Resource allocation method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a resource allocation method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: in response to a received storage space adjustment instruction, obtaining a first mechanism user data set and a second mechanism user data set; determining first user coincidence information corresponding to each target institution identification group based on the first institution user data set; based on the first user coincidence information, updating the first mechanism user data set to obtain an updated first mechanism user data set; generating an updated second institution user data set based on the second institution user data set; generating a mechanism grading information set; detecting and processing the use information of the storage space of each mechanism to obtain a mechanism deployable storage space information set; generating a mechanism capacity expansion information set and a mechanism capacity reduction information set; and controlling the database server to adjust the storage space capacity of each mechanism. According to the embodiment, excessive occupation and waste of storage resources can be reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technologies, and particularly to resource allocation methods, devices, electronic devices, and computer-readable media. Background Art

[0002] Resource allocation plays an important role in balancing the database storage spaces of various institutions. Currently, when performing resource allocation, the commonly adopted method is that the database server divides the storage space for each institution by means of random allocation or average allocation to meet the storage requirements of its user data. When the storage space corresponding to an institution is insufficient, the database server will re-divide the storage space and expand the storage space of the institution with insufficient storage space to complete the resource allocation.

[0003] However, it is found in practice that when resource allocation is performed in the above manner, the following technical problems often exist:

[0004] Since there are differences among the user groups of various institutions, there are also significant differences in the storage space capacities required by various institutions. When the storage space capacity required by an institution is small and there is a large amount of unused storage space, it is easy to cause waste of storage resources; when the storage space required by an institution is large and the storage space capacity is insufficient, if the above-mentioned re-division method is used to expand the storage space, it will lead to excessive occupation of storage resources.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the concept of the present disclosure, and thus, it may include information that does not form the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] This content part of the present disclosure is used to briefly introduce the concepts, which will be described in detail in the following detailed implementation part. This content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose resource allocation methods, devices, electronic devices, and computer-readable media to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide a resource allocation method, which includes: in response to confirming that the database storage space for storing institutional user data is insufficient, sending a database storage space adjustment request to a server; in response to receiving the storage space adjustment instruction sent by the above server, obtaining a first institutional user data set and a second institutional user data set, wherein each first institutional user data in the above first institutional user data set is log data when the institution calls a preset business product with user information as a query primary key, and each second institutional user data in the above second institutional user data set is user behavior tag data collected by the institution; based on the above first institutional user data set, determining the first user coincidence information corresponding to each target institutional identification group in the target institutional identification group set, wherein each target institutional identification group in the above target institutional identification group set is composed of two different preset institutional identifications in the preset institutional identification group; based on the obtained respective first user coincidence information, performing an update process on the above first institutional user data group set to obtain an updated first institutional user data group set; based on the above second institutional user data set, generating an updated second institutional user data group set; based on the above updated first institutional user data group set and the above updated second institutional user data group set, generating an institutional classification information set; performing a detection process on each institutional storage space usage information in the pre-obtained institutional storage space usage information set to obtain an institutional adjustable storage space information set; based on the above institutional classification information set and the above institutional adjustable storage space information set, generating an institutional expansion information set and an institutional contraction information set; based on the above institutional expansion information set and the above institutional contraction information set, controlling the associated database server to adjust the storage space capacity of each institution.

[0009] Second aspect, some embodiments of the present disclosure provide a resource allocation device, the device comprising: a sending unit configured to send a database storage space adjustment request to a server in response to confirming that the database storage space for storing institutional user data is insufficient; an obtaining unit configured to obtain a first institutional user data set and a second institutional user data set in response to receiving the storage space adjustment instruction sent by the above-mentioned server, wherein each first institutional user data in the above-mentioned first institutional user data set is log data when the institution calls a preset business product using user information as a query primary key, and each second institutional user data in the above-mentioned second institutional user data set is user behavior tag data collected by the institution; a determining unit configured to determine, based on the above-mentioned first institutional user data set, the first user coincidence information corresponding to each target institutional identification group in the target institutional identification group set, wherein each target institutional identification group in the above-mentioned target institutional identification group set is composed of two different preset institutional identifications in the preset institutional identification group; an updating processing unit configured to perform an updating process on the above-mentioned first institutional user data set based on the obtained respective first user coincidence information to obtain an updated first institutional user data set; a first generating unit configured to generate an updated second institutional user data set based on the above-mentioned second institutional user data set; a second generating unit configured to generate an institutional grading information set based on the above-mentioned updated first institutional user data set and the above-mentioned updated second institutional user data set; a detecting processing unit configured to perform a detecting process on each institutional storage space usage information in a pre-obtained institutional storage space usage information set to obtain an institutional adjustable storage space information set; a second generating unit configured to generate an institutional expansion information set and an institutional contraction information set based on the above-mentioned institutional grading information set and the above-mentioned institutional adjustable storage space information set; a control unit configured to control an associated database server to perform a storage space capacity adjustment on each institution based on the above-mentioned institutional expansion information set and the above-mentioned institutional contraction information set.

[0010] Third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs, which when executed by the one or more processors cause the one or more processors to implement the method described in any implementation manner of the first aspect above.

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

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the resource allocation method of some embodiments of the present disclosure, the excessive occupation and waste of storage resources can be reduced. Specifically, the reasons for the waste or excessive occupation of storage resources are as follows: Due to the differences among the user groups of each institution, there are also significant differences in the storage space capacity required by each institution. When the storage space capacity required by an institution is small and the unused storage space is large, it is easy to cause waste of storage resources; when the storage space required by an institution is large and the storage space capacity is insufficient, if the above-mentioned re-partitioning method is used to expand the storage space, it will lead to excessive occupation of storage resources. Based on this, the resource allocation method of some embodiments of the present disclosure, first, in response to confirming that the database storage space for storing institutional user data is insufficient, send a database storage space adjustment request to the server. Second, in response to receiving the storage space adjustment instruction sent by the above-mentioned server, obtain the first institutional user data set and the second institutional user data set. Among them, each first institutional user data in the above-mentioned first institutional user data set is log data when the institution calls a preset business product with user information as the query primary key, and each second institutional user data in the above-mentioned second institutional user data set is user behavior label data collected by the institution. Thus, two sample data sets of different source types corresponding to each institution can be obtained. Then, based on the above-mentioned first institutional user data set, determine the first user coincidence information corresponding to each target institutional identification group in the target institutional identification group set, where each target institutional identification group in the above-mentioned target institutional identification group set is composed of two different preset institutional identifications in the preset institutional identification group. Thus, the information on the coincidence of user groups between different institutions corresponding to the product call log records can be determined. After that, based on the obtained first user coincidence information, perform an update process on the above-mentioned first institutional user data set to obtain an updated first institutional user data set. Thus, according to the user coincidence sample data between different institutions, combined with the original sample data of each existing institution, the original sample data corresponding to each institution can be supplemented with samples to make up for the shortage of the original sample data volume. Next, based on the above-mentioned second institutional user data set, generate an updated second institutional user data set. Thus, the original sample data corresponding to the institution from the user behavior can be supplemented with samples to make up for the shortage of the original sample data volume. Then, based on the above-mentioned updated first institutional user data set and the above-mentioned updated second institutional user data set, generate an institutional classification information set. Thus, the level information of each institution can be obtained. Then, perform a detection process on each institutional storage space usage information in the pre-obtained institutional storage space usage information set to obtain an institutional adjustable storage space information set. Thus, the adjustable state of the current storage space capacity of each institution can be obtained.Next, based on the above institutional classification information set and the above institutional adjustable storage space information set, an institutional expansion information set and an institutional contraction information set are generated. Thus, the resource information of each institution to be expanded and the resource information of each institution to be contracted can be obtained. Finally, based on the above institutional expansion information set and the above institutional contraction information set, the associated database server is controlled to adjust the storage space capacity of each institution. Therefore, in the resource allocation method of some embodiments of the present disclosure, when the storage space of institutional user data is insufficient, the level of the institution is determined according to the customer group information of the institution, and on this basis, according to the institutional classification information and the current resource adjustable state of the institution, the institutional expansion information set and the institutional contraction information set are determined, so that institutions with less storage space capacity and more unused storage space can be contracted, and the contracted capacity can be used to expand the storage space of institutions that require more storage space and have insufficient storage space capacity. Thus, excessive occupation and waste of storage resources can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[0017] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0018] It should also be noted that, for the sake of convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

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

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in this disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified 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 this disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] For operations such as collection, storage, and use of log data and user behavior tag data involved in this disclosure, before performing the corresponding operations, relevant organizations or individuals shall fulfill obligations including conducting personal information security impact assessments, fulfilling the obligation of notification to the personal information subject, and obtaining the prior authorization and consent of the personal information subject.

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

[0024] Figure 1 Flow 100 of some embodiments of a resource allocation method according to the present disclosure is shown. The resource allocation method includes the following steps:

[0025] Step 101, in response to confirming that the database storage space for storing institutional user data is insufficient, send a database storage space adjustment request to the server.

[0026] In some embodiments, the execution subject of the resource allocation method (such as a computing device) may, in response to confirming that the database storage space for storing institutional user data is insufficient, send a database storage space adjustment request to the server. Among them, the above database storage space adjustment request may be a network request for confirming whether to adjust the database storage space.

[0027] Step 102, in response to receiving the storage space adjustment instruction sent by the server, obtain a first institutional user data set and a second institutional user data set.

