A kind of achievement database management system

Abstract

The invention relates to the technical field of achievement database, and discloses an achievement database management system, wherein a screening module screens achievement data to be stored and constructs an achievement data set; a determination module determines the category of achievement data to be stored in the achievement data set and constructs a risk achievement data set; a removal module extracts basic information of achievement data to be stored in the risk achievement data set according to historical risk data of the achievement database, removes the risk achievement data set based on the basic information, and obtains a safe achievement data set; a storage module determines the data storage volume of achievement data to be stored in the safe achievement data set, and stores the achievement data to be stored in the safe achievement data set in the achievement database according to the data storage volume, thereby removing risk data in the achievement data to be stored and ensuring safe storage of the achievement database, and at the same time realizing intelligent adjustment of the data storage process of the achievement database, improving storage efficiency, and reducing storage pressure of the achievement database.

Description

Technical Field

[0001] This invention relates to the field of results database technology, and in particular to a results database management system. Background Technology

[0002] An achievement repository is a database that collects, organizes, stores, and displays various types of achievements. These achievements can be from various fields such as science and technology, culture, art, and education. The achievement repository is an essential database for searching new technologies and achievements. It can be used for achievement novelty searches and technology transfer, and can also provide information sources for technical consulting and services, as well as important references for technological transformation, new product development, and process innovation.

[0003] However, with the development of the internet, single, traditional content repositories can no longer meet the needs of internet users for storing large amounts of content. The massive storage of content slows down the repository's response time, leading to a decrease in system responsiveness and severely impacting users' daily use and experience. Summary of the Invention

[0004] This invention provides a results repository management system to solve the technical problem in the prior art that the data storage process of the results repository cannot be intelligently adjusted, and the storage pressure of the results repository cannot be reduced.

[0005] To achieve the above objectives, the present invention provides a results repository management system, comprising:

[0006] The filtering module is used to receive the results data to be stored within a first preset time period, filter the results data to be stored, and construct a set of results data.

[0007] The determination module is used to determine the category of the result data to be stored in the result data set, and to construct a risk result data set based on the determination result;

[0008] The elimination module is used to obtain historical risk data from the results database, extract basic information of the results data to be stored from the risk results data set based on the historical risk data, and perform elimination processing on the risk results data set based on the basic information to obtain a safe results data set. The basic information includes the risk coefficient and risk attribute value of the results data to be stored.

[0009] The storage module is used to determine the data storage volume of the results data to be stored in the security results data set, and to store the results data to be stored in the results database according to the data storage volume.

[0010] Furthermore, the filtering module is specifically used for:

[0011] The filtering module is used to obtain the average data length of the results data to be stored, and to segment the results data to be stored according to the average data length to obtain the first overlapping data set of each results data to be stored.

[0012] The filtering module is used to delete overlapping data with an overlap length less than a preset length based on the first overlapping data set, so as to obtain a second overlapping data set.

[0013] The filtering module is used to construct the result data set based on the result data to be stored in the second overlapping data set.

[0014] Furthermore, the determination module is used for:

[0015] The determination module is used to perform data clustering analysis on the result data to be stored in the result data set to obtain several sub-data;

[0016] The determination module is used to determine the category of each sub-data based on a pre-trained category determination model and output the category probability corresponding to each sub-data.

[0017] The determination module is used to delete sub-data with a category probability less than a preset category probability, and retain sub-data with a category probability greater than or equal to the category probability.

[0018] The determination module is used to construct a risk outcome data set based on all the retained sub-data.

[0019] Furthermore, the rejection module is specifically used for:

[0020] The elimination module is used to determine the baseline risk coefficient based on the historical risk data;

[0021] The elimination module is used to determine the risk level of the outcome data to be stored in the risk outcome data set based on the risk coefficient and the risk attribute value when the ratio of the risk coefficient to the benchmark risk coefficient is greater than 1 / 2.

