Method, device, apparatus and computer-readable medium for generating a value subject dictionary

Abstract

The embodiments of the present disclosure disclose a method, device, equipment and computer-readable medium for generating a value account dictionary. A specific implementation of the method includes: obtaining a target value institution registration file; generating value flow cleanup information; obtaining a value flow business category set and an initial value account dictionary; processing the value flow cleanup information into value atom information to obtain a value atom information set; allocating each value atom information to a corresponding computing node; updating the value atom information subset corresponding to each computing node in parallel to generate a value account change dictionary; performing information change verification on the change information under each value account to generate a verification result; in response to determining that the verification result indicates that the value account change dictionary is correct, storing the value account change dictionary in the target database. This implementation can accurately and efficiently realize the information processing corresponding to the value flow cleanup information while avoiding wasting computing resources.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, apparatus, device, and computer-readable medium for generating a value subject dictionary. Background Art

[0002] At present, value flow cleaning is an important function in the value processing system, which is responsible for providing the value processing system with correct value flow data to ensure the normal operation of multiple functions of the value processing system. For the information processing of value flow cleaning information, the usual method is: in the process of executing information processing, the information processing of the above value flow cleaning information is placed in the central processing unit (CPU) for execution on a product basis to generate information processing results.

[0003] However, when using the above method to process value flow cleaning information, the following technical problems often occur:

[0004] With the expansion of business scale and the application of technologies such as quantitative trading, the demand for computing power for value processing operations is increasing. The computing power of the CPU alone may not be able to meet the production needs. In addition, parallel processing in units of products (if there are multiple value processing operations for the same product in parallel, it will cause read-write conflicts, and the processing logic and time consumption will increase) will cause a waste of computing resources in the CPU.

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

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose a value subject dictionary generation method, apparatus, device and computer-readable medium to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for generating a value account dictionary, including: obtaining a target value institution registration file from a target database; generating value flow clearing information in a target format based on the target value institution registration file; obtaining a preset value flow business category set and an initial value account dictionary; processing the value flow clearing information into value atom information at a target granularity based on the value flow business category set to obtain a value atom information set; allocating each value atom information in the value atom information set to a corresponding computing node; based on the initial value account dictionary, using a pre-deployed graphics processor set, updating the change amount at the value account granularity for the value atom information subsets corresponding to each computing node in parallel to generate a value account change dictionary; performing information change verification on the change information under each value account in the value account change dictionary to generate a verification result; in response to determining that the verification result indicates that the value account change dictionary is correct, storing the value account change dictionary in the target database.

[0009] In a second aspect, some embodiments of the present disclosure provide a value account dictionary generation device, comprising: a first acquisition unit, configured to acquire a target value institution registration file from a target database; a generation unit, configured to generate value flow cleanup information in a target format according to the target value institution registration file; a second acquisition unit, configured to acquire a preset value flow business category set and an initial value account dictionary; a processing unit, configured to process the value flow cleanup information into value atomic information at a target granularity according to the value flow business category set, and obtain a value atomic information set; an allocation unit, configured to allocate the value atomic information to the target value atomic information set; Each value atom information in the sub-information set is allocated to a corresponding computing node; the update unit is configured to update the change amount at the value account granularity of the value atom information subset corresponding to each computing node in parallel based on the above-mentioned initial value account dictionary using a pre-deployed graphics processor set to generate a value account change dictionary; the verification unit is configured to verify the change information under each value account in the above-mentioned value account change dictionary to generate a verification result; the storage unit is configured to store the above-mentioned value account change dictionary in the target database in response to determining that the above-mentioned verification result indicates that the above-mentioned value account change dictionary is correct.

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

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

[0012] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the value subject dictionary generation method of some embodiments of the present disclosure, the information processing corresponding to the value flow cleaning information can be accurately and efficiently realized under the premise of avoiding the waste of computing resources. Specifically, in the process of information processing corresponding to the value flow cleaning information, the reason for the waste of relevant computing resources is that with the expansion of business scale and the application of technologies such as quantitative trading, the demand for computing power for value processing operations is increasing, and the computing power of the CPU alone may not be able to meet the production needs. And parallel processing in units of products (if there are multiple value processing operations for the same product in parallel, it will cause read-write conflicts, and the processing logic and time consumption will increase), resulting in a waste of computing resources in the CPU. Based on this, the value subject dictionary generation method of some embodiments of the present disclosure, first, obtains the target value institution registration file from the target database as the data source file of the target value institution, so as to facilitate the subsequent generation of full and accurate value flow cleaning information. Then, according to the above-mentioned target value institution registration file, the value flow cleaning information in the target format is accurately generated to facilitate the value processing of the value flow cleaning information. Next, obtain the preset value flow business category set and the initial value account dictionary, so as to process the value flow cleaning information from the perspective of the value flow business category. Then, according to the above value flow business category set, the above value flow cleaning information is processed into value atom information at the target granularity to obtain the value atom information set, so as to perform task decomposition of the information processing task of the value flow cleaning information at the granularity of the value atom information, so as to facilitate the subsequent allocation of corresponding computing resources and avoid waste of computing resources. Secondly, each value atom information in the above value atom information set is allocated to the corresponding computing node, so as to facilitate the efficient processing of subsequent information processing tasks. Furthermore, according to the above initial value account dictionary, the pre-deployed graphics processor set is used to update the change amount at the value account granularity of the corresponding value atom information subset of each computing node in parallel, so as to accurately generate the value account change dictionary. Here, by using the image processor set, parallel processing and updating the change amount at the value account granularity, the value account change dictionary representing the corresponding change of each value account can be accurately and meticulously generated. Further, the change information under each value subject in the value subject change dictionary is verified to generate a verification result to ensure the accuracy of the information in the value subject change dictionary. Finally, in response to determining that the verification result indicates that the value subject change dictionary is correct, the value subject change dictionary is stored in the target database.In summary, by processing the value flow clearing information into value atomic information at the target granularity, utilizing a graphics processor set, parallel processing, and updating the change amount at the value account granularity, a value account change dictionary representing the corresponding changes of each value account can be accurately and meticulously generated, so as to achieve efficient information processing of value flow clearing information and avoid waste of computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0014] Figure 1 is a flow chart of some embodiments of the value subject dictionary generation method according to the present disclosure;