[0028] In some embodiments, the above-mentioned execution entity may, in response to receiving the storage space adjustment instruction sent by the above-mentioned server, obtain the first institutional user dataset and the second institutional user dataset from the database through a wired connection method or a wireless connection method. Among them, the above-mentioned storage space adjustment instruction may be an instruction for adjusting the storage space of the database. Each piece of first institutional user data in the above-mentioned first institutional user dataset may be log data when the institution calls a preset business product using user information as a query primary key. Each piece of second institutional user data in the above-mentioned second institutional user dataset may be user behavior tag data collected by the institution. The above-mentioned institution may be an institution within a preset industry. The above-mentioned preset industry may be a preset industry. For example, the above-mentioned preset industry may be the financial industry, and the above-mentioned institution may be a bank. The preset business products in each of the above-mentioned preset business products may be preset business products for institutions to call. For example, the business products may be, but are not limited to, one of the following: credit value assessment products, identity verification products. The above-mentioned user information may include a user identifier and a user mobile phone number. The above-mentioned user identifier may be the unique identifier of the user. For example, the above-mentioned user identifier may be the user's ID number. Each piece of first institutional user data in the above-mentioned first institutional user dataset may include, but is not limited to, a user identifier, a user mobile phone number, a product identifier, the product call time, and a call institution identifier. The above-mentioned product identifier may be the unique identifier of the business product. The above-mentioned product call time may be the time when the business product is called. The above-mentioned call institution identifier may be the unique identifier of the calling institution. The calling institution may be the institution that calls the business product. In addition, the above-mentioned user behavior tag data may include, but is not limited to, a user identifier, a user mobile phone number, a behavior tag, an associated institution identifier, and a retrospective time. The above-mentioned behavior tag may indicate whether the user repays the loan on time. When the above-mentioned behavior tag is 1, it indicates that the user fails to repay the loan on time; when the above-mentioned behavior tag is 0, it indicates that the user repays the loan on time. The above-mentioned associated institution identifier may be the unique identifier of the associated institution. The associated institution may be the institution that provides the user behavior tag data. The above-mentioned retrospective time may be the time range for evaluating the user's overdue status. For example, the above-mentioned retrospective time may be the past 3 months.

[0029] It should be noted that the above-mentioned wireless connection method may include, but is not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future-developed wireless connection methods.

[0030] Step 103: Based on the first institutional user dataset, determine the first user coincidence information corresponding to each target institutional identifier group in the target institutional identifier group set.

[0031] In some embodiments, the above-mentioned execution entity may, in various ways, determine the first user overlap information corresponding to each target institutional identification group in the target institutional identification group set based on the above-mentioned first institutional user data set. Each target institutional identification group in the above-mentioned target institutional identification group set may be composed of two different preset institutional identifications in the preset institutional identification group. The preset institutional identifications in the above-mentioned preset institutional identification group may be the identifications of pre-set institutions. The above-mentioned first user overlap information may be information on the overlap degree of users between the corresponding two calling institutions.

[0032] In some alternative implementation manners of some embodiments, the above-mentioned execution entity may determine the first user overlap information corresponding to each target institutional identification group in the target institutional identification group set based on the above-mentioned first institutional user data set through the following steps:

[0033] First step, classify each piece of first institutional user data in the above-mentioned first institutional user data set to obtain a set of first institutional user data groups. Each first institutional user data group in the above-mentioned set of first institutional user data groups may correspond one-to-one to a preset institutional identification in the preset institutional identification group. Each first institutional user data group in the above-mentioned set of first institutional user data groups may be a set of each piece of first institutional user data corresponding to the same calling institution. The classification process of each piece of first institutional user data in the above-mentioned first institutional user data set may be performed according to the calling institution identification included in the first institutional user data to obtain a set of first institutional user data groups.

[0034] Second step, generate the total number of users and a set of first institutional user identification groups based on the above-mentioned set of first institutional user data groups. The above-mentioned total number of users may be the number of all users corresponding to the set of first institutional user data groups. Each first institutional user identification group in the above-mentioned set of first institutional user identification groups may correspond one-to-one to a preset institutional identification in the above-mentioned preset institutional identification group. The first institutional user identification groups in the above-mentioned set of first institutional user identification groups may be sets of the identifications of each user corresponding to the same calling institution. First, determine the non-repeated user identifications included in the above-mentioned set of first institutional user data groups as the total user identification set. Then, determine the number of each total user identification in the above-mentioned total user identification set as the total number of users. After that, for each first institutional user data group in the above-mentioned set of first institutional user data groups, determine the user identifications included in the first institutional user data group as the first institutional user identification group.

[0035] Third step, for each preset institutional identification in the above-mentioned preset institutional identification group, perform the following steps:

[0036] The first sub-step is to select the first institutional user identification group that matches the above-mentioned preset institutional identification from the above-mentioned first institutional user identification group set. Among them, matching the above-mentioned preset institutional identification may mean that the calling institutional identification corresponding to the first institutional user identification group is the same as the above-mentioned preset institutional identification.

[0037] The second sub-step is to determine the number of each first institutional user identification in the selected first institutional user identification group as the number of first institutional users.

[0038] The third sub-step is to determine the ratio of the above-mentioned number of first institutional users to the above-mentioned total number of users as the proportion of institutional users.

[0039] The fourth step is to, in response to determining that the proportion of institutional users corresponding to each of the above-mentioned preset institutional identification groups meets the preset user proportion condition, perform pairwise grouping processing on the preset institutional identifications in the above-mentioned preset institutional identification group to obtain a set of target institutional identification groups. Among them, the above-mentioned preset user proportion condition may be that each proportion of institutional users is less than the corresponding target proportion of institutional users. Each proportion of institutional users corresponds to a unique target proportion of institutional users. The target proportion of institutional users may be the ratio of the number of users of an institution to the total number of users in the market.

[0040] The fifth step is to, for each target institutional identification group in the above-mentioned set of target institutional identification groups, perform matching processing on the two first institutional user identification groups corresponding to the above-mentioned target institutional identification group to obtain first user coincidence information. Among them, for each target institutional identification group in the above-mentioned set of target institutional identification groups, the following steps may be used to perform matching processing on the two first institutional user identification groups corresponding to the above-mentioned target institutional identification group to obtain first user coincidence information:

[0041] The first sub-step may be to determine the intersection between the two first institutional user identification groups corresponding to the above-mentioned target institutional identification group as the overlapping first institutional user identification group.

[0042] The second sub-step is to determine the number of each overlapping first institutional user identification in the above-mentioned overlapping first institutional user identification group as the first user coincidence number.

[0043] The third sub-step is to determine the above-mentioned target institutional identification group, the above-mentioned overlapping first institutional user identification group, and the above-mentioned first user coincidence number as the first user coincidence information.

[0044] Step 104 is to update the first institutional user data group set based on the obtained first user coincidence information to obtain an updated first institutional user data group set.

[0045] In some embodiments, the above-mentioned execution entity may, in various ways, update the above-mentioned first institutional user data set based on each obtained first user overlap information to obtain an updated first institutional user data set. Among them, the updated first institutional user data groups in the above-mentioned updated first institutional user data set may correspond one-to-one to the first institutional user data groups in the above-mentioned first institutional user data set. The updated first institutional user data groups in the above-mentioned updated first institutional user data set may be the first institutional user data groups after adding institutional user data.

[0046] In some alternative implementation manners of some embodiments, each first user overlap information in each obtained first user overlap information may include a first user overlap count. The above-mentioned execution entity may update the above-mentioned first institutional user data set based on each obtained first user overlap information through the following steps to obtain an updated first institutional user data set:

[0047] First step, for each first user overlap information in each obtained first user overlap information, perform the following steps:

[0048] First sub-step, determine a first preset institutional identifier and a second preset institutional identifier corresponding to the above-mentioned first user overlap information. Among them, first, one preset institutional identifier in the target institutional identifier group corresponding to the above-mentioned first user overlap information may be determined as the first preset institutional identifier. Then, another preset institutional identifier in the target institutional identifier group corresponding to the above-mentioned first user overlap information that is different from the above-mentioned first preset institutional identifier is determined as the second preset institutional identifier.

[0049] Second sub-step, determine the ratio between the first user overlap count included in the above-mentioned first user overlap information and the number of institutional users corresponding to the above-mentioned first preset institutional identifier as the first sampling ratio.

[0050] Third sub-step, determine the ratio between the first user overlap count included in the above-mentioned first user overlap information and the number of institutional users corresponding to the above-mentioned second preset institutional identifier as the second sampling ratio.

[0051] Fourth sub-step: Based on the above first sampling ratio, perform data sampling processing on the first institutional user data group corresponding to the above first preset institutional identifier to obtain a first sampled institutional user data group, and determine the above first sampled institutional user data group and the above second preset institutional identifier as the first to-be-supplemented institutional user data. Among them, the first sampled institutional user data in the above first sampled institutional user data group can be the first institutional user data randomly sampled. First, determine the product of the above first sampling ratio and the number of first institutional users corresponding to the above first preset institutional identifier as the first value. Then, randomly sample the first value of first institutional user data from the first institutional user data group corresponding to the above first preset institutional identifier, and determine each to-be-sampled first institutional user data obtained as the first sampled institutional user data to obtain the first sampled institutional user data group.