[0022] The elimination module is used to determine a first difference between the risk coefficient and the benchmark risk coefficient and a second difference between the benchmark risk coefficient and the second benchmark risk coefficient when the ratio of the risk coefficient to the benchmark risk coefficient is less than or equal to 1 / 2, wherein the benchmark risk coefficient is less than the second benchmark risk coefficient;

[0023] The elimination module is used to determine the allowable adjustment range of the benchmark risk coefficient based on the first difference and the second difference;

[0024] The elimination module is used to determine a first adjustment coefficient based on the minimum value in the allowed adjustment range;

[0025] The elimination module is used to determine a second adjustment coefficient based on the maximum value in the allowed adjustment range;

[0026] The elimination module is used to adjust the risk attribute value based on the first adjustment coefficient, the second adjustment coefficient and the benchmark risk coefficient, and calculate the adjusted risk attribute value;

[0027] The elimination module is used to determine the risk level of the outcome data to be stored in the risk outcome data set based on the risk coefficient and the adjusted risk attribute value.

[0028] The elimination module is used to calculate and adjust the risk attribute value according to the following formula:

[0029] ;

[0030] Where P is the adjusted risk attribute value, P1 is the risk attribute value, a is the loss factor in the process of calculating the adjusted risk attribute value, x is the baseline risk coefficient, m1 is the first adjustment coefficient, and m2 is the second adjustment coefficient.

[0031] Furthermore, the rejection module is specifically used for:

[0032] The elimination module is used to eliminate the risk outcome data to be stored from the risk outcome data set whose risk level is greater than or equal to the preset risk level.

[0033] The elimination module is used to obtain the safety outcome data set based on the remaining outcome data to be stored in the risk outcome data set.

[0034] Furthermore, the storage module is specifically used for:

[0035] The storage module is used to collect the historical storage speed of the results database within a second preset time period;

[0036] The storage module is used to calculate the product of the second preset time period and the historical storage speed, and uses the product as the storage capacity value of the results library.

[0037] Furthermore, the storage module is specifically used for:

[0038] The storage module is used to determine whether the results data to be stored in the security results data set can be stored in the results database based on the relationship between the storage capacity value and the data storage volume;

[0039] The storage module is used to obtain the importance of each data point to be stored in the security achievement data set when the storage capacity value is less than the data storage amount.

[0040] The storage module is used to sort the security achievement data to be stored in the data set based on the order of importance, and to store the data in the achievement database based on the sorting result and the preset storage speed Vm.

[0041] The storage module is used to calculate the storage difference between the storage capacity value and the storage amount when the storage capacity value is greater than or equal to the data storage amount.

[0042] The storage module is used to increase the preset storage speed Vm according to the storage difference to obtain the adjusted storage speed.

[0043] Furthermore, the storage module is specifically used for:

[0044] The storage module is used to preset a first preset storage difference and a second preset storage difference;

[0045] The storage module is used to increase and adjust the preset storage speed Vm according to the relationship between the storage difference, the first preset storage difference, and the second preset storage difference;

[0046] The storage module is used to increase the preset storage speed Vm based on the first preset increase adjustment coefficient h1 when the storage difference is less than the first preset storage difference, so as to obtain the adjusted storage speed Vm*h1.

[0047] The storage module is used to increase the preset storage speed Vm based on the second preset increase adjustment coefficient h2 when the storage difference is greater than or equal to the first preset storage difference and the storage difference is less than the second preset storage difference, so as to obtain the adjusted storage speed Vm*h2.