[0015] Figure 2 is a schematic diagram of the structure of some embodiments of the value subject dictionary generation device according to the present disclosure;

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

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

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

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

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

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

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] refer to Figure 1 , shows a process 100 of some embodiments of the method for generating a value subject dictionary according to the present disclosure. The method for generating a value subject dictionary includes the following steps:

[0024] Step 101, obtaining the target value institution registration file from the target database.

[0025] In some embodiments, the execution subject (e.g., electronic device) of the above-mentioned value subject dictionary generation method can obtain the target value institution registration file from the target database. The target database can be a database storing the value data corresponding to each target value institution. In practice, the target database can be an Oracle database or an OceanBase database. The target value institution registration file can be a value registration file corresponding to the target value institution. The value registration file can be a file including each value processing record in the target value institution. The value data can be data related to the value processing operation. In practice, the value processing operation can include but is not limited to at least one of the following: value value inflow operation, value value outflow operation, value product inflow operation, value product outflow operation. In practice, for the financial field, the target value institution can be a target financial institution (e.g., a securities company). The value value inflow operation can be a capital inflow operation. The value value outflow operation can be a capital outflow operation. The value product inflow operation can be a securities buying operation. The value product outflow operation can be a securities selling operation. The value processing record can be but is not limited to one of the following: a capital flow record, a securities flow record, and a fund change record.

[0026] Step 102, based on the target value institution registration file, generate value flow clearing information in the target format.

[0027] In some embodiments, the above-mentioned execution entity can generate value flow clearing information in the target format based on the above-mentioned target value institution registration file. Among them, the target format can be a pre-set information format. The value flow clearing information can be the information after the value flow record is cleaned up. In practice, for the financial field, the value flow clearing information can be clearing flow data. A value flow clearing sub-information can be {"Date":"20240220","Product":"x","Asset Unit":"y","Securities Code":"z","Business Category":"Securities Buying","Market":"Location A","Quantity":"100","Amount":"500"}.

[0028] As an example, the above-mentioned execution entity can use the pre-set "Registration and Settlement Data Interface Specification" related interface information to perform value flow settlement processing on the file content in the above-mentioned target value institution registration file to generate value flow clearing information in the target format.

[0029] Step 103, obtaining a preset value transfer business category set and an initial value account dictionary.

[0030] In some embodiments, the execution subject may obtain a preset value transfer business category set and an initial value account dictionary. The value transfer business category may be a business category of a value transfer business. In practice, the value transfer business may be a business related to value transfer processing. Each value transfer business category in the value transfer business category set may be preset by a relevant value processing expert. The initial value account dictionary may be a dictionary to which value account change information has not yet been added. In practice, the initial value account dictionary only includes each predefined value account, product information corresponding to the value account, and asset unit. The value account may be each account related to value classification. In practice, the value account may include but is not limited to at least one of the following: an account corresponding to each value transfer content under the first type corresponding to the value product, an account corresponding to each value transfer content under the second type corresponding to the value product, an account corresponding to each value transfer content under the third type corresponding to the value product, and an account corresponding to each value transfer content under the value value. Each value transfer content under the value product may be various value change contents related to the value product. Each value operation under the value value may be various value change contents related to the value value conversion. In practice, for the financial field, the value transfer business category set may include but is not limited to at least one of the following: securities purchase operations, securities sales operations, fund transfer operations, fund transfer operations, bond purchase operations, bond sales operations, futures closing operations, and futures two-way trading operations. The first type of corresponding value products may be securities. The second type of corresponding value products may be bonds. The third type of corresponding value products may be futures. The subjects corresponding to each value flow content under the first type of corresponding value products may include but is not limited to at least one of the following: securities purchase quantity, securities purchase amount, securities cost, securities sales quantity, securities sales amount. The subjects corresponding to each value flow content under the second type of corresponding value products may include but is not limited to at least one of the following: bond purchase quantity, bond purchase amount, bond cost, bond sales quantity, bond sales amount. The subjects corresponding to each value flow content under the third type of corresponding value products may include but is not limited to at least one of the following: futures closing number, futures opening number, and futures volume. The subjects corresponding to each value flow content under the value value may include but is not limited to at least one of the following: liquidation amount, purchase amount, and unsettled freeze. For example, the information in the initial value account dictionary is in the form of key-value pairs. Specifically, the key content is [product-asset unit-value account]. The value content is the change information of the value account.

[0031] Step 104, based on the value flow business category set, the value flow cleanup information is processed into value atom information at a target granularity to obtain a value atom information set.

[0032] In some embodiments, the execution subject may process the value flow clearing information into value atomic information at a target granularity according to the value flow business category set to obtain a value atomic information set. The value atomic information may be change information of a value account at the minimum information granularity. The target granularity may be the fund account granularity.

