Value-related information storage method, device and equipment and computer readable medium

Abstract

The embodiment of the invention discloses a value-related information storage method, device and equipment and a computer readable medium. A specific embodiment of the method comprises the steps of obtaining a historical subject value processing data set and at least one value index; extracting at least one historical value index data sequence; classifying the historical index data at the same time; for each computing node, determining residual available computing resources and a node calling sequence; determining computing node information corresponding to each historical value index data group; for each historical time, executing a first generation step: determining a historical value index data set; calling at least one graphics processor to generate an index importance degree information group; calling a central processing unit to generate a current index importance degree information group; generating current value voucher information; and executing corresponding value voucher information storage. According to the embodiment, the value voucher information can be efficiently and accurately generated under the condition that online computing resources are fully utilized.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a value-related information storage method, apparatus, device, and computer-readable medium. Background Art

[0002] At present, with the continuous development of society, users and enterprises are becoming more and more extensive. The value credentials (e.g., credit) of users and enterprises are crucial. For the determination of the value credentials of users or enterprises, the usual method is: first, obtain the historical value data sequence of the user or enterprise. Then, using the pre-deployed image processor, the historical value data sequence is directly input into the value credential information generation model (e.g., a recurrent neural network model) to generate value credential information.

[0003] However, when using the above method to generate value voucher information, the following technical problems often occur:

[0004] When the data volume corresponding to the historical value data sequence is large, the image processor is used to generate online information, which often causes the processor to process data under great pressure and run under high load for a long time. When the temperature exceeds the predetermined threshold (for example, 95 degrees), downtime is likely to occur. This leads to generation breakpoints in the process of generating value voucher information, which often results in the generated value voucher information being inefficient and inaccurate. In addition, using the historical value data sequence as the model input results in the generated value voucher information being inaccurate.

[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 value-related information storage methods, devices, equipment and computer-readable media to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In the first aspect, some embodiments of the present disclosure provide a method for storing value-related information, including: obtaining a historical subject value processing data set corresponding to a target subject and at least one value indicator related to value credential information; extracting at least one historical value indicator data sequence corresponding to the at least one value indicator from the above historical subject value processing data set; classifying each historical indicator data in the above at least one historical value indicator data sequence in the same time to generate a historical value indicator data group sequence, wherein each historical value indicator data in each historical value indicator data group corresponds to the same historical time; for each computing node in the current computing node cluster, determining the remaining available computing resources of at least one graphics processor deployed corresponding to the above computing node and the node calling order corresponding to the above computing node; according to the number of groups corresponding to the historical value indicator data group and the data byte size corresponding to the historical value indicator data group , the remaining available computing resources corresponding to each computing node and the node calling order, determine the computing node information corresponding to each historical value indicator data group in the above-mentioned historical value indicator data group sequence; for each historical time in the historical time sequence, execute the first generation step: determine the historical value indicator data group corresponding to the above-mentioned historical time as the target historical value indicator data group; call at least one graphics processor in the computing node corresponding to the above-mentioned target historical value indicator data group to generate the corresponding indicator importance information group; according to the obtained indicator importance information group sequence, call the central processing unit to generate the current indicator importance information group for the above-mentioned target subject; according to the above-mentioned current indicator importance information group and the obtained current subject value processing data set, generate the current value voucher information; store the above-mentioned current value voucher information and the subject information corresponding to the above-mentioned target subject in the form of an encrypted key-value pair.

[0009] In a second aspect, some embodiments of the present disclosure provide a value-related information storage device, including: an acquisition unit, configured to acquire a historical subject value processing data set corresponding to a target subject and at least one value indicator related to value credential information; an extraction unit, configured to extract at least one historical value indicator data sequence corresponding to the at least one value indicator from the above historical subject value processing data set; a classification unit, configured to classify each historical indicator data in the above at least one historical value indicator data sequence at the same time to generate a historical value indicator data group sequence, wherein each historical value indicator data in each historical value indicator data group corresponds to the same historical time; a first determination unit, configured to determine, for each computing node in the current computing node cluster, the remaining available computing resources of at least one graphics processor deployed corresponding to the above computing node and the node call order corresponding to the above computing node; a second determination unit, configured to determine the number of groups corresponding to the historical value indicator data group, ... The computing node information corresponding to each historical value indicator data group in the above historical value indicator data group sequence is determined based on the data byte size, the remaining available computing resources corresponding to each computing node, and the node calling order; the execution unit is configured to execute the first generation step for each historical time in the historical time sequence: determine the historical value indicator data group corresponding to the above historical time as the target historical value indicator data group; call at least one graphics processor in the computing node corresponding to the above target historical value indicator data group to generate the corresponding indicator importance information group; the calling unit is configured to call the central processing unit according to the obtained indicator importance information group sequence to generate the current indicator importance information group for the above target subject; the generating unit is configured to generate the current value voucher information according to the above current indicator importance information group and the obtained current subject value processing data set; the storage unit is configured to store the above current value voucher information and the subject information corresponding to the above target subject in the form of an encrypted key-value pair.