[0052] Fifth sub-step: Based on the above second sampling ratio, perform data sampling processing on the first institutional user data group corresponding to the above second preset institutional identifier to obtain a second sampled institutional user data group, and determine the above second sampled institutional user data group and the above first preset institutional identifier as the second to-be-supplemented institutional user data. Among them, the second sampled institutional user data in the above second sampled institutional user data group can be the first institutional user data randomly sampled. First, determine the product of the above second sampling ratio and the number of first institutional users corresponding to the above second preset institutional identifier as the second value. Then, randomly sample the second value of first institutional user data from the first institutional user data group corresponding to the above second preset institutional identifier, and determine each obtained first institutional user data as the second sampled institutional user data to obtain the second sampled institutional user data group.

[0053] Second step: Based on the obtained respective first to-be-supplemented institutional user data and second to-be-supplemented institutional user data, perform update processing on the above first institutional user data group set to obtain an updated first institutional user data group set. For each first institutional user data group in the above first institutional user data group set, perform the following steps:

[0054] First sub-step: Determine the institutional identifier corresponding to the above first institutional user data group as the to-be-supplemented data institutional identifier.

[0055] Second sub-step: Select, from the respective first to-be-supplemented institutional user data, the first to-be-supplemented institutional user data whose included second preset institutional identifier is the same as the above to-be-supplemented data institutional identifier as the target first to-be-supplemented institutional user data to obtain a target first to-be-supplemented institutional user data set.

[0056] The third sub-step is to select, from each piece of second to-be-supplemented institutional user data, the second to-be-supplemented institutional user data that includes the same first preset institutional identifier as the to-be-supplemented data institutional identifier as the target second to-be-supplemented institutional user data, and obtain a target second to-be-supplemented institutional user data set.

[0057] The fourth sub-step is to perform an aggregation process on the above-mentioned target first to-be-supplemented institutional user data set, the above-mentioned target second to-be-supplemented institutional user data set, and the above-mentioned first institutional user data group to obtain an updated first institutional user data group. Among them, first, replace each calling institutional identifier in the above-mentioned target first to-be-supplemented institutional user data set and each calling institutional identifier in the above-mentioned target second to-be-supplemented institutional user data set with the to-be-supplemented data institutional identifier, and determine each replaced target second to-be-supplemented institutional user data and each target second to-be-supplemented institutional user data as updated institutional user data to obtain an updated institutional user data set. Then, determine each of the above-mentioned updated institutional user data and each first institutional user data as updated first institutional user data to obtain an updated first institutional user data group.

[0058] It should be noted that for each institution to be graded, the above-mentioned execution subject can generate an updated first institutional user data group by adding new data to the first institutional user data group corresponding to the institution, which can increase the data sample size corresponding to the institution and facilitate a more comprehensive and accurate grading evaluation of the institution in the subsequent process.

[0059] Step 105: Generate an updated second institutional user data group set based on the second institutional user data set.

[0060] In some embodiments, the above-mentioned execution subject can generate an updated second institutional user data group set based on the above-mentioned second institutional user data set in various ways. Among them, the updated second institutional user data groups in the above-mentioned updated second institutional user data group set can correspond one-to-one to the second institutional user data groups in the above-mentioned second institutional user data group set. The updated second institutional user data groups in the above-mentioned updated second institutional user data group set can be the second institutional user data groups after adding new data.

[0061] In some optional implementation manners of some embodiments, the above-mentioned execution subject can generate an updated second institutional user data group set based on the above-mentioned second institutional user data set through the following steps:

[0062] First step: Classify each piece of second - institution user data in the above - mentioned second - institution user dataset to obtain a second - institution user data group set. Among them, each second - institution user data group in the above - mentioned second - institution user data group set can correspond one - to - one with the preset institution identifiers in the above - mentioned preset institution identifier group. Each second - institution user data group in the above - mentioned second - institution user data group set can be all the second - institution user data corresponding to the same associated institution. The classification process of each piece of second - institution user data in the above - mentioned second - institution user dataset can be carried out according to the associated institution identifier included in the second - institution user data to obtain the second - institution user data group set.

[0063] Second step: Generate a second - institution user identifier group set based on the above - mentioned second - institution user data group set. Among them, each second - institution user identifier group in the above - mentioned second - institution user identifier group set can correspond one - to - one with the preset institution identifiers in the above - mentioned preset institution identifier group. The second - institution user identifier groups in the above - mentioned second - institution user identifier group set can be a set of identifiers of users corresponding to the same associated institution. For each second - institution user data group in the above - mentioned second - institution user data group set, the respective user identifiers included in the above - mentioned second - institution user data group can be determined as a second - institution user identifier group.

[0064] Third step: Determine the second - user overlap information corresponding to each target institution identifier group in the above - mentioned target institution identifier group set. Among them, the above - mentioned second - user overlap information can be information about the degree of overlap of users between the two corresponding associated institutions. For each target institution identifier group in the above - mentioned target institution identifier group set, the following steps can be executed to generate the second - user overlap information:

[0065] First sub - step: Determine the intersection between the two second - institution user identifier groups corresponding to the above - mentioned target institution identifier group as the overlapping second - institution user identifier group.

[0066] Second sub - step: Determine the number of each overlapping second - institution user identifier in the above - mentioned overlapping second - institution user identifier group as the second - user overlap count.

[0067] Third sub - step: Determine the above - mentioned target institution identifier group, the above - mentioned overlapping second - institution user identifier group, and the above - mentioned second - user overlap count as the second - user overlap information.

[0068] Fourth step: Determine the third to - be - supplemented institution user data and the fourth to - be - supplemented institution user data corresponding to each second - user overlap information among the obtained second - user overlap information. Specifically, the following steps can be executed:

[0069] First sub - step: For each preset institution identifier in the above - mentioned preset institution identifier group, execute the following steps:

[0070] Sub-step 1: Select the second institutional user identification group that matches the above-mentioned preset institutional identification from the above-mentioned second institutional user identification group set. Among them, the second institutional user identification group that matches the above-mentioned preset institutional identification may be that the associated institutional identification corresponding to the second institutional user identification group is the same as the above-mentioned preset institutional identification.

[0071] Sub-step 2: Determine the number of each second institutional user identification in the selected second institutional user identification group as the number of second institutional users.

[0072] Second sub-step: For each second user coincidence information among the above-mentioned second user coincidence information, perform the following steps:

[0073] Sub-step 1: Determine the third preset institutional identification and the fourth preset institutional identification corresponding to the above-mentioned second user coincidence information. Among them, first, one preset institutional identification in the target institutional identification group corresponding to the above-mentioned second user coincidence information can be determined as the third preset institutional identification. Then, another preset institutional identification in the target institutional identification group corresponding to the above-mentioned second user coincidence information that is different from the above-mentioned third preset institutional identification is determined as the fourth preset institutional identification.

[0074] Sub-step 2: Determine the ratio between the second user coincidence number included in the above-mentioned second user coincidence information and the number of second institutional users corresponding to the above-mentioned third preset institutional identification as the third sampling ratio.

[0075] Sub-step 3: Determine the ratio between the second user coincidence number included in the above-mentioned second user coincidence information and the number of second institutional users corresponding to the above-mentioned fourth preset institutional identification as the fourth sampling ratio.

[0076] Sub-step 4: Based on the above-mentioned third sampling ratio, perform data sampling processing on the second institutional user data group corresponding to the above-mentioned third preset institutional identification to obtain a third sampled institutional user data group, and determine the above-mentioned third sampled institutional user data group and the above-mentioned fourth preset institutional identification as the third to-be-supplemented institutional user data. Among them, the third sampled institutional user data in the above-mentioned third sampled institutional user data group may be randomly sampled second institutional user data. First, determine the product of the above-mentioned third sampling ratio and the number of second institutional users corresponding to the above-mentioned third preset institutional identification as the third value. Then, randomly sample the third value of second institutional user data from the second institutional user data group corresponding to the above-mentioned third preset institutional identification, and determine each obtained second institutional user data as the third sampled institutional user data to obtain the third sampled institutional user data group.

[0077] Sub-step five: Based on the above fourth sampling ratio, perform data sampling processing on the second institutional user data group corresponding to the above fourth preset institutional identifier to obtain a fourth sampled institutional user data group, and determine the above fourth sampled institutional user data group and the above third preset institutional identifier as the fourth to-be-supplemented institutional user data. Among them, the third sampled institutional user data in the above third sampled institutional user data group can be randomly sampled second institutional user data. First, determine the product of the above fourth sampling ratio and the second institutional user data corresponding to the above fourth preset institutional identifier as the fourth value. Then, randomly sample the fourth value of second institutional user data from the second institutional user data group corresponding to the above fourth preset institutional identifier, and determine each obtained second institutional user data as the fourth sampled institutional user data to obtain a fourth sampled institutional user data group.

[0078] Step five: Based on each obtained third to-be-supplemented institutional user data and each fourth to-be-supplemented institutional user data, perform an update process on the above second institutional user data group set to obtain an updated second institutional user data group set. Among them, the generation steps of the above updated second institutional user data group set can refer to the generation steps of the updated first institutional user data group set in the above step 104 and will not be elaborated here.

[0079] It should be noted that depending on the above second institutional user data set, for each institution to be graded, the above execution entity can also generate an updated second institutional user data group by adding new data to the second institutional user data group corresponding to the institution, so as to increase the data sample size corresponding to the institution for more comprehensive and accurate grading evaluation of the institution in the future.