[0033] For example, three pieces of atomic information about fund values ​​are as follows:

[0034] The value atom information can be {"Date":"20240220","Product":"x","Asset Unit":"y","Fund Account":"Securities Liquidation Fund at Location A","Change":"-500"}. The value atom information can be {"Date":"20240220","Product":"x","Asset Unit":"y","Fund Account":"Undelivered Freeze","Change":"500"}. The value atom information can be {"Date":"20240220","Product":"x","Asset Unit":"y","Fund Account":"Purchase Amount","Change":"500"}.

[0035] For another example, three pieces of atomic information about security value:

[0036] The value atom information can be {"date":"20240220","product":"x","asset unit":"y","securities account":"purchase quantity","change":"100"}. The value atom information can be {"date":"20240220","product":"x","asset unit":"y","securities account":"purchase amount","change":"500"}. The value atom information can be {"date":"20240220","product":"x","asset unit":"y","securities account":"cost","change":"500"}.

[0037] As an example, atomic pipeline processing (i.e., the generation of value atomic information) has been pre-written to obtain CUDA kernel functions. One business category corresponds to one kernel function, and the kernel function can be called by category to process the clearing pipeline. Since the processing logic of unified business categories is basically the same, there are few conditional branches in the kernel function, and the GPU can exert all its computing power for parallel processing.

[0038] In practice, before processing the value atomic information at the target granularity, the pairing operation and fee calculation can be performed first. Specifically, the pairing operation can be: matching the flow in the liquidation file with the daily transaction to determine which sub-account (asset unit) the transaction should be cleared to. The fee calculation can be performed after the fee configuration is taken according to the business category and product (although the fee is given in the liquidation file, the actual production will still calculate the fee by itself, and then the calculated fee will be checked with the fee provided by the registration company).

[0039] In some optional implementations of some embodiments, the value flow cleaning information is processed into value atom information at a target granularity according to the value flow business category set to obtain a value atom information set, which may include the following steps:

[0040] In the first step, for each value transfer business category in the value transfer business category set, a business data group corresponding to the value transfer business category is screened out from the value transfer cleanup information. The business data group corresponding to the value transfer business category may be a data group that has a business association relationship with the value transfer business category.

[0041] In the second step, the pre-deployed graphics processing unit (GPU) is used to decompose the information of each business data in the obtained business data set at the target granularity to generate the above-mentioned value atomic information set.

[0042] As an example, the execution entity may utilize a pre-deployed image processor set to perform information decomposition at a target granularity on each business data in the obtained business data set in parallel to generate the value atom information set.

[0043] Step 105, allocating each value atom information in the above value atom information set to a corresponding computing node.

[0044] In some embodiments, the execution subject may distribute each value atom information in the value atom information set to a corresponding computing node, wherein the computing node may have at least one pre-deployed graphics processor in the graphics processor set.

[0045] As an example, the execution entity may allocate each value atom information in the value atom information set to a corresponding computing node through a greedy algorithm.

[0046] In some optional implementations of some embodiments, the above-mentioned allocating each value atom information in the above-mentioned value atom information set to the corresponding computing node may include the following steps:

[0047] The first step is to obtain the value processing method information corresponding to the above-mentioned value atom information set. The value processing method information can represent the method of information processing on the value atom information. The value processing method information can include: offline processing method and online processing method. The offline processing method can be a method of information processing using offline resources. The online processing method can be a method of information processing using online resources. That is, in practice, computing resources can be allocated as online resources and offline resources. Online resources can be resources for implementing online task execution. Offline resources can be resources for implementing offline task execution.

[0048] In the second step, in response to determining that the above value processing mode information is an offline processing mode, the offline resource information corresponding to each computing node in the above computing nodes is determined to obtain each offline resource information.

[0049] The third step is to determine the resource consumption interval corresponding to the value atom information, wherein the resource consumption interval can be pre-set according to the byte size corresponding to the value atom information.

[0050] The fourth step is to generate a total resource consumption interval corresponding to the above value atom information set according to the above resource consumption interval, wherein the total resource consumption interval can represent the amount of resources required by the value atom information set for information calculation.

[0051] As an example, the execution subject may multiply the minimum value in the resource consumption interval by the number of value atom information corresponding to the value atom information set to generate the minimum value of the total resource consumption interval. The maximum value in the resource consumption interval may be multiplied by the number of value atom information corresponding to the value atom information set to generate the maximum value of the total resource consumption interval. The total resource consumption interval is generated according to the minimum value of the total resource consumption interval and the maximum value of the total resource consumption interval.

[0052] The fifth step, in response to determining that the maximum value in the above-mentioned total resource consumption interval is less than or equal to the total offline resource amount corresponding to the above-mentioned each offline resource information, according to the above-mentioned resource consumption interval and the above-mentioned each offline resource information, each value atom information in the above-mentioned value atom information set is allocated to the corresponding computing node for offline processing by the above-mentioned each computing node.

[0053] As an example, the execution subject may determine the allocation information of the value atom information set on each computing node using a greedy algorithm based on the resource consumption interval and each offline resource information, and execute the allocation of the corresponding value atom information based on the allocation information for each computing node to process offline.

[0054] In step 6, in response to determining that the minimum value in the total resource consumption interval is greater than the total offline resource amount corresponding to each offline resource information, determine the online available resource information corresponding to each computing node, and obtain each online available resource information. The online available resources are resources that are not used by online tasks within the future scheduled time. The future scheduled time can be within the next 4 hours.

[0055] The seventh step is to determine the total online available resource amount corresponding to each of the above online available resource information.

[0056] As an example, the execution entity may add up each piece of online available resource information to generate a total online available resource amount.

[0057] In the eighth step, a first total resource amount between the total online available resource amount and the total offline resource amount is determined.

[0058] As an example, the execution entity may add the total online available resource amount to the total offline resource amount to generate a first total resource amount.