[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-related information storage method of some embodiments of the present disclosure, the value voucher information can be efficiently and accurately generated while fully utilizing online computing resources. Specifically, the reason why the relevant value voucher information is not accurate and efficient is that: when the data volume corresponding to the historical value data sequence is large, the image processor is used to generate online information, which often causes the processor to have a large data processing pressure and run under a high load state for a long time. When the temperature exceeds a predetermined threshold (for example, 95 degrees), it is easy to crash. The situation that a generation breakpoint occurs during the generation of the value voucher information often causes the generated value voucher information to be not efficient and accurate. In addition, using the historical value data sequence as a model input causes the generated value voucher information to be not accurate. Based on this, the value-related information storage method of some embodiments of the present disclosure first obtains the historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value voucher information, so as to obtain the data set corresponding to the value voucher information for the main analysis, and improves the accuracy and speed of online generation of value voucher information while being more representative and reducing the amount of data based on at least one value indicator. Then, at least one historical value indicator data sequence corresponding to the above-mentioned at least one value indicator is extracted from the above-mentioned historical subject value processing data set to facilitate the subsequent generation of value voucher information. Then, each historical indicator data in the above-mentioned at least one historical value indicator data sequence is classified at the same time to generate a historical value indicator data group sequence, wherein each historical value indicator data in each historical value indicator data group corresponds to the same historical time. Here, by classifying the historical indicator data at the same time, it is convenient to determine the importance of the indicator at each historical time in the future, so as to more accurately generate the importance of the indicator corresponding to each indicator from the time dimension. Then, for each computing node in the current computing node cluster, the remaining available computing resources of at least one graphics processor deployed corresponding to the above computing node and the node call order corresponding to the above computing node are determined. Here, by means of computing node clusters, and each computing node has multiple graphics processors deployed, in this way, in the form of a cluster, without affecting the work of other online work and by determining the remaining available computing resources and the node call order, the online available resources are reasonably arranged to avoid the graphics processor from being under long-term high load, so that the graphics processor crashes. Furthermore, based on the number of groups corresponding to the historical value indicator data groups, the data byte size corresponding to the historical value indicator data groups, the remaining available computing resources corresponding to each computing node and the node calling order, the computing node information corresponding to each historical value indicator data group in the above historical value indicator data group sequence is determined to achieve reasonable scheduling of resources, ensure full utilization of online resources, and greatly improve utilization efficiency.Secondly, for each historical time in the historical time series, the first generation step is performed: the first step is to determine the historical value indicator data group corresponding to the above historical time as the target historical value indicator data group, so as to facilitate the generation of indicator importance information. The second step is to call at least one graphics processor in the computing node corresponding to the above target historical value indicator data group to accurately generate the corresponding indicator importance information group. Furthermore, based on the obtained indicator importance information group sequence, the central processing unit is called to efficiently and accurately generate the current indicator importance information group for the above target subject. Therefore, based on the above current indicator importance information group and the obtained current subject value processing data set, the current value voucher information can be generated under the condition of an accurate current indicator importance information group. Finally, the above current value voucher information and the subject information corresponding to the above target subject are stored in the form of an encrypted key-value pair to facilitate the corresponding use of the current value voucher information and the subject information. 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-related information storage method according to the present disclosure;

[0015] Figure 2 is a schematic diagram of the structure of some embodiments of the value-related information storage 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 value-related information storage method according to the present disclosure. The value-related information storage method includes the following steps:

[0024] Step 101: Acquire a historical subject value processing data set corresponding to a target subject and at least one value indicator related to value voucher information.

[0025] In some embodiments, the execution subject (electronic device) of the above-mentioned value-related information storage method can obtain the historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value voucher information through a wired connection method or a wireless connection method. Among them, the target subject can be a subject for which value voucher information is to be generated. The value voucher information can be information that characterizes the value voucher. In practice, for the rating field, the corresponding value voucher information can be the subject credit. The historical subject value processing data set can be a subject value processing data set under a historical time period. The subject value processing data set can be a data set related to the corresponding value processing operation of the target subject. In practice, the value processing operation can include but is not limited to at least one of the following: subject debt repayment operation, subject borrowing operation, subject capital flow operation, and subject stock change operation. At least one value indicator can be a value indicator associated with the generation of value voucher information. In practice, at least one value indicator can be various indicators that are pre-set. In practice, at least one value indicator can include but is not limited to at least one of the following: subject borrowing trend indicator, subject repayment trend indicator, subject financial indicator, subject input product consumption indicator, subject scale indicator, and subject operating indicator.

[0026] Step 102: extract at least one historical value indicator data sequence corresponding to the at least one value indicator from the historical subject value processing data set.

[0027] In some embodiments, the execution subject may extract at least one historical value indicator data sequence corresponding to the at least one value indicator from the historical subject value processing data set. There is a one-to-one correspondence between the value indicator in the at least one value indicator and the historical value indicator data sequence in the at least one historical value indicator data sequence. The historical value indicator data sequence may be the value indicator data of the target subject at each corresponding historical time. That is, there is a one-to-one correspondence between the historical value indicator data in the historical value indicator data sequence and the historical time at each historical time.

[0028] As an example, the execution subject may utilize data extraction technology to extract at least one historical value indicator data sequence corresponding to the at least one value indicator from the historical subject value processing data set.

[0029] Step 103: classify the historical indicator data in the at least one historical value indicator data sequence according to time to generate a historical value indicator data group sequence.

[0030] In some embodiments, the execution subject may classify the historical indicator data in the at least one historical value indicator data sequence by time to generate a historical value indicator data group sequence, wherein the historical value indicator data in each historical value indicator data group have the same corresponding historical time.

[0031] Step 104 : for each computing node in the current computing node cluster, determine the remaining available computing resources of at least one graphics processor deployed corresponding to the computing node and the node calling order corresponding to the computing node.