[0080] Step 106: Generate an institutional grading information set based on the updated first institutional user data group set and the updated second institutional user data group set.

[0081] In some embodiments, the above execution entity can generate an institutional grading information set based on the above updated first institutional user data group set and the above updated second institutional user data group set in various ways. Among them, the above institutional grading information can be information on the value level obtained by classifying institutions. The above value level can represent the risk degree of the institutional credit value. For example, the above value level can be one of the following: level one, level two, level three, level four. Level one can represent a lower risk. Level two can represent a moderate risk. Level three can represent a higher risk. Level four can represent a very high risk. In addition, the value levels decrease gradually from level one to level four in descending order.

[0082] In some alternative implementations of some embodiments, the above-mentioned execution entity may generate an institutional grading information set based on the above-mentioned updated first institutional user data set and the above-mentioned updated second institutional user data set through the following steps:

[0083] First step, determine each non-repeated updated first institutional user data in the above-mentioned updated first institutional user data set as the first user data set to be evaluated.

[0084] Second step, determine each non-repeated updated second institutional user data in the above-mentioned updated second institutional user data set as the second user data set to be evaluated.

[0085] Third step, input each first user data to be evaluated in the above-mentioned first user data set to be evaluated into a pre-trained first user value scoring model in sequence to obtain a first user score information set. Among them, each first user score information in the above-mentioned first user score information set may correspond one-to-one with the first user data to be evaluated in the above-mentioned first user data set to be evaluated. The first user score information in the above-mentioned first user score information set may be information about the score representing the value level of the user corresponding to the first user data to be evaluated. The above-mentioned first user value scoring model may be an integrated model based on logistic regression with the first user data to be evaluated as the input and the first user score information as the output.

[0086] Fourth step, input each second user data to be evaluated in the above-mentioned second user data set to be evaluated into a pre-trained second user value scoring model in sequence to obtain a second user score information set. Among them, each second user score information in the above-mentioned second user score information set may correspond one-to-one with the second user data to be evaluated in the above-mentioned second user data set to be evaluated. The second user score information in the above-mentioned second user score information set may be information about the score representing the value level of the user corresponding to the second user data to be evaluated. The above-mentioned second user value scoring model may be an integrated model based on logistic regression with the second user data to be evaluated as the input and the second user score information as the output. The training method of the above-mentioned second user value scoring model may be the same as that of the above-mentioned first user value scoring model.

[0087] Fifth step, generate an institutional grading information set based on the above-mentioned first user score information set and the above-mentioned second user score information set. Among them, the above-mentioned execution entity may generate an institutional grading information set based on the above-mentioned first user score information set and the above-mentioned second user score information set through various methods.

[0088] It should be noted that in the process of adopting the technical solution to solve the first technical problem proposed in the above-mentioned background art section, there is often another technical problem as follows: how to determine the demand differences of each institution for the storage space capacity to reduce the waste or shortage of storage resources during storage resource allocation. For this problem of the above-mentioned technical problem 2, the conventional solution is generally: through a single index such as the maximum value or the median, according to the respective user scores corresponding to the institutions, determine the level of demand differences between the institutions, and perform resource allocation according to the different demand differences of the institutions. However, the above conventional solution still has the following problems: since the single index can only determine the demand differences of different institutions for the storage space capacity from one dimension, it is difficult to comprehensively identify the demand differences between different institutions. Therefore, when allocating storage resources to each institution, it is easy to cause waste or shortage of the storage resources allocated to the institutions. Therefore, in the face of the above-mentioned technical problem 2, combining the technical advantages of the solution R & D team itself in the field of computer technology, the present disclosure decides to adopt the following solution.

[0089] In some optional implementation manners of some embodiments, the above-mentioned execution subject may generate an institution grading information set based on the above-mentioned first user score information set and the above-mentioned second user score information set through the following steps:

[0090] Step 1, for each preset institution identifier in the above-mentioned preset institution identifier group, perform the following steps:

[0091] Sub-step 1, select the first user score information that matches the above-mentioned preset institution identifier from the above-mentioned first user score information set as the target first user score information, and obtain a target first user score information group. Among them, the first user score information that matches the above-mentioned preset institution identifier may be: the calling institution identifier included in the updated first institution user data corresponding to the first user score information is the same as the above-mentioned preset institution identifier.

[0092] Sub-step 2, select the second user score information that matches the above-mentioned preset institution identifier from the above-mentioned second user score information set as the target second user score information, and obtain a target second user score information group. Among them, the second user score information that matches the above-mentioned preset institution identifier may be: the associated institution identifier included in the updated second institution user data corresponding to the second user score information is the same as the above-mentioned preset institution identifier.

[0093] Sub-step 3: Based on the above-mentioned target first user score information group and the above-mentioned target second user score information group, generate the first user score distribution information and the second user score distribution information. Among them, the above-mentioned first user score distribution information can be information on various indicators representing the distribution of each user score corresponding to the target first user score information group. The above-mentioned various indicators can include but are not limited to the maximum value, the minimum value, the median, the first quartile, and the third quartile. The above-mentioned second user score distribution information can be information on various indicators representing the distribution of each user score corresponding to the target second user score information group. The preset indicator generation interface can be called to generate the first user score distribution information and the second user score distribution information based on the above-mentioned target first user score information group and the above-mentioned target second user score information group. Among them, the above-mentioned preset indicator generation interface can be encapsulated with an indicator generation function. The above-mentioned indicator generation function can generate the values of various indicators corresponding to the input data set according to the input data set.

[0094] Sub-step 4: Input the above-mentioned first user score distribution information into the preset institutional grading decision tree to obtain the first institutional level information. Among them, the above-mentioned institutional grading decision tree can be a decision tree with the user score distribution information as the input and the institutional level information as the output. The above-mentioned user score distribution information can be information on various indicators representing the distribution of each user score. The above-mentioned institutional level information can be information on the value level corresponding to the institution. The above-mentioned institutional level information can be a value level identifier. The above-mentioned value level identifier can be the unique identifier of the value level. The above-mentioned first institutional level information can be the institutional level information corresponding to the above-mentioned first user score distribution information.

[0095] It should be noted that compared with institutions with a low value level, institutions with a high value level can usually provide users with higher-quality products and services, attract more users, and thus generate more user data, which needs to be stored in the storage space allocated by the database server.

[0096] Sub-step 5: Input the above-mentioned second user score distribution information into the above-mentioned institutional grading decision tree to obtain the second institutional level information. Among them, the above-mentioned second institutional level information can be the institutional level information corresponding to the above-mentioned second user score distribution information.

[0097] Sub-step 6: Determine the above-mentioned first institutional level information, the above-mentioned second institutional level information, and the above-mentioned preset institutional identifier as the institutional grading information.

[0098] The above-mentioned steps for generating institutional grading information and its related content, as an inventive point of the embodiments of the present disclosure, solve the above-mentioned second technical problem of "how to determine the differences in the demand for storage space capacity of each institution to reduce resource waste or shortage during storage resource allocation". The conventional solution has the problem of easily causing waste or shortage of institutional storage resources. Among them, the reason for this problem is that since a single indicator can only determine the differences in the demand for storage space capacity of different institutions from one dimension, it is difficult to comprehensively identify the demand differences between different institutions. Therefore, when allocating storage resources to each institution, it is easy to cause waste or shortage of the allocated storage resources. If this solution solves the above problems, it can achieve the effect of reducing waste or shortage of the allocated storage resources of institutions. To achieve this effect, first, for each institution, two user score distribution information can be determined according to the corresponding target first user score information group and target second user score information group, and then input into the institutional grading decision tree to obtain two institutional grade information. Thus, from different dataset perspectives and from the perspective of integrating various indicators, the demand differences for storage space capacity between each institution can be determined more accurately. Then, according to the demand difference levels between different institutions and the current storage space usage information of each institution, it is determined whether each institution needs to expand or contract its capacity. Thus, the more accurate resource adjustment requirements of each institution can be obtained. Finally, according to the more accurate resource requirements, the storage space capacity of each institution is adjusted. Thus, waste or shortage of the allocated storage resources of institutions can be reduced. Therefore, the balance of storage resource allocation is improved.

[0099] Continuing, in the process of adopting the technical solution to solve the above-mentioned first and second technical problems, there is often the following third technical problem: how to determine the occupancy demand of institutions for storage space capacity in a timely manner to reduce storage resource waste. For this problem of the above-mentioned third technical problem, the conventional solution is generally: through complex machine learning methods, user score information representing the occupancy demand of storage resources is generated based on user data, and then the storage resources are allocated according to the user score information. However, the above-mentioned conventional solution still has the following problems: since complex machine learning methods, such as deep neural network models, have complex structures and long running times, when it is necessary to adjust storage resources in real time, it is difficult to generate the user score information of institutions in a timely manner for timely allocation of storage resources. Therefore, in the face of the above-mentioned third technical problem, combining the technical advantages of the solution R & D team in the field of computer technology, the present disclosure decides to adopt the following solution.