[0059] The ninth step, in response to determining that the maximum value in the above-mentioned total resource consumption interval is less than or equal to the above-mentioned first total resource amount, according to the above-mentioned resource consumption interval, the above-mentioned each offline resource information and the above-mentioned each online available resource information, each value atom information in the above-mentioned value atom information set is allocated to the corresponding computing node for offline processing by each of the above-mentioned computing nodes.

[0060] As an example, the execution subject may determine the allocation information of the value atom information set on each computing node using a greedy algorithm based on the resource consumption interval, each offline resource information, and each online available resource information. According to the allocation information, the corresponding value atom information is allocated for each computing node to be processed offline.

[0061] In step 10, in response to determining that the minimum value in the total resource consumption interval is greater than the first total resource amount, the value atom information set is divided according to the first total resource amount to generate a first value atom information group set, wherein the maximum value of the total resource consumption interval corresponding to each first value atom information group is less than or equal to the first total resource amount.

[0062] As an example, the above-mentioned execution entity can divide the above-mentioned value atomic information set with the first total resource amount as the dividing point, so that the total resource consumption interval corresponding to each first value atomic information group after the division is less than or equal to the first total resource amount, and obtain the first value atomic information group set.

[0063] The eleventh step is to batch distribute the value atom information groups in the first value atom information group set to corresponding computing nodes according to the resource consumption interval, the offline resource information and the online available resource information, so as to provide the computing nodes with offline processing.

[0064] As an example, for each batch of first value atomic information groups, the execution subject may determine the allocation information of the first value atomic information group on each computing node using a greedy algorithm based on the resource consumption interval, each offline resource information, and each online available resource information. According to the allocation information, the corresponding value atomic information is allocated for each computing node to be processed offline.

[0065] In some optional implementations of some embodiments, the above-mentioned allocating each value atom information in the above-mentioned value atom information set to the corresponding computing node further includes:

[0066] The first step is to determine the online processing mode information in response to the above value processing mode information being determined as the online processing mode, determine the online resource information corresponding to each of the above computing nodes, and obtain each online resource information.

[0067] In the second step, in response to determining that the maximum value in the above-mentioned total resource consumption interval is less than or equal to the total online resource amount corresponding to the above-mentioned each online resource information, according to the above-mentioned resource consumption interval and the above-mentioned each online resource information, each value atom information in the above-mentioned value atom information set is allocated to the corresponding computing node for online processing by the above-mentioned each computing node.

[0068] As an example, the execution subject may determine the allocation information of the value atom information set on each computing node using a greedy algorithm based on the resource consumption interval and each online resource information, and allocate the corresponding value atom information based on the allocation information for each computing node to process online.

[0069] In the third step, in response to determining that the minimum value in the above-mentioned total resource consumption interval is greater than the total online resource amount corresponding to the above-mentioned online resource information, the available offline resources corresponding to the above-mentioned computing nodes are determined. Among them, the task level corresponding to the offline execution task corresponding to the available offline resources is less than the allocation task level corresponding to the above-mentioned each value atom information. Available offline resources can be offline resources that are currently available. Available offline resources may be executing offline tasks or may not be executing offline tasks. The task level represents the importance of the task and also represents the execution order of the task.

[0070] The fourth step is to determine the total offline available resource amount corresponding to each of the above available offline resources.

[0071] As an example, the execution entity may add up the available offline resources to generate the total amount of offline available resources.

[0072] The fifth step is to determine a second total resource amount between the above-mentioned online resource information and the above-mentioned total offline available resource amount.

[0073] As an example, the execution entity may add the above-mentioned individual online resource information to the above-mentioned total offline available resource amount to generate a second total resource amount.

[0074] The sixth step, in response to determining that the maximum value in the above-mentioned total resource consumption interval is less than or equal to the above-mentioned second total resource amount, allocates each value atom information in the above-mentioned value atom information set to a corresponding computing node according to the above-mentioned resource consumption interval, the above-mentioned each online resource information and the above-mentioned each available offline resource, so that the above-mentioned each computing node can process it online.

[0075] As an example, the execution subject may determine the allocation information of the value atom information set on each computing node using a greedy algorithm based on the resource consumption interval, each online resource information, and each available offline resource. According to the allocation information, the corresponding value atom information is allocated for online processing by each computing node.

[0076] Step 7: In response to determining that the minimum value in the total resource consumption interval is greater than the second total resource amount, the value atom information set is divided according to the second total resource amount to generate a second value atom information group set. The maximum value of the total resource consumption interval corresponding to the second value atom information group is less than or equal to the second total resource amount. For specific implementation methods, refer to the generation of the first value atom information group set.

[0077] The eighth step is to batch distribute the value atom information groups in the second value atom information group set to corresponding computing nodes according to the resource consumption interval, the online resource information and the available offline resources, so as to be processed online by the computing nodes.

[0078] As an example, for each batch of second value atomic information groups, the execution subject may determine the allocation information of the second value atomic information groups on each computing node using a greedy algorithm based on the resource consumption interval, each online resource information, and each available offline resource. According to the allocation information, the corresponding value atomic information is allocated for online processing by each computing node.

[0079] Step 106, based on the above initial value account dictionary, using the pre-deployed graphics processor set, update the change amount at the value account granularity of the value atom information subset corresponding to each computing node in parallel to generate a value account change dictionary.

[0080] In some embodiments, the execution subject can use the pre-deployed graphics processor set to update the change amount at the value account granularity of the value atom information subsets corresponding to each computing node in parallel according to the initial value account dictionary to generate a value account change dictionary. Each computing node has a corresponding value atom information subset. That is, the processing logic corresponding to the value atom information subset is executed on the corresponding computing node. The value account change dictionary can be a dictionary after the value account change information has been added. That is, a dictionary with added value content as the change information of the value account.