[0032] In some embodiments, for each computing node in the current computing node cluster, the execution subject may determine the remaining available computing resources of at least one graphics processor deployed corresponding to the computing node and the node call order corresponding to the computing node. Among them, the computing node machine may include multiple computing nodes. The computing node may have at least one graphics processor (GPU) deployed correspondingly. At least one graphics processor may support the execution of online tasks and may also support the execution of offline tasks. That is, at least one graphics processor has online resources and offline resources that can be scheduled. Online resources are used for the execution of online tasks. Offline resources are used for the execution of offline tasks. The remaining available computing resources may be computing resources that at least one graphics processor can support the execution of online tasks. The node call order may be the order of use of each computing node. The computing resources corresponding to each computing node may be different, and the computing node cluster also supports the addition of computing nodes. The computing node supports the addition of graphics processors to increase the available computing resources. The computing nodes at the front of the node call order support priority in being called. The computing node may be a server for resource computing.

[0033] Here, by clustering computing nodes and setting the order of computing node calls, not only more computing resources are provided for online and offline tasks, but also the reasonable scheduling of computing resources can be dynamically realized to avoid long-term high load of each graphics processor in a single computing node. At the same time, it also ensures the accurate and efficient generation of subsequent current indicator importance information groups.

[0034] As an example, the execution subject may determine the remaining available computing resources corresponding to the computing nodes and the node calling order by means of computing resource query.

[0035] Step 105, determine the computing node information corresponding to each historical value indicator data group in the above historical value indicator data group sequence based on the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, the remaining available computing resources corresponding to each computing node, and the node calling order.

[0036] In some embodiments, the execution subject may determine the computing node information corresponding to each historical value indicator data group in the historical value indicator data group sequence according to the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, the remaining available computing resources corresponding to each computing node, and the node calling order. Among them, the number of groups corresponding to the historical value indicator data group may be the data volume of the historical value indicator data. The data byte size may be the storage byte size occupied by the historical value indicator data group. Specifically, the data byte size may be a value in bytes. The computing node corresponding to the historical value indicator data group may be the computing server corresponding to the historical value indicator data group for data processing.

[0037] In some optional implementations of some embodiments, the above-mentioned determining the computing node information corresponding to each historical value indicator data group in the above-mentioned historical value indicator data group sequence according to the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, the remaining available computing resources corresponding to each computing node, and the node calling order may include the following steps:

[0038] In the first step, the computing nodes are sorted according to the node calling sequence to generate a computing node information sequence. The computing node information may be a node identifier of a computing node. The computing node information in the computing node information sequence may be arranged in the order of resource priority processing.

[0039] The second step is to determine the target percentile data byte of the data byte size sequence corresponding to the above historical value indicator data group sequence. There is a one-to-one correspondence between the data byte size in the data byte size sequence and the historical value indicator data group in the historical value indicator data group sequence. The target percentile data byte can be the data byte whose size value of the corresponding data byte of the data byte size sequence is below the target percentile. For example, the target percentile position can be 1 / 3 size.

[0040] The third step is to remove the computing node information corresponding to the remaining available computing resources that are less than the above-mentioned target percentile data bytes from the above-mentioned computing node information sequence to obtain the computing node information sequence after removal.

[0041] The fourth step is to generate a sequence of remaining available computing resources for the computing node information sequence after the above removal.

[0042] The fifth step is to determine the data byte size sequence corresponding to the historical value indicator data group sequence, wherein there is a one-to-one correspondence between the historical value indicator data groups in the historical value indicator data group sequence and the data byte sizes in the data byte size sequence.

[0043] In the sixth step, the above-mentioned data byte size sequence and the above-mentioned remaining available computing resource sequence are input into the computing node scheduling model to generate computing node scheduling information. Among them, the above-mentioned computing node scheduling information represents the corresponding relationship between the data byte size subsequence and the remaining available computing resource subsequence, the sequence length corresponding to each data byte size subsequence is not uniform, and the total amount of data bytes corresponding to each data byte size sequence is less than the total amount of resources corresponding to the corresponding remaining available computing resource subsequence. The computing node scheduling model can be a model for node scheduling of computing nodes. The computing node scheduling information can represent information on how each computing node performs node scheduling. In practice, the computing node scheduling model can be a multi-layer fully connected layer connected in series.

[0044] The seventh step is to determine the computing node information corresponding to the above historical value indicator data group based on the above computing node scheduling information.

[0045] As an example, the execution subject may search for computing node information corresponding to the historical value indicator data group from computing node scheduling information.

[0046] Step 106, for each historical time in the historical time series, execute the first generation step:

[0047] Step 1061, determining the historical value indicator data group corresponding to the above historical time as the target historical value indicator data group.

[0048] In some embodiments, the execution entity may determine the historical value indicator data group corresponding to the historical time as the target historical value indicator data group.

[0049] As an example, the execution subject may determine the historical value indicator data group corresponding to the historical time as the target historical value indicator data group by means of data group query.

[0050] Step 1062: Call at least one graphics processor in the computing node corresponding to the target historical value indicator data group to generate a corresponding indicator importance information group.

[0051] In some embodiments, the execution subject may call at least one graphics processor in the computing node corresponding to the target historical value indicator data group to generate a corresponding indicator importance information group. There is a one-to-one correspondence between the indicator importance information in the indicator importance information group and the value indicator in at least one value indicator. The indicator importance information may be a value between 0 and 1. The higher the value, the more important the indicator content representing the value indicator at the corresponding historical time is relative to the value voucher information.