[0100] Optionally, before inputting each first to-be-evaluated user data in the above-mentioned first to-be-evaluated user dataset into the pre-trained first user value scoring model to obtain the first user score information set, the above-mentioned execution entity may further perform the following steps:

[0101] First, obtain a sample institutional user dataset. Among them, the sample user data in the above sample user dataset can be the first institutional user data used as a sample. Each sample user data in the above sample user dataset corresponds to a sample label. The above sample label can be 0 or 1. 0 can represent timely repayment. 1 indicates failure to repay on time. The sample institutional user dataset can be obtained from a database.

[0102] Second, preprocess each sample user data in the above sample user dataset to obtain a preprocessed sample user dataset. Among them, the above preprocessed sample user dataset can be a sample user dataset after encoding text variables. For each sample user data in the above sample user dataset, a preset data encoding interface can be called to preprocess the above sample user data to obtain preprocessed sample user data. Among them, the above preset data encoding interface can encapsulate a data encoding function. The above data encoding function can perform type detection and encoding on the input data including each variable. The above type detection can be to detect whether the variable is a text variable. The above encoding can be one-hot encoding for text variables with data categories between 2 and 8, and label encoding for text variables with data categories greater than 10.

[0103] Third, perform an indiscriminate feature filtering process on the above preprocessed sample user dataset to obtain a filtered sample user dataset. Among them, the above filtered sample user dataset can be a preprocessed sample user dataset after filtering out feature variables with variances less than a preset variance value. The above preset variance value can be a lower limit value of the variance set in advance. For example, the above preset variance value can be 0.010. The above preprocessed sample user dataset can be subjected to an indiscriminate feature filtering process through a variance filtering method to obtain a filtered sample user dataset.

[0104] Fourth, perform a feature selection process on the above filtered sample user dataset to obtain a target sample user dataset. Among them, the above target sample user dataset can be composed of the selected feature variables in the filtered sample user dataset. The above filtered sample user dataset can be subjected to a feature selection process through an XGBoost (Extreme Gradient Boosting) model to obtain a target sample user dataset.

[0105] Step 5: Classify the above target sample user dataset to obtain a first sample user dataset and a second sample user dataset. Among them, the first sample user data in the above first sample user dataset can be the target sample user data with the corresponding sample label being 0. The second sample user data in the above second sample user dataset can be the target sample user data with the corresponding sample label being 1. The above target sample user dataset can be classified according to the sample label corresponding to the target sample user data to obtain a first sample user dataset and a second sample user dataset.

[0106] Step 6: Generate a sample size ratio based on the above first sample user dataset and the above second sample user dataset. Among them, the above sample size ratio can be the ratio of the number of first sample user data to the number of second sample user data. First, determine the number of each first sample user data in the above first sample user dataset as the first sample size. Then, determine the number of each second sample user data in the above second sample user dataset as the second sample size. Finally, determine the ratio of the above first sample size to the above second sample size as the sample size ratio.

[0107] Step 7: In response to determining that the above sample size ratio is less than a preset sample size ratio threshold, perform an update process on the above target sample user dataset to obtain an updated target sample user dataset. Among them, the above preset sample size ratio threshold can be the lower limit value of the above sample size ratio. The above updated target sample user dataset can be the target sample user dataset after adding newly generated target sample user data. First, the Synthetic Minority Over-sampling Technique (SMOTE) sampling method can be used to generate a synthetic sample user dataset based on the above second sample user dataset. Then, each synthetic sample user data in the above synthetic sample user dataset is used as target sample user data and added to the above target sample user dataset to obtain an updated target sample user dataset.

[0108] Step 8: Use the above updated target sample user dataset as a training sample set, and construct a binary tree set based on the above training sample set. Among them, each leaf node corresponding to the above binary tree set corresponds to at least one training sample. Each binary tree in the above binary tree set can be constructed by performing feature splitting through the gain of the structural score for the training sample set. A binary tree can be generated by performing feature splitting through the gain of the structural score according to the above training sample set. When the binary tree reaches the maximum depth or there is no gain in the structural score, a new tree is added, and the training is based on the fitting residuals of the previous tree.

[0109] Step 9: For each leaf node corresponding to the above binary tree set, perform logistic regression training on at least one training sample corresponding to the leaf node to obtain a logistic regression model.

[0110] Step 10: Based on the obtained logistic regression models, predict each training sample in the above training sample set to obtain a set of training sample prediction values. Among them, the training sample prediction values in the above set of training sample prediction values correspond one by one to the training samples in the training sample set. The training sample prediction values in the above set of training sample prediction values can be the average of the respective prediction results of the corresponding training samples. The prediction results in the above respective prediction results can be the results of predicting the training samples by the logistic regression model corresponding to the leaf node to which the training sample belongs. The prediction results in the above respective prediction results correspond one by one to the binary trees in the binary tree set. For each training sample in the above training sample set, each binary tree can be traversed to find the leaf node to which the training sample belongs, and the logistic regression model corresponding to the leaf node can be used to predict to obtain a prediction result, and the result after averaging the prediction results of each binary tree is determined as the training sample prediction value.

[0111] Step 11: Based on the above set of training sample prediction values, perform logistic regression training using a preset logistic regression iterative algorithm to obtain a first user value scoring model. Among them, the above logistic regression iterative algorithm can be an Adaboost (Adaptive Boosting) iterative algorithm based on a logistic regression base model.

[0112] As an example, first, the above execution entity can use the above set of training sample prediction values as a target training sample set, and initialize the weights of all target training samples. Then, in each round of iteration, the current weights are used to train a logistic regression model, and the model error and model weights are calculated, and the weights of all target training samples are updated to start the next round of iteration. Finally, after multiple rounds of iteration, according to the model weights corresponding to the logistic regression models in each round, the logistic regression models in each round are weighted and combined to obtain a first user value scoring model. By updating the weights of all target training samples in each round, the weights of the target training samples with classification errors can be increased, and the weights of the target training samples with correct classification can be decreased.

[0113] The above-mentioned first user value scoring model training steps and their related content are an inventive point of the embodiments of the present disclosure, which solve the above-mentioned technical problem three "how to determine the occupancy demand of an institution for storage space capacity and reduce storage resource waste in a timely manner". The conventional solution has the problem of being difficult to reduce storage resource waste in a timely manner, and the reason for this problem is that: due to complex machine learning methods such as deep neural network models having complex structures and long running times, when it is necessary to adjust storage resources in real time, it is difficult to generate user score information of institutions in a timely manner for timely allocation of storage resources. Thus, it is difficult to reduce storage resource waste in a timely manner. If this solution solves the above problems, the effect of timely reducing storage resource waste can be achieved. To achieve this effect, first, preprocess and screen features of the sample user data set. Thus, a target sample user data set after encoding including various feature variables that have a greater impact on the prediction result can be obtained. Secondly, detect whether there is data imbalance in the target sample user data set, and perform data supplementation for the data imbalance to obtain an updated target sample user data set. Thus, the distribution of the updated target sample user data set can be made balanced, which is convenient for improving the accuracy and reliability of the model. Then, use the gain of the structure score to split the tree, and form multiple binary trees by fitting the residuals. Next, for each leaf node of each tree, use logistic regression for training. Thus, various logistic regression models can be obtained. Then, according to each logistic regression model, determine the prediction value result of each training sample. After that, according to each prediction value result, use the Adaboost algorithm to iteratively train the logistic regression base model to obtain the first user value scoring model. Thus, a first user value scoring model with a relatively simple structure can be obtained, and thus, the running duration of the model can be shortened, and user score information can be generated in a timely manner for timely generating institution classification information representing the occupancy demand of the storage capacity. Finally, the user score information of each institution can be used in a timely manner to adjust the storage resources of each institution. Thus, storage resource waste can be reduced in a timely manner. Also because the above-mentioned first user value scoring model integrates multiple logistic regression base models, thus, the interpretability of the model is also relatively strong. Also because the model is trained in an ensemble learning manner, furthermore, the accuracy of the model is also relatively high.

[0114] In practice, when the amount of original sample data corresponding to the current institution is insufficient, through the user overlap information between institutions and combined with the sample data of other institutions with similar features, the amount of original sample data corresponding to the current institution can be supplemented. Thus, the amount of sample data corresponding to the current institution can be made sufficient without spending a long time accumulating the amount of data. Also because two sample data sets of different source types are also used to generate institution classification information, thus, classification information of different data dimensions of each institution can be obtained.

[0115] Step 107: Detect and process each institutional storage space usage information in the pre-acquired institutional storage space usage information set to obtain an institutional available storage space information set.

[0116] In some embodiments, the above-mentioned execution entity can detect and process each institutional storage space usage information in the pre-acquired institutional storage space usage information set in various ways to obtain an institutional available storage space information set. Among them, the institutional storage space usage information in the institutional storage space usage information set can be the information of the storage space allocated to the corresponding institution. The institutional available storage space information in the institutional available storage space information set can be the information of the allocation status when the corresponding institution participates in the storage space capacity allocation. The above-mentioned allocation status can be one of the following: expansion status and reduction status. The above-mentioned expansion status can represent the expansion of the storage space. The above-mentioned reduction status can represent the reduction of the storage space.

[0117] In some optional implementation manners of some embodiments, each institutional storage space usage information in the institutional storage space usage information set may include an institutional identifier, an allocated storage capacity, and a used storage capacity. Among them, the above-mentioned allocated storage capacity can be the capacity of the storage space pre-allocated to the institution. The above-mentioned used storage capacity can be the storage capacity used when storing institutional user data. The above-mentioned execution entity can detect and process each institutional storage space usage information in the pre-acquired institutional storage space usage information set through the following steps to obtain an institutional available storage space information set:

[0118] For each institutional storage space usage information in the institutional storage space usage information set, perform the following steps:

[0119] First step: Determine the ratio of the used storage capacity included in the above-mentioned institutional storage space usage information to the allocated storage capacity as the used ratio.