[0048] The storage module is used to increase the preset storage speed Vm based on a third preset increase adjustment coefficient h3 when the storage difference is greater than or equal to the second preset storage difference, thereby obtaining an adjusted storage speed Vm*h3; where 1

[0049] Furthermore, the storage module is specifically used for:

[0050] The storage module is used to perform a correlation evaluation on the adjusted storage speed Vm*hi based on a pre-trained evaluation model to obtain the corresponding correlation evaluation value, where i=1,2,3;

[0051] The storage module is used to determine whether compensation is needed for adjusting the storage speed Vm*hi based on the relationship between the correlation evaluation value and the correlation evaluation threshold. ​

[0052] The storage module is used to determine that no compensation is needed for adjusting the storage speed Vm*hi when the correlation evaluation value is greater than the correlation evaluation threshold, and to store the results data to be stored in the security results data set in the results database based on the adjusted storage speed Vm*hi.

[0053] The storage module is used to determine that compensation is needed for adjusting the storage speed Vm*hi when the correlation evaluation value is less than or equal to the correlation evaluation threshold.

[0054] The storage module is used to calculate the correlation evaluation difference between the correlation evaluation value and the correlation evaluation threshold, and to perform compensation processing on the adjusted storage speed Vm*hi based on the correlation evaluation difference to obtain the target storage speed.

[0055] The storage module is used to store the results data to be stored in the security results data set into the results database based on the target storage speed.

[0056] This invention provides a results database management system, which has the following advantages compared to the prior art:

[0057] This invention discloses a results repository management system. A filtering module filters the results data to be stored, constructing a results data set. A judgment module classifies the results data to be stored in the results data set, constructing a risky results data set. A removal module extracts basic information of the results data to be stored in the risky results data set based on historical risk data in the results repository, and removes the risky results data set based on this basic information to obtain a safe results data set. A storage module determines the data storage volume of the results data to be stored in the safe results data set and stores the results data to be stored in the results repository according to the data storage volume. This system can remove risky data from the results data to be stored, ensuring safe storage in the results repository. It also enables intelligent adjustment of the results repository data storage process, improving storage efficiency and reducing storage pressure on the results repository. Attached Figure Description

[0058] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0059] Figure 1 A schematic diagram of the structure of a results database management system according to an embodiment of the present invention is shown. Detailed Implementation

[0060] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0061] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0062] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0063] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0064] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0065] like Figure 1 As shown, an embodiment of the present invention discloses a results database management system, including: a screening module, a judgment module, a rejection module, and a storage module.

[0066] In some embodiments of this application, a filtering module is used to receive the results data to be stored within a first preset time period, and to filter the results data to be stored to construct a results data set; a judgment module is used to classify the results data to be stored in the results data set, and to construct a risk results data set based on the judgment result; a removal module is used to obtain historical risk data from the results library, and to extract basic information of the results data to be stored in the risk results data set based on the historical risk data, and to remove the results data from the risk results data set based on the basic information to obtain a safe results data set, wherein the basic information includes the risk coefficient and risk attribute value of the results data to be stored; a storage module is used to determine the data storage volume of the results data to be stored in the safe results data set, and to store the results data to be stored in the safe results data set in the results library according to the data storage volume.

[0067] The beneficial effects of the above technical solution are: the present invention can remove risky data from the data to be stored, ensuring the safe storage of the results database, and at the same time realizes intelligent adjustment of the data storage process of the results database, improving storage efficiency and reducing the storage pressure of the results database.

[0068] In some embodiments of this application, the filtering module is specifically used for:

[0069] The filtering module is used to obtain the average data length of the results data to be stored, and to segment the results data to be stored according to the average data length to obtain the first overlapping data set of each results data to be stored.

[0070] The filtering module is used to delete overlapping data with an overlap length less than a preset length based on the first overlapping data set, so as to obtain a second overlapping data set.

[0071] The filtering module is used to construct the result data set based on the result data to be stored in the second overlapping data set.

[0072] In this embodiment, the first preset time period can be set to 30 minutes or 60 minutes, depending on the actual situation, and no specific limitation is made here.

[0073] In this embodiment, the average data length is calculated based on the data length of all the results data to be stored.

[0074] In this embodiment, the first overlapping data set refers to all overlapping data within an average data length. That is, after the data to be stored is divided, several segments will be obtained, and overlapping data may exist in different segments. At this time, all overlapping data constitute the first overlapping data set.

