A multi-source data-based authorized operation collaborative management system and method

Abstract

The application discloses a kind of authorized operation collaborative management system and method based on multi-source data, it is related to data processing technical field, the authorized data of each data authorized party is extracted in the application, metadata information containing data type and data purpose is obtained, authorized data is stored by database, and is applied to call by data user;Reasonable degree of this application of data user is calculated;According to data purpose, historical application reasonable degree is divided into sub-set, the maximum value of the highest frequency interval of reasonable degree in sub-set is taken as reasonable degree threshold value, and the audit result is obtained by comparing reasonable degree with threshold value, if pass, send authorized data to data user and record information, if not pass, send rejection basis;Optimization cycle and effective data time are preset, update sub-set and calculate new threshold value at the end of cycle.The application is driven by data instead of subjective decision, standardizes multi-source data whole life cycle management, improves data authorization audit accuracy and cross-department data collaborative efficiency, supports data security compliance circulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an authorized operation collaborative management system and method based on multi-source data. BACKGROUND

[0002] Under the trend of accelerating development of digital economy, the value of data as a core production factor is increasingly prominent, and the demand for collaboration between departments and business fields within enterprises continues to grow. Authorized operation management, as a core mechanism for integrating scattered data resources and promoting the compliance and efficient flow of data elements, is a key prerequisite for maximizing data value. It not only breaks down the data barriers between departments and ensures that data is accurately called within a safe and controllable range, but also standardizes the authorization and operation processes of data, deeply matches data resources with business scenarios, and provides strong data support for strategic decision-making and business innovation of enterprises. It is a necessary basis for building a data value closed loop in the digital transformation of enterprises.

[0003] In the current cross-department data calling scenario, whether the data calling is reasonable depends on manual experience or subjective rules for decision-making. Under this mode, the degree of fit between data calling and actual business needs cannot be guaranteed: on the one hand, unnecessary data may be overcalled, causing waste of system resources, excessive load pressure, and even data security risks; on the other hand, key data may not be fully called, making it difficult to efficiently achieve business goals. SUMMARY

[0004] The purpose of the present application is to provide an authorized operation collaborative management system and method based on multi-source data to solve the problems in the prior art.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] Step S1: Extracting the authorized data of each data authorization party and obtaining the metadata information of the authorized data, the metadata information being structured data including the data type and data purpose of the authorized data; the database stores the authorized data according to the metadata information; the data user applies to call the authorized data in the database;

[0007] Further, step S1 further includes:

[0008] The database divides the authorized data of each data authorization party according to business attributes to obtain data types; the data purpose is a subcategory contained in the data type, which is divided by the database;

[0009] The data user needs to provide a historical access record when applying to access the authorized data in the database; the historical access record includes the data type and data purpose of each access within a preset statistical time, and the data purpose of each application access is unique.

[0010] Step S2, calculating the rationality of the current application of the data user according to the metadata information;

[0011] Further, step S2 includes:

[0012] Step S2-1, extracting the data type set H of the data user applied to access within the statistical time in the historical access record, calculating the historical access frequency proportion of each data type in the data type set H, and obtaining the data type set R` of the authorized information applied to access by the data user;

[0013] Calculate the intersection of the data type set H and the data type set R`, sum the historical frequency proportions of all data types in the intersection, and obtain the business relevance index R = ∑h i , wherein h i represents the historical access frequency proportion of the i-th data type in the data type set H, i∈H∩R`, 0≤R≤100%;

[0014] Step S2-2, extracting the data type record of the data purpose of the current application applied to access by all data users within the statistical time in the historical access record, integrating into the calling set G, calculating the same-purpose coverage of each data type, and the same-purpose coverage is the proportion of the number of data users accessing the data type in the number of all data users in the calling set G; a purpose coverage threshold is preset, and data types with a same-purpose coverage greater than the purpose coverage threshold are screened out to form a data type set F;

[0015] Obtain the intersection number m T of the data type set R` and the data type set F, and the total number of data types m A in the data type set R`, calculate the data necessity index N, N = m T / m A , 0≤N≤100%;

[0016] Step S2-3, calculating the rationality C of the current application of the data user according to the business relevance index R and the data necessity index N:

[0017] ;

[0018] Wherein w R and w NThe weights of the business relevance index R and the data necessity index N, respectively, w R +w N = 1.