[0120] Second step: In response to determining that the above-mentioned used ratio is less than the first preset used space ratio, obtain the historical storage space usage data sequence corresponding to the above-mentioned institutional storage space usage information. Among them, the above-mentioned first preset used space ratio can be the lower limit value of the used ratio set in advance. For example, the above-mentioned first preset used space ratio can be 0.5. The above-mentioned historical storage space usage data sequence can correspond to the same institution as the above-mentioned institutional storage space usage information. The above-mentioned historical storage space usage data sequence can be a sequence of historical storage space usage data arranged in chronological order. The above-mentioned historical storage space usage data can include, but is not limited to, the storage date and the used capacity.

[0121] Step 3: Input the above historical storage space usage data sequence into a preset storage space usage prediction model to obtain a storage space usage prediction data sequence. Among them, the above storage space usage prediction model can be a time series prediction model that takes the historical storage space usage data sequence as input and the storage space usage prediction data sequence as output. For example, the above storage space usage prediction model can be, but is not limited to, one of the following: autoregressive integrated moving average model, long short-term memory network. The above storage space usage prediction data sequence can be a sequence of daily storage space usage in a future time period. The above future time period can be one month. The storage space usage prediction data in the above storage space usage prediction data sequence can include the storage date and the estimated usage capacity. The above estimated usage capacity can be the predicted daily storage space usage.

[0122] Step 4: Generate a target usage capacity based on the used storage capacity included in the above institutional storage space usage information and the above storage space usage prediction data sequence. Among them, the above target usage capacity can be the total predicted storage space usage one month later. The sum of the used storage capacity included in the above institutional storage space usage information and the respective estimated usage capacities included in the above storage space usage prediction data sequence can be determined as the target usage capacity.

[0123] Step 5: Determine the difference between the allocated storage capacity included in the above institutional storage space usage information and the above target usage capacity as the adjustable space capacity.

[0124] Step 6: In response to determining that the above adjustable space capacity is greater than a preset new capacity threshold, determine the institutional identifier, the above adjustable space capacity, and a preset shrinkage identifier included in the above institutional storage space usage information as institutional adjustable storage space information. Among them, the above preset new capacity threshold can be the lower limit of the newly added storage capacity for institutions at different levels set in advance. For example, the above preset new capacity threshold can be 5G. The above preset shrinkage identifier can indicate that the storage space of the corresponding institution can be shrunk.

[0125] Optionally, the above execution entity can also, in response to determining that the above used ratio is greater than a second preset used space ratio, determine the institutional identifier and a preset expansion identifier included in the above institutional storage space usage information as institutional adjustable storage space information. Among them, the above second preset used space ratio can be the upper limit of the used ratio set in advance. For example, the above second preset used space ratio can be 0.95. The above preset expansion identifier can indicate that the storage space of the corresponding institution needs to be expanded.

[0126] Step 108: Generate an institutional expansion information set and an institutional shrinkage information set based on the institutional classification information set and the institutional adjustable storage space information set.

[0127] In some embodiments, the above-mentioned execution entity can, in various ways, generate an organization expansion information set and an organization reduction information set based on the above-mentioned organization hierarchical information set and the above-mentioned organization's allocable storage space information set. Among them, the organization expansion information in the above-mentioned organization expansion information set can be information on the storage capacity to be expanded for the corresponding organization. The organization reduction information in the above-mentioned organization reduction information set can be information on the storage capacity to be reduced for the corresponding organization. Specifically, the following steps can be executed:

[0128] First step, for each piece of organization hierarchical information in the above-mentioned organization hierarchical information set, execute the following steps:

[0129] First sub-step, in response to determining that the value level corresponding to the first organization level information included in the above-mentioned organization hierarchical information is higher than the value level corresponding to the second organization level information included, determine the second organization level information included in the above-mentioned organization hierarchical information as the target organization level information.

[0130] Second sub-step, in response to determining that the value level corresponding to the first organization level information included in the above-mentioned organization hierarchical information is lower than or equal to the value level corresponding to the second organization level information included, determine the first organization level information included in the above-mentioned organization hierarchical information as the target organization level information.

[0131] Second step, group the various pieces of organization's allocable storage space information in the above-mentioned organization's allocable storage space information set to obtain a first organization's allocable storage space information group and a second organization's allocable storage space information group. Among them, the first organization's allocable storage space information in the above-mentioned first organization's allocable storage space information group can be the organization's allocable storage space information including a preset expansion identifier. The second organization's allocable storage space information in the above-mentioned second organization's allocable storage space information group can be the organization's allocable storage space information including a preset reduction identifier.

[0132] Third step, through a preset sorting algorithm, according to the organization hierarchical information set, sort the various pieces of first organization's allocable storage space information in the above-mentioned first organization's allocable storage space information group in descending order to obtain a first organization's allocable storage space information sequence. Among them, the above-mentioned first organization's allocable storage space information sequence can be a set composed of the various pieces of first organization's allocable storage space information sorted in descending order according to the level of the corresponding organization. For example, the above-mentioned sorting algorithm can be, but is not limited to, one of the following: bubble sort, quick sort.

[0133] Fourthly, through the above sorting algorithm, according to the size of the adjustable space capacity, sort the second - institution adjustable storage space information in the second - institution adjustable storage space information group in descending order to obtain the second - institution adjustable storage space information sequence as the capacity - reduction allocation storage space information sequence.

[0134] Fifthly, initialize the count value of the pre - designed counter to obtain the target count value. Among them, the above - mentioned target count value can be initially set to 1.

[0135] Sixthly, based on the above - mentioned first - institution adjustable storage space information sequence, target count value, and capacity - reduction allocation storage space information sequence, execute the following steps for generating the institution expansion information set and institution capacity - reduction information set:

[0136] The first sub - step: Determine the first - institution adjustable storage space information with the same serial number as the above - mentioned target count value in the above - mentioned first - institution adjustable storage space information sequence as the target - institution adjustable storage space information.

[0137] The second sub - step: Select the target - institution level information whose corresponding institution identifier is the same as the institution identifier included in the above - mentioned target - institution adjustable storage space information from the obtained various target - institution level information as the to - be - expanded institution level information.

[0138] The third sub - step: Select, in sequence, each capacity - reduction allocation storage space information from the above - mentioned capacity - reduction allocation storage space information sequence whose corresponding target - institution level information meets the preset level condition to obtain at least one capacity - reduction allocation storage space information. Among them, the above - mentioned preset level condition can be that the value level corresponding to the capacity - reduction allocation storage space information is lower than the value level corresponding to the to - be - expanded institution level information.

[0139] The fourth sub - step: Select the hierarchical resource proposed configuration information that matches the above - mentioned to - be - expanded institution level information from the preset hierarchical resource proposed configuration information set. Each hierarchical resource proposed configuration information in the above - mentioned hierarchical resource proposed configuration information set includes a value level identifier and a proposed configuration resource quantity. The above - mentioned proposed configuration resource quantity can be the capacity of the storage resource allocated each time. Matching with the above - mentioned to - be - expanded institution level information can be that the value level identifier corresponding to the hierarchical resource proposed configuration information is the same as the value level identifier corresponding to the above - mentioned to - be - expanded institution level information.

[0140] The fifth sub-step is to generate an expansion resource quantity and a shrinkage organization resource information group based on the selected hierarchical resource proposed configuration information and the above-mentioned at least one shrinkage deployment storage space information through a preset resource matching interface. Among them, the above-mentioned expansion resource quantity can be the storage space capacity to be used for expansion. Each shrinkage organization resource information in the above-mentioned shrinkage organization resource information group can include a shrinkage organization identifier and a shrinkage resource quantity. The above-mentioned shrinkage organization identifier can be the identifier of the organization to be shrunk. The above-mentioned shrinkage resource quantity can be the storage space capacity to be used for shrinkage. The sum of the respective shrinkage resource quantities included in the above-mentioned shrinkage organization resource information group is greater than or equal to the above-mentioned expansion resource quantity. The above-mentioned resource matching interface can be encapsulated with a resource matching function. The above-mentioned resource matching function can determine the expansion resource quantity and the shrinkage organization resource information group based on the input hierarchical resource proposed configuration information and the above-mentioned at least one shrinkage deployment storage space information.

[0141] As an example, when the proposed configuration resource quantity included in the hierarchical resource proposed configuration information is 20 and the above-mentioned at least one shrinkage deployment storage space information is {"Organization 1": 15, "Organization 2": 10, "Organization 3": 6, "Organization 4": 4}, the above-mentioned expansion resource quantity can be 21, and the shrinkage organization resource information group can be {"Organization 1": 15, "Organization 3": 6}. In addition, when the proposed configuration resource quantity is 40 and the above-mentioned at least one shrinkage deployment storage space information remains unchanged, the above-mentioned expansion resource quantity can be 40, and the shrinkage organization resource information group can be {"Organization 1": 15, "Organization 2": 10, "Organization 3": 6, "Organization 4": 4, "Database Server": 5}, where {"Database Server": 5} indicates that the database server re-partitions a new storage space for allocation.