[0075] In this embodiment, the preset length refers to the minimum data length set in advance. If the data length is less than the preset data length, the data is incomplete.

[0076] In this embodiment, the second overlapping data set refers to the complete result data to be stored obtained by retaining the complete data in the first overlapping data set.

[0077] The beneficial effects of the above technical solution are: by segmenting and filtering all the results data to be stored, the present invention can achieve the purpose of filtering complete data and remove incomplete results data to be stored, thus ensuring the effectiveness of the data stored in the results database.

[0078] In some embodiments of this application, the determination module is used for:

[0079] The determination module is used to perform data clustering analysis on the result data to be stored in the result data set to obtain several sub-data;

[0080] The determination module is used to determine the category of each sub-data based on a pre-trained category determination model and output the category probability corresponding to each sub-data.

[0081] The determination module is used to delete sub-data with a category probability less than a preset category probability, and retain sub-data with a category probability greater than or equal to the category probability.

[0082] The determination module is used to construct a risk outcome data set based on all the retained sub-data.

[0083] In this embodiment, the category determination model is pre-trained and is trained based on different data samples and the types and matching probabilities of the data samples. Therefore, the category probability of the corresponding sub-data matching can be obtained.

[0084] In this embodiment, each sub-data item will have a corresponding matching category probability.

[0085] In this embodiment, sub-data with a category probability less than the preset category probability is considered safe data and requires no further judgment. Sub-data with a category probability greater than or equal to the category probability is considered unknown risk data and requires further judgment.

[0086] The beneficial effects of the above technical solution are: by performing data clustering analysis on the result data to be stored in the result data set, and judging the corresponding sub-data according to the category probability, the present invention can avoid risky data from bringing risks to the result database.

[0087] In some embodiments of this application, the rejection module is specifically used for:

[0088] The elimination module is used to determine the baseline risk coefficient based on the historical risk data;

[0089] The elimination module is used to determine the risk level of the outcome data to be stored in the risk outcome data set based on the risk coefficient and the risk attribute value when the ratio of the risk coefficient to the benchmark risk coefficient is greater than 1 / 2.

[0090] The elimination module is used to determine a first difference between the risk coefficient and the benchmark risk coefficient and a second difference between the benchmark risk coefficient and the second benchmark risk coefficient when the ratio of the risk coefficient to the benchmark risk coefficient is less than or equal to 1 / 2, wherein the benchmark risk coefficient is less than the second benchmark risk coefficient;

[0091] The elimination module is used to determine the allowable adjustment range of the benchmark risk coefficient based on the first difference and the second difference;

[0092] The elimination module is used to determine a first adjustment coefficient based on the minimum value in the allowed adjustment range;

[0093] The elimination module is used to determine a second adjustment coefficient based on the maximum value in the allowed adjustment range;

[0094] The elimination module is used to adjust the risk attribute value based on the first adjustment coefficient, the second adjustment coefficient and the benchmark risk coefficient, and calculate the adjusted risk attribute value;

[0095] The elimination module is used to determine the risk level of the outcome data to be stored in the risk outcome data set based on the risk coefficient and the adjusted risk attribute value.

[0096] The elimination module is used to calculate and adjust the risk attribute value according to the following formula:

[0097] ;

[0098] Where P is the adjusted risk attribute value, P1 is the risk attribute value, a is the loss factor in the process of calculating the adjusted risk attribute value, x is the baseline risk coefficient, m1 is the first adjustment coefficient, and m2 is the second adjustment coefficient.

[0099] In this embodiment, the baseline risk coefficient is a value obtained by analyzing all historical risk data. It can be used to measure whether the data to be stored has risk or unknown risk. When it is greater than or equal to the baseline risk coefficient, the current data to be stored may be risky data or unknown risk data.