[0052] In some optional implementations of some embodiments, calling at least one graphics processor in a computing node corresponding to the target historical value indicator data group to generate a corresponding indicator importance information group may include the following steps:

[0053] Call at least one graphics processor in the computing node corresponding to the target historical value indicator data group to perform the following second generation step:

[0054] Sub-step 1, obtain at least one overall evaluation information of an indicator corresponding to the target subject at the above historical time, wherein the overall evaluation information of the indicator in the above at least one overall evaluation information of the indicator and the value indicator in the above at least one value indicator have a one-to-one correspondence. The overall evaluation information of the indicator can be the overall evaluation information of the indicator content at the historical time. The overall evaluation information of the indicator can be a numerical value between 0-100. The larger the numerical value, the more it represents the indicator tendency of the corresponding indicator content. For example, for the value indicator being the subject's borrowing trend indicator, the corresponding overall evaluation information of the indicator can be that the subject's borrowing amount is very large. For another example, for the value indicator being the subject's borrowing trend indicator, the corresponding overall evaluation information of the indicator can be the numerical value "80".

[0055] Sub-step 2: preprocessing each data in the target historical value indicator data group according to the overall evaluation information of the at least one indicator to obtain a preprocessed data group.

[0056] As an example, for each indicator overall evaluation information in at least one indicator overall evaluation information, first, determine the indicator data change information of the corresponding target historical value indicator data group. Then, determine whether the above indicator data change information matches the above indicator overall evaluation information. Then, in response to determining that there is no match, adjust the data of each data in the target historical value indicator data group according to the above indicator overall evaluation information, so that the indicator data change information corresponding to each indicator data after adjustment matches the above indicator overall evaluation information.

[0057] Sub-step 3, obtaining the first historical indicator importance information group corresponding to the above historical time and the indicator importance information generation model at the current time. Among them, the indicator importance information generation model can be a neural network model for generating indicator importance information at the current time. In practice, the indicator importance information generation model can be a multi-task classification model based on value credential information. For example, the indicator importance information generation model can be a multi-layer convolutional layer connected in series, and the indicator importance information generation model can be trained based on the back propagation method.

[0058] Sub-step 4, based on the pre-processed data group, using the indicator importance information generation model to generate a second historical indicator importance information group at the historical time. The second historical indicator importance information in the second historical indicator importance information group has a one-to-one correspondence with the value indicator in the at least one value indicator.

[0059] As an example, the execution entity may input the preprocessed data group into an indicator importance information generation model to generate a second historical indicator importance information group.

[0060] Sub-step 5: generating the indicator importance information group according to the first historical indicator importance information group and the second historical indicator importance information group.

[0061] Optionally, generating the indicator importance information group according to the first historical indicator importance information group and the second historical indicator importance information group includes:

[0062] For each value indicator of the at least one value indicator, the following third generating step is performed:

[0063] Sub-step 1: determine the first historical indicator importance information and the second historical indicator importance information corresponding to the above value indicator, and use them as the first target historical indicator importance information and the second target historical indicator importance information respectively.

[0064] Sub-step 2: Determine the historical indicator importance information generation model corresponding to the above historical time. Wherein, each historical time has a corresponding historical indicator importance information generation model.

[0065] Sub-step 3, obtaining the model accuracy value corresponding to the historical indicator importance information generation model and the model accuracy value corresponding to the indicator importance information generation model, as the first model accuracy value and the second model accuracy value respectively.

[0066] Sub-step 4: normalize the first model accurate value and the second model accurate value to obtain a first normalized value and a second normalized value, wherein the sum of the first normalized value and the second normalized value is 1.

[0067] Sub-step 5: multiply the first normalized value and the first target historical indicator importance information to obtain first multiplication information.

[0068] Sub-step 6: multiply the second normalized value and the second target historical indicator importance information to obtain second multiplication information.

[0069] Sub-step 7, adding the first multiplication information and the second multiplication information to obtain the initial indicator importance information corresponding to the value indicator.

[0070] Sub-step 8, in response to determining that the initial indicator importance information is within the information interval corresponding to the value indicator, the initial indicator importance information is determined as the indicator importance information corresponding to the value indicator. Each value indicator has a corresponding indicator value interval (i.e., information interval). The indicator value interval can represent the upper and lower limits of the content corresponding to the value indicator.

[0071] Optionally, in response to determining that the initial indicator importance information is within the information interval corresponding to the value indicator, after determining the initial indicator importance information as the indicator importance information corresponding to the value indicator, the method further includes:

[0072] In the first step, in response to determining that the initial indicator importance information is greater than or equal to the maximum value corresponding to the information interval, the maximum value in the information interval is determined as the indicator importance information corresponding to the value indicator.

[0073] In the second step, in response to determining that the initial indicator importance information is less than or equal to the minimum value corresponding to the information interval, the minimum value in the information interval is determined as the indicator importance information corresponding to the value indicator.

[0074] Step 107, based on the obtained indicator importance information group sequence, calling the central processing unit to generate a current indicator importance information group for the above target subject.

[0075] In some embodiments, the execution subject may call a central processing unit (CPU) according to the obtained indicator importance information group sequence to generate a current indicator importance information group for the target subject, wherein the current indicator importance information may be an indicator importance information group at the current time.

[0076] In some optional implementations of some embodiments, based on the obtained indicator importance information group sequence, calling a central processor to generate a current indicator importance information group for the target subject includes the following steps:

[0077] The first step is to divide each historical time in the historical time series into time divisions to generate a historical segment sequence, wherein a historical segment includes at least one adjacent historical time.