[0142] The sixth sub-step is to delete each shrinkage deployment storage space information in the above-mentioned shrinkage deployment storage space information sequence that matches the above-mentioned shrinkage organization resource information group to obtain a post-deletion shrinkage deployment storage space information sequence. Among them, matching the above-mentioned shrinkage organization resource information group can mean that the organization identifier corresponding to the shrinkage deployment storage space information is the same as the shrinkage organization identifier included in any shrinkage organization resource information.

[0143] The seventh sub-step is to determine the organization identifier included in the above-mentioned target organization's adjustable storage space information and the above-mentioned expansion resource quantity as organization expansion information.

[0144] In the eighth sub-step, in response to determining that the above-mentioned target count value is equal to or greater than the pre-set count value, each obtained organization expansion information is determined as an organization expansion information set, and each organization reduction resource information in each obtained organization reduction resource information group is associated with the target expansion organization identifier to be determined as organization reduction information, thereby obtaining an organization reduction information set. Among them, the above-mentioned pre-set count value may be the number of each first organization's allocable storage space information in the above-mentioned first organization's allocable storage space information group. The above-mentioned target expansion organization identifier may be the organization identifier included in the target organization's allocable storage space information corresponding to the organization reduction resource information.

[0145] Optionally, in response to determining that the above-mentioned target count value is less than the above-mentioned pre-set count value, the target count value is updated to obtain an updated count value, and the updated count value is used as the target count value, and the above-mentioned reduced deployment storage space information sequence after deletion is used as the deployment storage space information sequence for reduction, and the above-mentioned steps for generating the organization expansion information set and the organization reduction information set are executed again. Among them, the sum of the above-mentioned target count value and 1 may be determined as the updated count value.

[0146] Step 109: Based on the organization expansion information set and the organization reduction information set, control the associated database server to adjust the storage space capacity of each organization.

[0147] In some embodiments, the above-mentioned execution entity may control the associated database server to adjust the storage space capacity of each organization based on the above-mentioned organization expansion information set and the above-mentioned organization reduction information set. Among them, for each expansion organization corresponding to the above-mentioned organization expansion information set, the above-mentioned database server may expand the storage space capacity of the above-mentioned expansion organization according to each organization reduction information corresponding to the above-mentioned expansion organization. The above-mentioned database server may also, for each reduction organization corresponding to the above-mentioned organization reduction information set, reduce the storage space capacity of the above-mentioned reduction organization according to the organization reduction information corresponding to the above-mentioned reduction organization.

[0148] It should be added that the resource allocation method of the present disclosure can not only be used to balance the database storage space of each organization, but also be adaptively used to adjust the bandwidth resources of the access servers of each organization and the financial quotas applicable to each organization, so as to achieve the balance of bandwidth resources and financial quota resources and reduce the waste and over-occupation of resources.

[0149] The above embodiments of the present disclosure have the following beneficial effects: Through the resource allocation method of some embodiments of the present disclosure, the excessive occupation and waste of storage resources can be reduced. Specifically, the reasons for the waste or excessive occupation of storage resources are as follows: Due to the differences among the user groups of each institution, there are also significant differences in the storage space capacity required by each institution. When the storage space capacity required by an institution is small and there is a lot of unused storage space, it is easy to cause waste of storage resources; when the storage space required by an institution is large and the storage space capacity is insufficient, if the above-mentioned re-partitioning method is used to expand the storage space, it will lead to excessive occupation of storage resources. Based on this, in the resource allocation method of some embodiments of the present disclosure, first, in response to confirming that the database storage space for storing institutional user data is insufficient, a database storage space adjustment request is sent to the server. Secondly, in response to receiving the storage space adjustment instruction sent by the above server, a first institutional user data set and a second institutional user data set are obtained. Among them, each first institutional user data in the above first institutional user data set is log data when the institution calls a preset business product with user information as the query primary key, and each second institutional user data in the above second institutional user data set is user behavior label data collected by the institution. Thus, two sample data sets of different source types corresponding to each institution can be obtained. Then, based on the above first institutional user data set, the first user coincidence information corresponding to each target institutional identification group in the target institutional identification group set is determined, where each target institutional identification group in the above target institutional identification group set is composed of two different preset institutional identifications in the preset institutional identification group. Thus, the information on the coincidence of user groups between different institutions corresponding to the product call log records can be determined. After that, based on the obtained first user coincidence information, the above first institutional user data set is updated to obtain an updated first institutional user data set. Thus, according to the user coincidence sample data between different institutions, combined with the original sample data of each existing institution, the original sample data corresponding to each institution can be supplemented with samples to make up for the shortage of the original sample data volume. Next, based on the above second institutional user data set, an updated second institutional user data set is generated. Thus, the original sample data corresponding to the institution from the user behavior can be supplemented with samples to make up for the shortage of the original sample data volume. Then, based on the above updated first institutional user data set and the above updated second institutional user data set, an institutional classification information set is generated. Thus, the level information of each institution can be obtained. Then, the detection process is performed on each institutional storage space usage information in the pre-obtained institutional storage space usage information set to obtain an institutional adjustable storage space information set. Thus, the adjustable state of the current storage space capacity of each institution can be obtained.Next, based on the above institutional classification information set and the above institutional adjustable storage space information set, an institutional expansion information set and an institutional reduction information set are generated. Thus, the resource information of each institution to be expanded and the resource information of each institution to be reduced can be obtained. Finally, based on the above institutional expansion information set and the above institutional reduction information set, the associated database server is controlled to adjust the storage space capacity of each institution. Therefore, in the resource allocation method of some embodiments of the present disclosure, when the storage space of institutional user data is insufficient, the level of the institution is determined according to the customer group information of the institution, and on this basis, according to the institutional classification information and the current resource adjustable state of the institution, the institutional expansion information set and the institutional reduction information set are determined, and institutions with less storage space capacity and more unused storage space can be reduced in capacity, and the reduced capacity can be used to expand the storage space of institutions that require more storage space and have insufficient storage space capacity. Thus, excessive occupation and waste of storage resources can be reduced.

[0150] Further referring to Figure 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a resource allocation device. These device embodiments correspond to Figure 1 the method embodiments shown, and the resource allocation device 200 can be specifically applied to various electronic devices.

[0151] As Figure 2As shown, the resource allocation device 200 of some embodiments includes: a sending unit 201, an obtaining unit 202, a determining unit 203, an updating processing unit 204, a first generating unit 205, a second generating unit 206, a detecting processing unit 207, a third generating unit 208, and a control unit 209. Among them, the sending unit 201 is configured to send a database storage space adjustment request to the server in response to confirming that the database storage space for storing institutional user data is insufficient; the obtaining unit 202 is configured to obtain a first institutional user data set and a second institutional user data set in response to receiving the storage space adjustment instruction sent by the above server, wherein each first institutional user data in the above first institutional user data set is log data when the institution calls a preset business product with user information as a query primary key, and each second institutional user data in the above second institutional user data set is user behavior tag data collected by the institution; the determining unit 203 is configured to determine the first user coincidence information corresponding to each target institutional identification group in the target institutional identification group set based on the above first institutional user data set, wherein each target institutional identification group in the above target institutional identification group set is composed of two different preset institutional identifications in the preset institutional identification group; the updating processing unit 204 is configured to perform an updating process on the above first institutional user data group set based on the obtained first user coincidence information to obtain an updated first institutional user data group set; the first generating unit 205 is configured to generate an updated second institutional user data group set based on the above second institutional user data set; the second generating unit 206 is configured to generate an institutional grading information set based on the above updated first institutional user data group set and the above updated second institutional user data group set; the detecting processing unit 207 is configured to perform a detecting process on each institutional storage space usage information in the pre-obtained institutional storage space usage information set to obtain an institutional adjustable storage space information set; the third generating unit 208 is configured to generate an institutional expansion information set and an institutional contraction information set based on the above institutional grading information set and the above institutional adjustable storage space information set; the control unit 209 is configured to control the associated database server to adjust the storage space capacity of each institution based on the above institutional expansion information set and the above institutional contraction information set.

[0152] It can be understood that the various units described in this resource allocation device 200 correspond to the respective steps in the method described in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the resource allocation device 200 and the units included therein, and will not be repeated here.

[0153] Further referring to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0154] As Figure 3 shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may 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 through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0155] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or include all the shown devices. Instead, more or fewer devices may be implemented or included. Figure 3 Each block shown in

[0156] may represent a device or, as needed, multiple devices.

[0157] It should be noted that, in some embodiments of the present disclosure, the above-mentioned computer-readable medium may be a computer-readable signal medium, 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, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, 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, which carries computer-readable program code. Such a propagated data signal may take various 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 the computer-readable storage medium, which can 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.

[0158] 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 digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed network.