[0081] In some optional implementations of some embodiments, based on the initial value account dictionary, using a pre-deployed graphics processor set, updating the change amount at the value account granularity of the value atom information subset corresponding to each computing node in parallel to generate a value account change dictionary may include the following steps:

[0082] In the first step, for each of the above computing nodes, the following first generation step is performed:

[0083] Sub-step 1, determine the value atom information subset corresponding to the above-mentioned computing node as the target value atom information subset.

[0084] Sub-step 2, using the computing resources corresponding to the computing node, determine the value subject set corresponding to the target value atom information subset. The computing resources corresponding to the computing node are provided by at least one graphics processor in the graphics processor set. The value subject set can be each value subject involved in the target value atom information subset.

[0085] Sub-step 3, based on the above value account set, update the value account of each value account in the above initial value account dictionary to generate an updated value account dictionary.

[0086] As an example, first, each initial value account in the initial value account dictionary is determined. Then, at least one value account that is not in each initial value account and is in the value account set is determined. Finally, the at least one value account is added to the initial value account dictionary to generate an updated value account dictionary.

[0087] Sub-step 4, for each value account in the above value account set, perform the following second generation step:

[0088] The first sub-step is to obtain the value processing kernel function corresponding to the above value items. The value processing kernel function corresponding to each value item can be preset. The value processing kernel function represents the calculation logic of how to determine the value change of each value atomic information corresponding to the value item.

[0089] The second sub-step is to determine at least one target value atomic information corresponding to the above value subject.

[0090] The third sub-step is to use the computing resources corresponding to the above-mentioned computing nodes and the above-mentioned value processing kernel function to determine the value change of the above-mentioned at least one target value atomic information to generate the value change information corresponding to the above-mentioned value items.

[0091] Sub-step 5, adding value change information to the updated value account dictionary based on the value account set and the corresponding value change information set to generate an added value account dictionary.

[0092] As an example, the execution entity may add the value account set and the value change information set to the updated value account dictionary in the form of key-value pairs to generate the added value account dictionary.

[0093] In the second step, the change information of the value account granularity of each added value account dictionary is summarized to generate a summarized value account dictionary as the above-mentioned value account change dictionary.

[0094] Step 107, performing information change verification on the change information under each value account in the above value account change dictionary to generate a verification result.

[0095] In some embodiments, the execution subject may perform information change verification on the change information under each value subject in the value subject change dictionary to generate a verification result, wherein the verification result may indicate whether there are errors in the various information in the value subject change dictionary.

[0096] As an example, first, the execution subject may randomly extract at least one value account change information corresponding to at least one value account from the value account change dictionary. Then, determine whether there is an error in the at least one value account change information. Finally, in response to determining that there is an error in the at least one value account change information, generate a result indicating that there is an error in various information in the value account change dictionary. In response to determining that there is no error in the at least one value account change information, generate a result indicating that there is no error in various information in the value account change dictionary.

[0097] Step 108, in response to determining that the verification result indicates that the value account change dictionary is correct, the value account change dictionary is stored in the target database.

[0098] In some embodiments, in response to determining that the verification result indicates that the value account change dictionary is correct, the execution entity may store the value account change dictionary in a target database.

[0099] In some optional implementations of some embodiments, the above-mentioned information change verification of the change information under each value account in the above-mentioned value account change dictionary to generate a verification result may include the following steps:

[0100] The first step is to use a pre-trained natural language model to find historical problem value items from the database. The natural language model can be a Transformer model. The historical problem value items can be value items that often have statistical problems of changes in the historical time period.

[0101] The second step is to obtain the change information corresponding to the above-mentioned historical problem value subjects from the above-mentioned value subject change dictionary as the target change information.

[0102] The third step is to send the above change information to the operation terminal used by the target detection personnel, so that the above target detection personnel can detect whether there is a problem between the above historical problem value items and the above target change information.

[0103] In some optional implementations of some embodiments, the above-mentioned information change verification of the change information under each value account in the above-mentioned value account change dictionary to generate a verification result may include the following steps:

[0104] In the first step, for each of the above value accounts, the following third generation step is performed:

[0105] Sub-step 1, obtaining the preset subject keyword group corresponding to the above-mentioned value subject. The preset subject keyword group may be a keyword group pre-set for the value subject. The preset subject keyword group may be various keywords closely related to the value subject.

[0106] Sub-step 2, segment the above-mentioned value flow cleaning information to generate a word graph set, wherein each word graph has corresponding value flow cleaning sub-information, each node in the word graph is a word, and the edge in the word graph represents the relationship between words.

[0107] Sub-step 3, determining the keyword map between the keywords in the above-mentioned preset subject keyword group.

[0108] Sub-step 4, determining from the above word graph set a word graph whose similarity to the above keyword graph is greater than a target value, and obtaining at least one word graph. The target value may be a preset value.

[0109] Sub-step 5: determining the subject keyword content group corresponding to each word graph in the at least one word graph, wherein the subject keyword content may be the associated content corresponding to the subject keyword.

[0110] Sub-step 6: for each subject keyword content group, determine the content positioning information corresponding to each subject keyword content in the subject keyword content group to obtain a content positioning information group, wherein the content positioning information may be the positioning information of the subject keyword content in the value circulation cleaning information.

[0111] Sub-step 7, generating a value subject information sub-table in a predetermined format according to the obtained at least one subject keyword content group, the preset subject keyword group and the corresponding at least one content positioning information group.