[0078] In the second step, at least one historical weight corresponding to each historical segment in the historical segment sequence is determined by at least one predetermined historical weight list, wherein a historical weight list in at least one historical weight list has a one-to-one correspondence with a value indicator in at least one value indicator. Each historical weight list in at least one historical weight list may be pre-set. The historical weight list may characterize the correspondence between the historical weight and the historical segment under the corresponding value indicator. The closer the historical segment is to the current time, the greater the corresponding historical weight.

[0079] The third step is to execute the following first information generation step for each indicator importance information group in the indicator importance information group sequence:

[0080] Sub-step 1, determining the historical segment corresponding to the above indicator importance information group as the target historical segment.

[0081] Sub-step 2: determining at least one historical weight corresponding to the target historical frame as at least one target historical weight.

[0082] Sub-step 3: multiply each indicator importance information in the indicator importance information group by each target history weight in at least one target history weight to generate a multiplied information group.

[0083] In the fourth step, for each value indicator in the at least one value indicator, the following second information generation step is performed:

[0084] Sub-step 1, selecting a multiplication information sequence corresponding to the value index from the obtained multiplication information group sequence as the target multiplication information sequence.

[0085] Sub-step 2: Generate the first current indicator importance information through a linear regression model based on the above target multiplication information sequence.

[0086] The fifth step is to input the obtained multiplication information group sequence into the pre-trained current indicator importance information generation model to generate the second current indicator importance information group. Among them, the current indicator importance information generation model can be a neural network model that generates indicator importance information at the current time. In practice, each input of the current indicator importance information generation model is a multiplication information group, and the indicator importance information group at the current time (i.e., the second current indicator importance information group) is predicted through the multiplication information groups at various historical times. Here, the current indicator importance information generation model can be a multi-layer series of convolutional layers + fully connected layers. The current indicator importance information generation model aims to learn the overall indicator importance at various historical times, and through the overall changes in the overall indicator importance at various times, to learn the overall change characteristics, to predict the overall indicator importance under various value indicators at the current time (i.e., the second current indicator importance information group).

[0087] Step 6: Determine the first model weight corresponding to the linear regression model and the second model weight corresponding to the current indicator importance information generation model. The sum of the first model weight and the second model weight is 1. The second model weight is higher than the first model weight. The first model weight and the second model weight can be pre-set.

[0088] The seventh step is to perform weighted summation of the first current indicator importance information group and the second current indicator importance information group according to the first model weight and the second model weight to generate a weighted indicator importance information group.

[0089] The above-mentioned optional implementation methods and optional contents in some embodiments, as an inventive point of the present disclosure, solve the technical problem of "the weighted indicator importance is not accurate enough." Based on this, the present disclosure predicts the indicator importance of each value indicator at the current time by using linear regression. The model generated by the current indicator importance information can predict the overall indicator importance at the current time. Combining the first model weight and the second model weight, the importance of at least one indicator corresponding to at least one value indicator at the current time can be accurately determined.

[0090] In some optional implementations of some embodiments, the above-mentioned calling a central processor according to the obtained indicator importance information group sequence to generate a current indicator importance information group for the above-mentioned target subject may include the following steps:

[0091] The first step is to call the above central processing unit to perform the following fourth generation step:

[0092] Sub-step 1: Generate an indicator importance information matrix corresponding to the indicator importance information group sequence, wherein the corresponding row elements of the indicator importance information matrix represent historical time, and the corresponding column elements represent value indicator information.

[0093] Sub-step 2: normalize the elements of the above-mentioned indicator importance information matrix to obtain a normalized matrix, wherein the sum of the elements corresponding to each element in the normalized matrix is ​​1.

[0094] Sub-step 3: Generate at least one current indicator importance information corresponding to at least one value indicator.

[0095] As an example, generating at least one current indicator importance information corresponding to at least one value indicator includes:

[0096] For each value indicator of the at least one value indicator, the following fifth generating step is performed:

[0097] The first sub-step is to select, from the normalized matrix, a set of elements that are associated with the value index.

[0098] As an example, the execution subject may select an element set corresponding to the value indicator from the normalized matrix by searching for column elements.

[0099] The second sub-step is to add up the elements in the above element set to generate the current indicator importance information corresponding to the above value indicator.

[0100] Sub-step 4: determining the obtained at least one current indicator importance information as the above-mentioned current indicator importance information group.

[0101] Step 108, generating current value voucher information according to the above-mentioned current indicator importance information group and the acquired current subject value processing data set.

[0102] In some embodiments, the execution subject may generate current value voucher information according to the current indicator importance information group and the acquired current subject value processing data set, wherein the current value voucher information may be value voucher information at the current time.

[0103] In some optional implementations of some embodiments, the above-mentioned processing data set based on the above-mentioned current indicator importance information group and the acquired current subject value to generate the current value voucher information may include the following steps:

[0104] The first step is to extract at least one current value indicator data corresponding to the at least one value indicator from the current subject value processing data set, wherein the at least one current value indicator data may be indicator data corresponding to at least one value indicator at the current time.

[0105] The second step is to perform weighted processing on the above-mentioned current indicator importance information group and the above-mentioned at least one current value indicator data to generate a weighted processing information group.

[0106] As an example, the execution subject may multiply each current indicator importance information with the corresponding current value indicator data to generate weighted processing information and obtain a weighted processing information group.