[0100] In this embodiment, when determining the risk level based on the risk coefficient and the risk attribute value, it means calculating the first product of the risk coefficient and the corresponding weight, calculating the second product of the risk attribute value and the corresponding weight, and using the sum of the first product and the second product as the risk level.

[0101] In this embodiment, the baseline risk coefficient is less than the second baseline risk coefficient. The second baseline risk coefficient is a value that measures whether the data to be stored has risk. When it is greater than or equal to the second baseline risk coefficient, the corresponding data to be stored is directly determined to be risky data.

[0102] In this embodiment, the allowable adjustment range includes a first difference and a second difference.

[0103] In this embodiment, the first adjustment coefficient and the second adjustment coefficient are determined according to the difference-adjustment coefficient mapping table, which is constructed based on historical data.

[0104] The beneficial effects of the above technical solution are: the present invention determines the risk level of the result data to be stored in the risk result data set based on the risk coefficient and the adjusted risk attribute value or risk attribute value, thereby accurately judging whether the result data to be stored has risks, effectively reducing the storage risk of the result library, and ensuring the safe storage of the result library.

[0105] In some embodiments of this application, the rejection module is specifically used for:

[0106] The elimination module is used to eliminate the risk outcome data to be stored from the risk outcome data set whose risk level is greater than or equal to the preset risk level.

[0107] The elimination module is used to obtain the safety outcome data set based on the remaining outcome data to be stored in the risk outcome data set.

[0108] In this embodiment, the security outcome data set also includes the aforementioned "sub-data corresponding to categories with a deletion probability less than a preset category probability".

[0109] In some embodiments of this application, the storage module is specifically used for:

[0110] The storage module is used to collect the historical storage speed of the results database within a second preset time period;

[0111] The storage module is used to calculate the product of the second preset time period and the historical storage speed, and uses the product as the storage capacity value of the results library.

[0112] In this embodiment, the second preset time period can be 40 minutes or 70 minutes, etc., and is not specifically limited here.

[0113] In this embodiment, the storage capacity value refers to the amount of data stored in the results repository during a second preset time period.

[0114] The beneficial effects of the above technical solution are: the present invention calculates the storage capacity value of the results library, which can lay the foundation for the intelligent storage of the results library and provide reliable data support.

[0115] In some embodiments of this application, the storage module is specifically used for:

[0116] The storage module is used to determine whether the results data to be stored in the security results data set can be stored in the results database based on the relationship between the storage capacity value and the data storage volume;

[0117] The storage module is used to obtain the importance of each data point to be stored in the security achievement data set when the storage capacity value is less than the data storage amount.

[0118] The storage module is used to sort the security achievement data to be stored in the data set based on the order of importance, and to store the data in the achievement database based on the sorting result and the preset storage speed Vm.

[0119] The storage module is used to calculate the storage difference between the storage capacity value and the storage amount when the storage capacity value is greater than or equal to the data storage amount.

[0120] The storage module is used to increase the preset storage speed Vm according to the storage difference to obtain the adjusted storage speed.

[0121] In this embodiment, the basis for judging the importance can be whether the data to be stored is protected by a key. When it is protected by a key and the level of key protection is higher, the importance is higher. Other criteria can also be used, depending on the actual situation.

[0122] In this embodiment, the preset storage speed Vm refers to the storage speed of the results database for the results data to be stored. The preset storage speed Vm can avoid the results database from bearing too much storage pressure.

[0123] The beneficial effects of the above technical solution are: the present invention can prioritize the storage of important data to be stored, avoid data leakage, and protect user privacy and work results.

[0124] In some embodiments of this application, the storage module is specifically used for:

[0125] The storage module is used to preset a first preset storage difference and a second preset storage difference;

[0126] The storage module is used to increase and adjust the preset storage speed Vm according to the relationship between the storage difference, the first preset storage difference, and the second preset storage difference;

[0127] The storage module is used to increase the preset storage speed Vm based on the first preset increase adjustment coefficient h1 when the storage difference is less than the first preset storage difference, so as to obtain the adjusted storage speed Vm*h1.