[0112] As an example, the execution entity may add at least one subject keyword content group, a preset subject keyword group and a corresponding at least one content positioning information group to an empty table in a predetermined format to generate a value subject information sub-table.

[0113] The second step is to summarize the obtained value account information sub-tables to generate a value account information table.

[0114] The third step is to perform information change verification on the change information under each value account in the above value account change dictionary according to the above value account information table to generate a verification result.

[0115] As an example, the execution subject may determine the information similarity between each value account information in the value account information table and the change information under each value account in the value account change dictionary to generate information similarity. In response to determining that the information similarity is greater than or equal to the target similarity, information indicating that the verification has passed is generated. In response to determining that the information similarity is less than the target similarity, information indicating that the verification has not passed is generated.

[0116] In some optional implementations of some embodiments, after step 108, the steps further include:

[0117] In the first step, in response to determining that the verification result indicates that the value account change dictionary is incorrect, it is determined that there is at least one value account question information. The value account question information may be information that the corresponding value account or the corresponding value change information may have a verification problem. The value account question information may include but is not limited to at least one of the following: a value account identifier and a value change information identifier.

[0118] The second step is to determine at least one content positioning information corresponding to the at least one value subject question information, wherein the value subject question information in the at least one value subject question information and the content positioning information in the at least one content positioning information have a one-to-one correspondence.

[0119] The third step is to use the pre-deployed CPU resources to process the query information of at least one valuable subject according to the at least one content positioning information to obtain at least one processing result.

[0120] As an example, the above-mentioned execution subject can determine at least one value subject content corresponding to at least one content positioning information based on the pre-deployed CPU resources. Then, based on the at least one value subject content, determine whether there is a problem with each value subject question information in the at least one value subject question information. Finally, in response to determining that none of them exist, generate a processing result representing that the value subject question information is untrue, and obtain at least one processing result. In response to determining that they exist, determine the value subject information with problems as the problem value subject information, and obtain a problem value subject information set. Determine the content positioning information set corresponding to the problem value subject information set. According to the content positioning information set, adjust the value subject change information set corresponding to the problem value subject information set to generate an adjusted value subject change information set. Generate at least one processing result representing that the problem value subject information set is adjusted with value subject change information to be adjusted into the adjusted value subject change information set.

[0121] The fourth step is to generate a processed value account change dictionary based on the at least one processing result and the value account change dictionary.

[0122] As an example, in response to determining that there is no problem with the at least one processing result representation, the value account change dictionary is determined as a processed value account change dictionary. In response to determining that there is no problem with the at least one processing result representation, the value account change dictionary is adjusted according to the adjusted value account change information set to generate a processed value account change dictionary.

[0123] The fifth step is to store the processed value account change dictionary as the value account change dictionary in the target database.

[0124] In some optional implementations of some embodiments, after step 108, the steps further include:

[0125] In the first step, in response to receiving the change retrieval information for the target value account, the value account change information and the value account information sub-table corresponding to the target value account are screened out from the stored value account change dictionary. The target value account may be a value account in the value account set corresponding to the value account change dictionary. The change retrieval information may be a request to obtain the value change information corresponding to the target value account.

[0126] The second step is to send the above value account change information and the above value account information sub-table to the target terminal, wherein the target terminal may be the terminal that sends the above change retrieval information.

[0127] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the value subject dictionary generation method of some embodiments of the present disclosure, the information processing corresponding to the value flow cleaning information can be accurately and efficiently realized under the premise of avoiding the waste of computing resources. Specifically, in the process of information processing corresponding to the value flow cleaning information, the reason for the waste of relevant computing resources is that with the expansion of business scale and the application of technologies such as quantitative trading, the demand for computing power for value processing operations is increasing, and the computing power of the CPU alone may not be able to meet the production needs. And parallel processing in units of products (if there are multiple value processing operations for the same product in parallel, it will cause read-write conflicts, and the processing logic and time consumption will increase), resulting in a waste of computing resources in the CPU. Based on this, the value subject dictionary generation method of some embodiments of the present disclosure, first, obtains the target value institution registration file from the target database as the data source file of the target value institution, so as to facilitate the subsequent generation of full and accurate value flow cleaning information. Then, according to the above-mentioned target value institution registration file, the value flow cleaning information in the target format is accurately generated to facilitate the value processing of the value flow cleaning information. Next, obtain the preset value flow business category set and the initial value account dictionary, so as to process the value flow cleaning information from the perspective of the value flow business category. Then, according to the above value flow business category set, the above value flow cleaning information is processed into value atom information at the target granularity to obtain the value atom information set, so as to perform task decomposition of the information processing task of the value flow cleaning information at the granularity of the value atom information, so as to facilitate the subsequent allocation of corresponding computing resources and avoid waste of computing resources. Secondly, each value atom information in the above value atom information set is allocated to the corresponding computing node, so as to facilitate the efficient processing of subsequent information processing tasks. Furthermore, according to the above initial value account dictionary, the pre-deployed graphics processor set is used to update the change amount at the value account granularity of the corresponding value atom information subset of each computing node in parallel, so as to accurately generate the value account change dictionary. Here, by using the image processor set, parallel processing and updating the change amount at the value account granularity, the value account change dictionary representing the corresponding change of each value account can be accurately and meticulously generated. Further, the change information under each value subject in the value subject change dictionary is verified to generate a verification result to ensure the accuracy of the information in the value subject change dictionary. Finally, in response to determining that the verification result indicates that the value subject change dictionary is correct, the value subject change dictionary is stored in the target database.In summary, by processing the value flow clearing information into value atomic information at the target granularity, utilizing a graphics processor set, parallel processing, and updating the change amount at the value account granularity, a value account change dictionary representing the corresponding changes of each value account can be accurately and meticulously generated, so as to achieve efficient information processing of value flow clearing information and avoid waste of computing resources.