[0159] The above computer-readable medium may be included in the above device; or it may exist separately and not be assembled into the electronic device. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: in response to confirming that the database storage space for storing institutional user data is insufficient, send a database storage space adjustment request to the server; in response to receiving the storage space adjustment instruction sent by the above server, obtain a first institutional user data set and a second institutional user data set, wherein each first institutional user data in the above first institutional user data set is log data when the institution calls a preset business product using user information as a query primary key, and each second institutional user data in the above second institutional user data set is user behavior label data collected by the institution; based on the above first institutional user data set, determine the first user coincidence information corresponding to each target institutional identification group in the target institutional identification group set, wherein each target institutional identification group in the above target institutional identification group set is composed of two different preset institutional identifications in the preset institutional identification group; based on the obtained respective first user coincidence information, perform an update process on the above first institutional user data group set to obtain an updated first institutional user data group set; based on the above second institutional user data set, generate an updated second institutional user data group set; based on the above updated first institutional user data group set and the above updated second institutional user data group set, generate an institutional grading information set; perform a detection process on each institutional storage space usage information in the pre-obtained institutional storage space usage information set to obtain an institutional adjustable storage space information set; based on the above institutional grading information set and the above institutional adjustable storage space information set, generate an institutional expansion information set and an institutional contraction information set; based on the above institutional expansion information set and the above institutional contraction information set, control the associated database server to adjust the storage space capacity of each institution.

[0160] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In 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., by connecting through the Internet using an Internet service provider).

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0162] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes: a sending unit, an obtaining unit, a determining unit, an updating processing unit, a first generating unit, a second generating unit, a detecting processing unit, a third generating unit, and a control unit. Among them, the names of these units do not constitute a limitation on the unit itself in some cases. For example, the sending unit can also be described as "the unit that sends a database storage space adjustment request to the server".

[0163] The functions described above can be at least partially performed by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0164] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and 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, technical solutions formed by mutually replacing the above features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A resource allocation method, comprising: In response to confirming that the database storage space for storing the organization user data is insufficient, sending a database storage space adjustment request to the server; In response to receiving the storage space adjustment instruction sent by the server, a first institution user data set and a second institution user data set are obtained, wherein each first institution user data in the first institution user data set is log data when the institution uses user information as a query primary key to call a preset business product, and each second institution user data in the second institution user data set is user behavior label data collected by the institution; Based on the first institution user data set, determining first user overlap information corresponding to each target institution identification group in the target institution identification group set, wherein each target institution identification group in the target institution identification group set is composed of two different preset institution identifications in the preset institution identification group; Based on the obtained overlap information of each first user, the first institution user data set is updated to obtain an updated first institution user data set; Based on the second institution user data set, generating and updating a second institution user data set; generating an organization classification information set based on the updated first organization user data set and the updated second organization user data set; Detect and process the storage space usage information of each institution in the pre-acquired storage space usage information set of the institution to obtain the storage space information set that can be allocated by the institution; Generate an organization expansion information set and an organization reduction information set based on the organization classification information set and the organization deployable storage space information set; Based on the organization expansion information set and the organization reduction information set, the associated database server is controlled to adjust the storage space capacity of each organization.

2. The method according to claim 1, wherein: The determining, based on the first institution user data set, first user coincidence information corresponding to each target institution identification group in the target institution identification group set includes: Classify and process each first institution user data in the first institution user data set to obtain a first institution user data group set, wherein each first institution user data group in the first institution user data group set corresponds to a preset institution identifier in the preset institution identifier group; Based on the first institution user data group set, generating a total number of users and a first institution user identification group set, wherein each first institution user identification group in the first institution user identification group set corresponds to a preset institution identification in the preset institution identification group; For each preset institution identifier in the preset institution identifier group, perform the following steps: Selecting a first institution user identification group that matches the preset institution identification from the first institution user identification group set; Determine the number of each first institution user identifier in the selected first institution user identifier group as the number of first institution users; Determine the ratio of the number of users of the first institution to the total number of users as the proportion of institutional users; In response to determining that the proportion of users of each institution corresponding to the preset institution identification group meets the preset user proportion condition, performing grouping processing on the preset institution identifications in the preset institution identification group in pairs to obtain a target institution identification group set; For each target institution identification group in the target institution identification group set, matching processing is performed on two first institution user identification groups corresponding to the target institution identification group to obtain first user overlap information.

3. The method according to claim 2, wherein: Each piece of first user overlap information obtained includes a first user overlap number; and the first institution user data set is updated based on each piece of first user overlap information obtained to obtain an updated first institution user data set, including: For each piece of first user coincidence information obtained, perform the following steps: Determine a first preset institution identifier and a second preset institution identifier corresponding to the first user coincidence information; Determine a first sampling ratio as a ratio between the number of first user overlaps included in the first user overlap information and the number of institution users corresponding to the first preset institution identifier; Determine a ratio between the number of first user overlaps included in the first user overlap information and the number of institution users corresponding to the second preset institution identifier as a second sampling ratio; Based on the first sampling ratio, data sampling processing is performed on the first institution user data group corresponding to the first preset institution identifier to obtain a first sampled institution user data group, and the first sampled institution user data group and the second preset institution identifier are determined as first institution user data to be supplemented; Based on the second sampling ratio, data sampling processing is performed on the second institution user data group corresponding to the second preset institution identifier to obtain a second sampled institution user data group, and the second sampled institution user data group and the first preset institution identifier are determined as second institution user data to be supplemented; Based on the obtained first institution user data to be supplemented and the obtained second institution user data to be supplemented, the first institution user data set is updated to obtain an updated first institution user data set.

4. The method according to any one of claims 1 to 3, wherein: The step of generating and updating the second institution user data set based on the second institution user data set includes: Classify each second institution user data in the second institution user data set to obtain a second institution user data set; Based on the second institution user data set, generating a second institution user identification set, wherein each second institution user identification set in the second institution user identification set corresponds to a preset institution identification in the preset institution identification set; Determine the second user overlap information corresponding to each target organization identification group in the target organization identification group set; Determine the third to-be-supplemented institution user data and the fourth to-be-supplemented institution user data corresponding to each second user overlap information in each obtained second user overlap information; Based on the obtained third institution user data to be supplemented and the fourth institution user data to be supplemented, the second institution user data set is updated to obtain an updated second institution user data set.

5. The method according to claim 1, wherein: The step of generating an organization classification information set based on the updating of the first organization user data set and the updating of the second organization user data set comprises: Determine each non-repetitive updated first institution user data in the updated first institution user data set as a first user data set to be evaluated; Determine each non-repetitive updated second institution user data in the updated second institution user data set as a second user data set to be evaluated; Input each first user data to be evaluated in the first user data set to be evaluated into a pre-trained first user value scoring model to obtain a first user score information set, wherein each first user score information in the first user score information set corresponds to the first user data to be evaluated in the first user data set to be evaluated; Input each second user data to be evaluated in the second user data set to be evaluated into a pre-trained second user value scoring model to obtain a second user score information set, wherein each second user score information in the second user score information set corresponds to the second user data to be evaluated in the second user data set to be evaluated; An institution rating information set is generated based on the first user score information set and the second user score information set.

6. The method according to claim 1, wherein: Each piece of organization storage space usage information in the organization storage space usage information set includes an organization identifier, allocated storage capacity, and used storage capacity; And the detection and processing of each organization storage space usage information in the pre-acquired organization storage space usage information set to obtain the organization deployable storage space information set includes: For each organization storage space usage information in the organization storage space usage information set, perform the following steps: Determine the ratio of the used storage capacity to the allocated storage capacity included in the storage space usage information of the organization as the used percentage; In response to determining that the used percentage is less than a first preset used space percentage, obtaining a historical storage space usage data sequence corresponding to the organization storage space usage information; Inputting the historical storage space usage data sequence into a preset storage space usage prediction model to obtain a storage space usage prediction data sequence; Generate a target usage capacity based on the used storage capacity included in the storage space usage information of the organization and the storage space usage prediction data sequence; Determine the difference between the allocated storage capacity included in the storage space usage information of the organization and the target usage capacity as the available space capacity; In response to determining that the adjustable space capacity is greater than a preset new capacity threshold, the organization identifier, the adjustable space capacity and a preset reduction identifier included in the organization storage space usage information are determined as organization adjustable storage space information.

7. The method according to claim 6, wherein: The method further comprises: In response to determining that the used percentage is greater than a second preset used space percentage, the organization identifier and the preset expansion identifier included in the organization storage space usage information are determined as organization allocable storage space information.

8. A resource allocation device, comprising: A sending unit, configured to send a database storage space adjustment request to the server in response to confirming that the database storage space for storing the organization user data is insufficient; an acquisition unit, configured to acquire a first institution user data set and a second institution user data set in response to receiving a storage space adjustment instruction sent by the server, wherein each first institution user data in the first institution user data set is log data when the institution uses user information as a query primary key to call a preset business product, and each second institution user data in the second institution user data set is user behavior label data collected by the institution; A determination unit is configured to determine, based on the first institution user data set, first user overlap information corresponding to each target institution identification group in the target institution identification group set, wherein each target institution identification group in the target institution identification group set is composed of two different preset institution identifications in the preset institution identification group; An update processing unit is configured to perform update processing on the first institution user data set based on the obtained overlap information of each first user to obtain an updated first institution user data set; A first generating unit is configured to generate an updated second institution user data set based on the second institution user data set; a second generating unit configured to generate an institution rating information set based on the updated first institution user data set and the updated second institution user data set; A detection and processing unit is configured to detect and process each organization storage space usage information in the pre-acquired organization storage space usage information set to obtain an organization deployable storage space information set; A third generating unit is configured to generate an organization expansion information set and an organization reduction information set based on the organization classification information set and the organization allocatable storage space information set; The control unit is configured to control the associated database server to adjust the storage space capacity of each organization based on the organization expansion information set and the organization reduction information set.

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

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

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