[0128] The storage module is used to increase the preset storage speed Vm based on the second preset increase adjustment coefficient h2 when the storage difference is greater than or equal to the first preset storage difference and the storage difference is less than the second preset storage difference, so as to obtain the adjusted storage speed Vm*h2.

[0129] The storage module is used to increase the preset storage speed Vm based on a third preset increase adjustment coefficient h3 when the storage difference is greater than or equal to the second preset storage difference, thereby obtaining an adjusted storage speed Vm*h3; where 1

[0130] In this embodiment, when the storage capacity value is greater than or equal to the storage amount, it indicates that the storage capacity of the results library is strong, which can increase the storage speed of the results library.

[0131] The beneficial effects of the above technical solution are as follows: The present invention increases and adjusts the preset storage speed Vm according to the relationship between the storage difference, the first preset storage difference, and the second preset storage difference. By increasing and adjusting the preset storage speed Vm, the data storage speed can be further increased and the storage efficiency can be improved while ensuring the stable storage of the results database, thereby realizing intelligent storage in the data storage process of the results database.

[0132] In some embodiments of this application, the storage module is specifically used for:

[0133] The storage module is used to perform a correlation evaluation on the adjusted storage speed Vm*hi based on a pre-trained evaluation model to obtain the corresponding correlation evaluation value, where i=1,2,3;

[0134] The storage module is used to determine whether compensation is needed for adjusting the storage speed Vm*hi based on the relationship between the correlation evaluation value and the correlation evaluation threshold.

[0135] ​The storage module is used to determine that no compensation is needed for adjusting the storage speed Vm*hi when the correlation evaluation value is greater than the correlation evaluation threshold, and to store the results data to be stored in the security results data set in the results database based on the adjusted storage speed Vm*hi.

[0136] The storage module is used to determine that compensation is needed for adjusting the storage speed Vm*hi when the correlation evaluation value is less than or equal to the correlation evaluation threshold.

[0137] The storage module is used to calculate the correlation evaluation difference between the correlation evaluation value and the correlation evaluation threshold, and to perform compensation processing on the adjusted storage speed Vm*hi based on the correlation evaluation difference to obtain the target storage speed.

[0138] The storage module is used to store the results data to be stored in the security results data set into the results database based on the target storage speed.

[0139] In this embodiment, the evaluation model is pre-trained and is trained based on different data samples and the evaluation values ​​matched by the data samples. Therefore, the correlation evaluation value of the corresponding value can be obtained.

[0140] In this embodiment, the correlation evaluation value refers to the value that measures whether the adjusted storage speed Vm*hi can reach the correlation evaluation threshold. When the correlation evaluation value is greater than the correlation evaluation threshold, it means that the current adjusted storage speed Vm*hi can be taken as the final storage speed. When the correlation evaluation value is less than or equal to the correlation evaluation threshold, it means that the adjusted storage speed Vm*hi can be further adjusted.

[0141] In this embodiment, the target storage speed Vm*hi is obtained by compensating for the correlation evaluation difference, as follows:

[0142] A first preset correlation assessment difference and a second preset correlation assessment difference are pre-set;

[0143] The storage speed Vm*hi is adjusted to compensate based on the relationship between the correlation assessment difference, the first preset correlation assessment difference, and the second preset correlation assessment difference.

[0144] When the correlation evaluation difference is less than the first preset correlation evaluation difference, the storage speed Vm*hi is adjusted based on the first preset compensation coefficient e1 to obtain the target storage speed Vm*hi*e1.

[0145] When the correlation evaluation difference is greater than or equal to the first preset correlation evaluation difference, and the correlation evaluation difference is less than the second preset correlation evaluation difference, the storage speed Vm*hi is adjusted based on the second preset compensation coefficient e2 to obtain the target storage speed Vm*hi*e2.