[0128] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a value subject dictionary generation device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the value subject dictionary generating device can be specifically applied to various electronic devices.

[0129] like Figure 2 As shown, a value subject dictionary generation device 200 includes: a first acquisition unit 201, a generation unit 202, a second acquisition unit 203, a processing unit 204, an allocation unit 205, an update unit 206, a verification unit 207 and a storage unit 208. Among them, the first acquisition unit 201 is configured to acquire a target value institution registration file from a target database; the generation unit 202 is configured to generate value flow cleanup information in a target format according to the target value institution registration file; the second acquisition unit 203 is configured to acquire a preset value flow business category set and an initial value subject dictionary; the processing unit 204 is configured to process the value flow cleanup information into value atom information at a target granularity according to the value flow business category set, and obtain a value atom information set; the allocation unit 205 is configured to allocate each value atom in the value atom information set to a target value atom information set; The information is distributed to the corresponding computing nodes; the updating unit 206 is configured to update the change amount at the value account granularity of the value atomic information subsets corresponding to each computing node in parallel according to the above-mentioned initial value account dictionary and using the pre-deployed graphics processor set to generate a value account change dictionary; the verification unit 207 is configured to verify the change information under each value account in the above-mentioned value account change dictionary to generate a verification result; the storage unit 208 is configured to store the above-mentioned value account change dictionary in the target database in response to determining that the above-mentioned verification result indicates that the above-mentioned value account change dictionary is correct.

[0130] It is understandable that the units recorded in the value subject dictionary generating device 200 and the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the value subject dictionary generation device 200 and the units included therein, and will not be described in detail here.

[0131] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0132] like Figure 3 As shown, the electronic device 300 may include a processing device (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 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0133] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0134] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0135] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0136] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0137] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the target value institution registration file from the target database; generates value flow cleaning information in the target format according to the target value institution registration file; obtains a preset value flow business category set and an initial value account dictionary; processes the value flow cleaning information into value atom information at the target granularity according to the value flow business category set to obtain a value atom information set; distributes each value atom information in the value atom information set to a corresponding computing node; according to the initial value account dictionary, uses a pre-deployed graphics processor set to update the change amount at the value account granularity of the value atom information subset corresponding to each computing node in parallel to generate a value account change dictionary; performs information change verification on the change information under each value account in the value account change dictionary to generate a verification result; in response to determining that the verification result indicates that the value account change dictionary is correct, the value account change dictionary is stored in the target database.

[0138] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0140] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a first acquisition unit, a generation unit, a second acquisition unit, a processing unit, an allocation unit, an update unit, a verification unit and a storage unit. The names of these units do not constitute a limitation on the units themselves in some cases. For example, the first acquisition unit may also be described as "a unit for acquiring a target value institution registration file from a target database".

[0141] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0142] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for generating a value subject dictionary, comprising: Obtain target value institution registration documents from target database; Generate value circulation cleanup information in the target format according to the target value institution registration file; Obtain the preset value flow business category set and initial value account dictionary; According to the value flow business category set, the value flow cleaning information is processed into value atom information at a target granularity to obtain a value atom information set; Allocating each value atom information in the value atom information set to a corresponding computing node; According to the initial value account dictionary, using the pre-deployed graphics processor set, the value atom information subsets corresponding to each computing node are updated in parallel at the value account granularity to generate a value account change dictionary, wherein: According to the initial value account dictionary, using the pre-deployed graphics processor set, updating the change amount at the value account granularity of the value atom information subset corresponding to each computing node in parallel to generate a value account change dictionary includes: For each of the computing nodes, the following first generation step is performed: Determine the value atom information subset corresponding to the computing node as the target value atom information subset; Determine the value subject set corresponding to the target value atom information subset by using the computing resources corresponding to the computing node, wherein the computing resources corresponding to the computing node are provided by at least one graphics processor in the graphics processor set; According to the value account set, updating the value account of each value account in the initial value account dictionary to generate an updated value account dictionary; For each value account in the value account set, the following second generation step is performed: Obtaining a value processing kernel function corresponding to the value subject; Determine at least one target value atom information corresponding to the value subject; Using the computing resources corresponding to the computing node and the value processing kernel function, determine the value change of the at least one target value atomic information to generate the value change information corresponding to the value account; According to the value account set and the corresponding value change information set, adding the value change information to the updated value account dictionary to generate an added value account dictionary; Summarizing the change information of the value account granularity of each added value account dictionary obtained to generate a summarized value account dictionary as the value account change dictionary; Performing information change verification on the change information under each value account in the value account change dictionary to generate a verification result; In response to determining that the verification result indicates that the value account change dictionary is correct, the value account change dictionary is stored in a target database.

2. The method according to claim 1, wherein: According to the value flow business category set, the value flow cleaning information is processed into value atomic information at a target granularity to obtain a value atomic information set, including: For each value flow business category in the value flow business category set, filtering out a business data group corresponding to the value flow business category from the value flow cleaning information; By using the pre-deployed image processor set, each business data in the obtained business data set is decomposed at a target granularity to generate the value atom information set.