[0107] In the third step, the weighted processed information group is input into a pre-trained value voucher information generation model to generate the current value voucher information. The value voucher information generation model may be a neural network model for generating value voucher information. In practice, the value voucher information generation model may be a pre-trained time series classification model. The value voucher information generation model may be trained based on back propagation. The loss function corresponding to the value voucher information generation model may be a cross entropy loss function. For example, the value voucher information generation model may be a long short-term memory network (LSTM, Long Short-Term Memory) model.

[0108] Step 109, the current value voucher information and the subject information corresponding to the target subject are stored in the form of an encrypted key-value pair. The subject information may represent the subject identity information of the target subject. For example, the subject information may be the subject identifier of the target subject.

[0109] In some embodiments, the execution subject may store the current value voucher information and the subject information corresponding to the target subject in the form of an encrypted key-value pair.

[0110] As an example, first, the execution subject can use the target encryption algorithm to encrypt the current value credential information and the subject information respectively to generate encrypted credential information and encrypted subject information. Then, the encrypted credential information and the encrypted subject information are stored in the form of key-value pairs.

[0111] In some optional implementations of some embodiments, the steps further include:

[0112] The first step is to perform a voucher rating for the above current value voucher information. The voucher rating can represent the voucher level of the target entity. In practice, the higher the voucher rating, the higher the credit of the corresponding target entity.

[0113] The second step is to store the above-mentioned current value voucher information, the above-mentioned voucher rating and the above-mentioned subject information in the form of encrypted key-value pairs. The specific implementation method will not be repeated here.

[0114] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the value-related information storage method of some embodiments of the present disclosure, the value voucher information can be efficiently and accurately generated while fully utilizing online computing resources. Specifically, the reason why the relevant value voucher information is not accurate and efficient is that: when the data volume corresponding to the historical value data sequence is large, the image processor is used to generate online information, which often causes the processor to have a large data processing pressure and run under a high load state for a long time. When the temperature exceeds a predetermined threshold (for example, 95 degrees), it is easy to crash. The situation that a generation breakpoint occurs during the generation of the value voucher information often causes the generated value voucher information to be not efficient and accurate. In addition, using the historical value data sequence as a model input causes the generated value voucher information to be not accurate. Based on this, the value-related information storage method of some embodiments of the present disclosure first obtains the historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value voucher information, so as to obtain the data set corresponding to the value voucher information for the main analysis, and improves the accuracy and speed of online generation of value voucher information while being more representative and reducing the amount of data based on at least one value indicator. Then, at least one historical value indicator data sequence corresponding to the above-mentioned at least one value indicator is extracted from the above-mentioned historical subject value processing data set to facilitate the subsequent generation of value voucher information. Then, each historical indicator data in the above-mentioned at least one historical value indicator data sequence is classified at the same time to generate a historical value indicator data group sequence, wherein each historical value indicator data in each historical value indicator data group corresponds to the same historical time. Here, by classifying the historical indicator data at the same time, it is convenient to determine the importance of the indicator at each historical time in the future, so as to more accurately generate the importance of the indicator corresponding to each indicator from the time dimension. Then, for each computing node in the current computing node cluster, the remaining available computing resources of at least one graphics processor deployed corresponding to the above computing node and the node call order corresponding to the above computing node are determined. Here, by means of computing node clusters, and each computing node has multiple graphics processors deployed, in this way, in the form of a cluster, without affecting the work of other online work and by determining the remaining available computing resources and the node call order, the online available resources are reasonably arranged to avoid the graphics processor from being under long-term high load, so that the graphics processor crashes. Furthermore, based on the number of groups corresponding to the historical value indicator data groups, the data byte size corresponding to the historical value indicator data groups, the remaining available computing resources corresponding to each computing node and the node calling order, the computing node information corresponding to each historical value indicator data group in the above historical value indicator data group sequence is determined to achieve reasonable scheduling of resources, ensure full utilization of online resources, and greatly improve utilization efficiency.Secondly, for each historical time in the historical time series, the first generation step is performed: the first step is to determine the historical value indicator data group corresponding to the above historical time as the target historical value indicator data group, so as to facilitate the generation of indicator importance information. The second step is to call at least one graphics processor in the computing node corresponding to the above target historical value indicator data group to accurately generate the corresponding indicator importance information group. Furthermore, based on the obtained indicator importance information group sequence, the central processing unit is called to efficiently and accurately generate the current indicator importance information group for the above target subject. Therefore, based on the above current indicator importance information group and the obtained current subject value processing data set, the current value voucher information can be generated under the condition of an accurate current indicator importance information group. Finally, the above current value voucher information and the subject information corresponding to the above target subject are stored in the form of an encrypted key-value pair to facilitate the corresponding use of the current value voucher information and the subject information.

[0115] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a value-related information storage device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the value-related information storage device can be specifically applied to various electronic devices.