[0146] When the correlation evaluation difference is greater than or equal to the second preset correlation evaluation difference, the storage speed Vm*hi is adjusted based on the third preset compensation coefficient e3 to obtain the target storage speed Vm*hi*e3.

[0147] The beneficial effects of the above technical solution are: the present invention compensates for the storage speed Vm*hi by adjusting the relationship between the correlation evaluation difference, the first preset correlation evaluation difference, and the second preset correlation evaluation difference, which can further improve the data storage speed of the results library, while ensuring the stable storage of the results library and preventing the phenomenon of storage pressure.

[0148] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0149] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, features in the embodiments disclosed herein can be combined with each other in any manner, provided there is no structural conflict. The omission of all such combinations in this specification is merely for brevity and resource conservation. Therefore, the invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0150] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A results library management system, characterized in that: include: A screening module, used for receiving the achievement data to be stored within a first preset time period, screening the achievement data to be stored, and constructing an achievement data set; A determination module, used for determining the category of the achievement data to be stored in the achievement data set, and constructing a risk achievement data set based on the determination result; A removal module is used to obtain historical risk data of the achievement library, and extract basic information of the achievement data to be stored in the risk achievement data set according to the historical risk data, and remove the risk achievement data set based on the basic information to obtain a safe achievement data set, wherein the basic information includes the risk coefficient of the achievement data to be stored and the risk attribute value of the achievement data to be stored; A storage module, used for determining the data storage capacity of the achievement data to be stored in the security achievement data set, and storing the achievement data to be stored in the security achievement data set in the achievement library according to the data storage capacity; The elimination module is used to determine a baseline risk coefficient based on the historical risk data; The elimination module is used to determine the risk level of the achievement data to be stored in the risk achievement data set according to the risk coefficient and the risk attribute value when the ratio of the risk coefficient to the benchmark risk coefficient is greater than 1 / 2; The elimination module is used for determining a first difference between the risk coefficient and the benchmark risk coefficient and a second difference between the benchmark risk coefficient and a second benchmark risk coefficient when the ratio of the risk coefficient to the benchmark risk coefficient is less than or equal to 1 / 2, wherein the benchmark risk coefficient is less than the second benchmark risk coefficient; The elimination module is used to determine the allowable adjustment range of the benchmark risk coefficient according to the first difference and the second difference; The elimination module is used to determine the first adjustment coefficient according to the minimum value in the allowable adjustment range; The elimination module is used to determine the second adjustment coefficient according to the maximum value in the allowable adjustment range; The elimination module is used to adjust the risk attribute value based on the first adjustment coefficient, the second adjustment coefficient and the reference risk coefficient, and calculate an adjusted risk attribute value; The elimination module is used to determine the risk level of the achievement data to be stored in the risk achievement data set according to the risk coefficient and the adjusted risk attribute value; The elimination module is used to calculate and adjust the risk attribute value according to the following formula: ； Among them, P is the adjusted risk attribute value, P1 is the risk attribute value, a is the loss factor in the process of calculating the adjusted risk attribute value, x is the benchmark risk coefficient, m1 is the first adjustment coefficient, and m2 is the second adjustment coefficient.

2. The achievement database management system according to claim 1 is characterized in that: The screening module is specifically used for: The screening module is used to obtain the average data length of the achievement data to be stored, and to segment the achievement data to be stored according to the average data length to obtain a first overlapping data set for each achievement data to be stored; The screening module is used to delete overlapping data with an overlapping length less than a preset length based on the first overlapping data set to obtain a second overlapping data set; The screening module is used to construct the achievement data set according to the achievement data to be stored in the second overlapping data set.

3. The achievement database management system according to claim 1 is characterized in that: The determination module is used for: The determination module is used to perform data clustering analysis on the achievement data to be stored in the achievement data set to obtain a plurality of sub-data; The determination module is used to perform category determination on each sub-data based on a pre-trained category determination model, and output the category probability corresponding to each sub-data; The determination module is used to delete the sub-data corresponding to the category probability less than the preset category probability, and retain the sub-data corresponding to the category probability greater than or equal to the category probability; The determination module is used to construct a risk outcome data set based on all retained sub-data.

