquisition unit is configured to, in response to receiving a product value distribution request corresponding to a target area, acquire a set of user information corresponding to the target area from the storage module, wherein the target area has a corresponding regional terminal, and the regional terminal includes: a calculation module, a storage module and a sending module; The second acquisition unit is configured to acquire from the storage module a product usage level information set, a product generated value information set, a user team information set, and a product usage anomaly detection information set corresponding to the user information set. The determining unit is configured to use the computing module to determine the product value information set corresponding to the user information set based on the product usage level information set, the product generated value information set, the user team information set, and the product usage anomaly detection information set. The sending unit is configured to use the sending module to send the encrypted product value information set corresponding to the product value information set to the upper-level regional terminal corresponding to the target area, wherein the upper-level regional terminal includes the terminal backup data corresponding to each lower-level regional terminal; The execution unit is configured to, in response to determining that the verification information sent by the upper-level regional terminal is correct, perform the following sending steps for each piece of user information in the user information set: package the product usage level information, product value generation information, user team information, the user information, and product value distribution rule information corresponding to the user information to generate value package information; and use the sending module to send the value package information to the user terminal using a sending method determined by the user corresponding to the user information, wherein the sending method is one of the following: encrypted SMS link method, encrypted email method, etc. The device further includes: periodically receiving product usage anomaly detection information sets for the user information set sent by the upper-level regional terminal; and the product usage anomaly detection information set corresponding to the offline user information set in the upper-level regional terminal is generated through the following steps: filtering target user information with corresponding user anomaly tags as target anomaly tags from the target user information set in the storage module to obtain at least one abnormal user information; generating at least one target anomaly indicator based on at least one historical value product call data sequence corresponding to the at least one abnormal user information; generating a first risk control funnel detection model for historical call data based on the at least one target anomaly indicator; performing user anomaly detection for the offline user information set based on the first risk control funnel detection model to obtain a first user anomaly detection information set; for each offline user information in the offline user information set, determining team anomaly detection information corresponding to the offline user information based on the user team information corresponding to the offline user information; and generating a product usage anomaly detection information set for the offline user information set based on the first user anomaly detection information set and the team anomaly detection information set.

7. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-5.

8. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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