[0116] like Figure 2As shown, a value-related information storage device 200 includes: an acquisition unit 201, an extraction unit 202, a classification unit 203, a first determination unit 204, a second determination unit 205, an execution unit 206, a calling unit 207, a generation unit 208 and a storage unit 209. Among them, the acquisition unit 201 is configured to acquire the historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value voucher information; the extraction unit 202 is configured to extract at least one historical value indicator data sequence corresponding to the above at least one value indicator from the above historical subject value processing data set; the classification unit 203 is configured to classify the various historical indicator data in the above at least one historical value indicator data sequence at the same time to generate a historical value indicator data group sequence, wherein the historical time corresponding to each historical value indicator data in each historical value indicator data group is the same; the first determination unit 204 is configured to determine, for each computing node in the current computing node cluster, the remaining available computing resources of at least one graphics processor deployed corresponding to the above computing node and the node call order corresponding to the above computing node; the second determination unit 205 is configured to determine the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, and each computing node The corresponding remaining available computing resources and node calling order are used to determine the computing node information corresponding to each historical value indicator data group in the above-mentioned historical value indicator data group sequence; the execution unit 206 is configured to execute the first generation step for each historical time in the historical time sequence: determine the historical value indicator data group corresponding to the above-mentioned historical time as the target historical value indicator data group; call at least one graphics processor in the computing node corresponding to the above-mentioned target historical value indicator data group to generate the corresponding indicator importance information group; the calling unit 207 is configured to call the central processing unit according to the obtained indicator importance information group sequence to generate the current indicator importance information group for the above-mentioned target subject; the generating unit 208 is configured to generate the current value voucher information according to the above-mentioned current indicator importance information group and the obtained current subject value processing data set; the storage unit 209 is configured to store the above-mentioned current value voucher information and the subject information corresponding to the above-mentioned target subject in the form of an encrypted key-value pair.

[0117] It is understandable that the units recorded in the value-related information storage 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-related information storage device 200 and the units contained therein, and will not be described in detail here.

[0118] 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.

[0119] 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 into 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.

[0120] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although 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.

[0121] 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.

[0122] 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. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, 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 suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0123] 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.

[0124] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains the historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value voucher information; extracts at least one historical value indicator data sequence corresponding to the above-mentioned at least one value indicator from the above-mentioned historical subject value processing data set; performs time classification on each historical indicator data in the above-mentioned at least one historical value indicator data sequence to generate a historical value indicator data group sequence, wherein each historical value indicator data in each historical value indicator data group corresponds to the same historical time; for each computing node in the current computing node cluster, determines the remaining available computing resources of at least one graphics processor deployed corresponding to the above-mentioned computing node and the node calling order corresponding to the above-mentioned computing node; according to the number of groups corresponding to the historical value indicator data group, the historical value indicator data group According to the corresponding data byte size, the remaining available computing resources corresponding to each computing node and the node calling order, the computing node information corresponding to each historical value indicator data group in the above-mentioned historical value indicator data group sequence is determined; for each historical time in the historical time sequence, the first generation step is executed: the historical value indicator data group corresponding to the above-mentioned historical time is determined as the target historical value indicator data group; at least one graphics processor in the computing node corresponding to the above-mentioned target historical value indicator data group is called to generate the corresponding indicator importance information group; according to the obtained indicator importance information group sequence, the central processing unit is called to generate the current indicator importance information group for the above-mentioned target subject; according to the above-mentioned current indicator importance information group and the obtained current subject value processing data set, the current value credential information is generated; the above-mentioned current value credential information and the subject information corresponding to the above-mentioned target subject are stored in the form of an encrypted key-value pair.

[0125] 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 cases involving a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] 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.

[0127] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor including an acquisition unit, an extraction unit, a classification unit, a first determination unit, a second determination unit, an execution unit, a calling unit, a generation unit and a storage unit. The names of these units do not constitute limitations on the units themselves in certain circumstances, for example, the acquisition unit may also be described as "a unit for acquiring a historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value credential information".

[0128] 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.

[0129] 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-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for storing value-related information, comprising: Acquire a historical subject value processing data set corresponding to the target subject and at least one value indicator related to the value voucher information; Extracting at least one historical value indicator data sequence corresponding to the at least one value indicator from the historical subject value processing data set; Classifying each historical indicator data in the at least one historical value indicator data sequence by time to generate a historical value indicator data group sequence, wherein each historical value indicator data in each historical value indicator data group corresponds to the same historical time; For each computing node in the current computing node cluster, determining the remaining available computing resources of at least one graphics processor deployed corresponding to the computing node and the node call order corresponding to the computing node; Determine the computing node information corresponding to each historical value indicator data group in the historical value indicator data group sequence according to the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, the remaining available computing resources corresponding to each computing node, and the node calling order; For each historical time in the historical time series, the first generation step is performed: Determine a historical value indicator data group corresponding to the historical time as a target historical value indicator data group; Calling at least one graphics processor in a computing node corresponding to the target historical value indicator data group to generate a corresponding indicator importance information group; According to the obtained indicator importance information group sequence, a central processor is called to generate a current indicator importance information group for the target subject; Generate current value voucher information according to the current indicator importance information group and the acquired current subject value processing data set; The current value credential information and the subject information corresponding to the target subject are stored in the form of an encrypted key-value pair.

2. The method according to claim 1, wherein: The calling of at least one graphics processor in a computing node corresponding to the target historical value indicator data group to generate a corresponding indicator importance information group includes: Call at least one graphics processor in the computing node corresponding to the target historical value indicator data group to perform the following second generation step: Acquire at least one indicator overall evaluation information corresponding to the target subject at the historical time, wherein the indicator overall evaluation information in the at least one indicator overall evaluation information and the value indicator in the at least one value indicator have a one-to-one correspondence relationship; According to the at least one overall indicator evaluation information, data preprocessing is performed on each data in the target historical value indicator data group to obtain a preprocessed data group; Obtaining a first historical indicator importance information group corresponding to the historical time and an indicator importance information generation model at the current time; According to the preprocessed data group, using the indicator importance information generation model to generate a second historical indicator importance information group at the historical time; The indicator importance information group is generated according to the first history indicator importance information group and the second history indicator importance information group.