3. The method according to claim 1, wherein: The allocating each value atom information in the value atom information set to a corresponding computing node includes: Obtaining value processing method information corresponding to the value atom information set; In response to determining that the value processing mode information is an offline processing mode, determining offline resource information corresponding to each computing node in the computing nodes to obtain each offline resource information; Determine the resource consumption interval corresponding to the value atom information; According to the resource consumption interval, generating a total resource consumption interval corresponding to the value atom information set; In response to determining that the maximum value in the total resource consumption interval is less than or equal to the total offline resource amount corresponding to each offline resource information, allocating each value atom information in the value atom information set to a corresponding computing node according to the resource consumption interval and each offline resource information, so that each computing node can process it offline; In response to determining that the minimum value in the total resource consumption interval is greater than the total offline resource amount corresponding to each offline resource information, determining the online available resource information corresponding to each computing node, and obtaining each online available resource information, wherein the online available resource is a resource that is not used by an online task within a predetermined time period in the future; Determine the total online available resource amount corresponding to each piece of online available resource information; Determine a first total amount of resources between the total amount of online available resources and the total amount of offline resources; In response to determining that the maximum value in the total resource consumption interval is less than or equal to the first total resource amount, allocating each value atom information in the value atom information set to a corresponding computing node according to the resource consumption interval, each offline resource information and each online available resource information, so that each computing node can process it offline; In response to determining that the minimum value in the resource consumption total amount interval is greater than the first total resource amount, dividing the value atom information set according to the first total resource amount to generate a first value atom information group set; According to the resource consumption interval, the offline resource information and the online available resource information, the first value atom information groups in the first value atom information group set are distributed in batches to corresponding computing nodes for offline processing by the computing nodes.

4. The method according to claim 3, wherein: The step of allocating each value atom information in the value atom information set to a corresponding computing node further includes: In response to determining that the value processing mode information is an online processing mode, determining the online resource information corresponding to each computing node in the computing nodes, and obtaining each online resource information; In response to determining that the maximum value in the total resource consumption interval is less than or equal to the total online resource amount corresponding to each online resource information, allocating each value atom information in the value atom information set to a corresponding computing node according to the resource consumption interval and each online resource information, so that each computing node performs online processing; In response to determining that the minimum value in the total resource consumption interval is greater than the total online resource amount corresponding to each online resource information, determining each available offline resource corresponding to each computing node, wherein the task level corresponding to the offline execution task corresponding to the available offline resource is less than the allocation task level corresponding to each value atom information; Determine the total offline available resource amount corresponding to each of the available offline resources; Determine a second total resource amount between each online resource information and the total offline available resource amount; In response to determining that the maximum value in the total resource consumption interval is less than or equal to the second total resource amount, allocating each value atom information in the value atom information set to a corresponding computing node according to the resource consumption interval, each online resource information and each available offline resource, so that each computing node performs online processing; In response to determining that the minimum value in the resource consumption total amount interval is greater than the second total resource amount, dividing the value atom information set according to the second total resource amount to generate a second value atom information group set; According to the resource consumption interval, the various online resource information and the various available offline resources, the various second value atomic information groups in the second value atomic information group set are distributed in batches to corresponding computing nodes for online processing by the various computing nodes.

5. The method according to claim 1, wherein: The performing information change verification on the change information under each value subject in the value subject change dictionary to generate a verification result includes: Using pre-trained natural language models, find historical question value subjects from the database; Acquire the change information corresponding to the historical problem value subject from the value subject change dictionary as the target change information; The change information is sent to an operation terminal used by a target detection personnel so that the target detection personnel can detect whether there is a problem between the historical problem value account and the target change information.

6. The method according to claim 1, wherein: The method further comprises: In response to receiving the change retrieval information for the target value account, filtering out the value account change information and the value account information sub-table corresponding to the target value account from the stored value account change amount dictionary; The value subject change information and the value subject information sub-table are sent to a target terminal.

7. A value subject dictionary generating device, comprising: A first acquisition unit is configured to acquire a target value institution registration file from a target database; A generating unit configured to generate value circulation clearing information in a target format according to the target value institution registration file; A second acquisition unit is configured to acquire a preset value transfer business category set and an initial value account dictionary; A processing unit is configured to process the value flow cleaning information into value atom information at a target granularity according to the value flow business category set, thereby obtaining a value atom information set; A distribution unit, configured to distribute each value atom information in the value atom information set to a corresponding computing node; The updating unit is configured to update the value atom information subsets corresponding to each computing node in parallel according to the initial value account dictionary and using the pre-deployed graphics processor set to update the value atom information subsets corresponding to each computing node in parallel according to the value account granularity to generate a value account change dictionary, wherein the updating of the value atom information subsets corresponding to each computing node in parallel according to the initial value account dictionary and using the pre-deployed graphics processor set to generate the value account change dictionary includes: for each of the computing nodes, executing the following first generation step: determining the value atom information subset corresponding to the computing node as the target value atom information subset; determining the value account set corresponding to the target value atom information subset using the computing resources corresponding to the computing node, wherein the computing resources corresponding to the computing node are provided by at least one graphics processor in the graphics processor set; According to the value account set, each value account in the initial value account dictionary is updated to generate an updated value account dictionary; for each value account in the value account set, the following second generation step is performed: obtaining the value processing kernel function corresponding to the value account; determining at least one target value atom information corresponding to the value account; using the computing resources corresponding to the computing node and the value processing kernel function, determining the value change of the at least one target value atom information to generate the value change information corresponding to the value account; according to the value account set and the corresponding value change information set, adding the value change information to the updated value account dictionary to generate an added value account dictionary; summarizing the change information of the value account granularity of each added value account dictionary to generate a summarized value account dictionary as the value account change dictionary; A verification unit configured to perform information change verification on the change information under each value account in the value account change dictionary to generate a verification result; The storage unit is configured to store the value account change dictionary in a target database in response to determining that the verification result indicates that the value account change dictionary is correct.

8. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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