4. The achievement database management system according to claim 1 is characterized in that: The rejection module is specifically used for: The elimination module is used to eliminate the to-be-stored achievement data whose risk level is greater than or equal to the preset risk level in the risk achievement data set; The elimination module is used to obtain the safety achievement data set according to the remaining achievement data to be stored in the risk achievement data set.

5. The achievement database management system according to claim 1 is characterized in that: The storage module is specifically used for: The storage module is used to collect the historical storage speed of the achievement library within a second preset time period; The storage module is used to calculate the product value of the second preset time period and the historical storage speed, and use the product value as the storage capacity value of the achievement library.

6. The achievement database management system according to claim 5 is characterized in that: The storage module is specifically used for: The storage module is used to determine whether the achievement data to be stored in the security achievement data set can be stored in the achievement library according to the relationship between the storage capacity value and the data storage capacity; The storage module is used for obtaining the importance of each achievement data to be stored in the security achievement data set when the storage capacity value is less than the data storage amount; The storage module is used to sort the achievement data to be stored in the security achievement data set based on the importance relationship, and store the achievement data to be stored in the achievement library based on the sorting result and the preset storage speed Vm; The storage module is used for calculating the storage difference between the storage capacity value and the data storage capacity when the storage capacity value is greater than or equal to the data storage capacity; The storage module is used to increase and adjust the preset storage speed Vm according to the storage difference to obtain an adjusted storage speed.

7. The achievement database management system according to claim 6 is characterized in that: The storage module is specifically used for: The storage module is used to preset a first preset storage difference and a second preset storage difference; The storage module is used to increase and adjust the preset storage speed Vm according to the relationship between the storage difference, the first preset storage difference and the second preset storage difference; The storage module is used for increasing and adjusting the preset storage speed Vm based on a first preset increase adjustment coefficient h1 to obtain an adjusted storage speed Vm*h1 when the storage difference is less than the first preset storage difference; The storage module is used for increasing and adjusting the preset storage speed Vm based on a second preset increase adjustment coefficient h2 to obtain an adjusted storage speed Vm*h2 when the storage difference is greater than or equal to the first preset storage difference and the storage difference is less than the second preset storage difference; The storage module is used for increasing and adjusting the preset storage speed Vm based on the third preset increase adjustment coefficient h3 when the storage difference is greater than or equal to the second preset storage difference, so as to obtain the adjusted storage speed Vm*h3; wherein, 1 <h1<h2<h3<1.2。 8. The achievement database management system according to claim 7 is characterized in that: The storage module is specifically used for: The storage module is used to perform a correlation evaluation on the adjusted storage speed Vm*hi based on a pre-trained evaluation model to obtain a corresponding correlation evaluation value, where i=1, 2, 3; The storage module is used to determine whether it is necessary to compensate the adjusted storage speed Vm*hi according to the relationship between the correlation evaluation value and the correlation evaluation threshold; The storage module is used for determining that it is not necessary to compensate the adjustment storage speed Vm*hi when the correlation evaluation value is greater than the correlation evaluation threshold, and storing the to-be-stored achievement data in the security achievement data set in the achievement library based on the adjustment storage speed Vm*hi; The storage module is used for judging that the adjustment storage speed Vm*hi needs to be compensated when the correlation evaluation value is less than or equal to the correlation evaluation threshold; The storage module is used to calculate the correlation evaluation difference between the correlation evaluation value and the correlation evaluation threshold, and to perform compensation processing on the adjusted storage speed Vm*hi based on the correlation evaluation difference to obtain a target storage speed; The storage module is used to store the to-be-stored achievement data in the security achievement data set in the achievement library based on the target storage speed.

Images