3. The method according to claim 2, wherein: The step of generating the indicator importance information group according to the first historical indicator importance information group and the second historical indicator importance information group includes: For each value indicator of the at least one value indicator, the following third generating step is performed: Determine the first historical indicator importance information and the second historical indicator importance information corresponding to the value indicator, as the first target historical indicator importance information and the second target historical indicator importance information respectively; Determine a historical indicator importance information generation model corresponding to the historical time; Obtaining a model accuracy value corresponding to the historical indicator importance information generation model and a model accuracy value corresponding to the indicator importance information generation model, as the first model accuracy value and the second model accuracy value, respectively; Normalizing the first model accurate value and the second model accurate value to obtain a first normalized value and a second normalized value; Multiplying the first normalized value and the first target historical indicator importance information to obtain first multiplication information; Multiplying the second normalized value and the second target historical indicator importance information to obtain second multiplication information; Adding the first multiplication information and the second multiplication information to obtain initial indicator importance information corresponding to the value indicator; In response to determining that the initial indicator importance information is within the information interval corresponding to the value indicator, the initial indicator importance information is determined as the indicator importance information corresponding to the value indicator.

4. The method according to claim 3, wherein: After, in response to determining that the initial indicator importance information is within the information interval corresponding to the value indicator, determining the initial indicator importance information as the indicator importance information corresponding to the value indicator, the method further includes: In response to determining that the initial indicator importance information is greater than or equal to the maximum value corresponding to the information interval, determining the maximum value in the information interval as the indicator importance information corresponding to the value indicator; In response to determining that the initial indicator importance information is less than or equal to the minimum value corresponding to the information interval, the minimum value in the information interval is determined as the indicator importance information corresponding to the value indicator.

5. The method according to claim 4, wherein: Determining the computing node information corresponding to each historical value indicator data group in the historical value indicator data group sequence according to the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, the remaining available computing resources corresponding to each computing node, and the node calling order includes: Sort the computing nodes according to the node calling sequence to generate a computing node information sequence; Determine the target quantile data byte of the data byte size sequence corresponding to the historical value indicator data group sequence; Removing computing node information corresponding to remaining available computing resources less than the target quantile data bytes from the computing node information sequence to obtain a computing node information sequence after removal; Generating a remaining available computing resource sequence for the removed computing node information sequence; Determine a data byte size sequence corresponding to the historical value indicator data group sequence; Inputting the data byte size sequence and the remaining available computing resource sequence into a computing node scheduling model to generate computing node scheduling information, wherein the computing node scheduling information represents the corresponding relationship between the data byte size subsequence and the remaining available computing resource subsequence, the sequence lengths corresponding to the various data byte size subsequences are not uniform, and the total amount of data bytes corresponding to each data byte size sequence is less than the total amount of resources corresponding to the corresponding remaining available computing resource subsequence; The computing node information corresponding to the historical value indicator data group is determined according to the computing node scheduling information.

6. The method according to claim 5, wherein: The method of calling a central processor according to the obtained indicator importance information group sequence to generate a current indicator importance information group for the target subject includes: The central processing unit is called to execute the following fourth generation step: Generate an indicator importance information matrix corresponding to the indicator importance information group sequence; Performing element normalization on the indicator importance information matrix to obtain a normalized matrix; Generate at least one current indicator importance information corresponding to at least one value indicator; The obtained at least one current indicator importance information is determined as the current indicator importance information group.

7. The method according to claim 1, wherein: The step of processing the data set based on the current indicator importance information group and the acquired current subject value to generate current value voucher information includes: extracting at least one current value indicator data corresponding to the at least one value indicator from the current subject value processing data set; Performing weighted processing on the current indicator importance information group and the at least one current value indicator data to generate a weighted processing information group; The weighted processed information group is input into a pre-trained value voucher information generation model to generate the current value voucher information.

8. A value-related information storage device, comprising: An acquisition unit configured to acquire a historical subject value processing data set corresponding to a target subject and at least one value indicator related to the value voucher information; An extraction unit configured to extract at least one historical value indicator data sequence corresponding to the at least one value indicator from the historical subject value processing data set; a classification unit configured to classify the respective historical indicator data in the at least one historical value indicator data sequence at the same time to generate a historical value indicator data group sequence, wherein the respective historical value indicator data in each historical value indicator data group correspond to the same historical time; A first determining unit is configured to determine, for each computing node in the current computing node cluster, remaining available computing resources of at least one graphics processor deployed corresponding to the computing node and a node calling order corresponding to the computing node; The second determining unit is configured to determine the computing node information corresponding to each historical value indicator data group in the historical value indicator data group sequence according to the number of groups corresponding to the historical value indicator data group, the data byte size corresponding to the historical value indicator data group, the remaining available computing resources corresponding to each computing node, and the node calling order; The execution unit is configured to execute the first generation step for each historical time in the historical time series: determine the historical value indicator data group corresponding to the historical time as the target historical value indicator data group; call at least one graphics processor in the computing node corresponding to the target historical value indicator data group to generate a corresponding indicator importance information group; A calling unit is configured to call a central processor according to the obtained indicator importance information group sequence to generate a current indicator importance information group for the target subject; a generating unit configured to generate current value voucher information according to the current indicator importance information group and the acquired current subject value processing data set; The storage unit is configured to store the current value credential information and the subject information corresponding to the target subject in the form of an encrypted key-value pair.

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

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

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