[0019] Step S3, according to the rationality, the data user's current application is audited, and the audit result of the current application is obtained;

[0020] Further, the process of auditing in step S3 includes:

[0021] Obtain the rationality of all applications of the data user within the preset statistical time, divide the rationality into several subsets according to the data usage, denoted as S1, S2,..., S m Where m is the total number of data usage types applied by the data user within the statistical time;

[0022] Divide all rationalities in the subset to which the data usage of the current application belongs into a histogram of a preset interval, and calculate the rationality threshold U; the rationality threshold U is the maximum value of the interval with the highest frequency of rationality in the subset to which the data usage of the current application belongs;

[0023] If the rationality C of the current application is greater than or equal to U, it is determined that the audit result of the current application is passed, the database sends the authorized data to the data user, and records the data type, data usage and rationality of the current application;

[0024] If the rationality C of the current application is less than U, it is determined that the audit result of the current application is not passed, and the data user is sent a rejection basis, which includes the business relevance index R, the data necessity index N, the rationality of the current application and the rationality threshold U, and the data user resubmits the application.

[0025] Step S4, optimizing the audit according to the audit result;

[0026] Further, the process of optimizing the audit in step S4 includes:

[0027] A preset optimization period and an effective data time, at the end of each optimization period, all applications of the data user within the current optimization period are classified into the corresponding subset, and the applications in the subset that exceed the effective data time are removed, a new subset is formed, and a new rationality threshold U' is calculated.

[0028] An authorized operation collaborative management system based on multi-source data, the system includes a data storage module, a rationality calculation module, an application audit module and an audit optimization module;

[0029] The data storage module is used for extracting the authorization data, obtaining metadata information and sending to the database for storage; the data storage module comprises an authorization data processing unit and a call record processing unit;

[0030] The authorization data processing unit is used for extracting the authorization data of each data authorization party, obtaining the metadata information of the authorization data, and sending to the database for storage according to the metadata information;

[0031] The call record processing unit is used for obtaining the historical call record provided by the data user when the data user applies to call the authorization data in the database.

[0032] The rationality calculation module is used for calculating the rationality of the current application of the data user; the rationality calculation module comprises an index calculation unit and a rationality calculation unit;

[0033] The index calculation unit is used for extracting the data type set H applied by the data user in the statistical time in the historical call record, obtaining the data type set R` of the authorization information applied by the data user, obtaining the business correlation index R according to the data type set H and the data type set R`, extracting the call set G in the historical call record, calculating the use coverage, forming the data type set F, and calculating the data necessity index N;

[0034] The rationality calculation unit is used for calculating the rationality C of the current application according to the correlation index R and the data necessity index N;

[0035] The application audit module is used for calculating the rationality threshold and obtaining the audit result of the current application according to the rationality threshold; the application audit module comprises an audit threshold calculation unit and an audit result execution unit;

[0036] The audit threshold calculation unit is used for dividing the rationality into a plurality of subsets according to the data use, dividing all the rationalities in the subset to which the data use of the current application belongs into a histogram of a preset interval, and calculating the rationality threshold U;

[0037] The audit result execution unit is used for obtaining the audit result of the current application according to the rationality of the current application and the rationality threshold.

[0038] The audit optimization module is used for optimizing the audit according to the audit result; the audit optimization module comprises an optimization parameter setting unit and an audit data updating unit;

[0039] The optimization parameter setting unit is used for presetting the optimization period and the effective data time;

[0040] The audit data updating unit is used to form a new sub-set and calculate a new rationality threshold U` according to the new sub-set.

[0041] The output end of the data storage module is connected to the input end of the rationality calculation module; the output end of the rationality calculation module is connected to the input end of the application audit module; and the output end of the application audit module is connected to the input end of the audit optimization module.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] 1. The present application extracts the authorization data of each data authorization party first to obtain metadata information containing data types and data purposes, and the database stores the authorization data according to the metadata information, and the data user provides historical access records containing data types and data purposes within a statistical time when applying; then the business correlation index and the data necessity index are calculated to obtain the rationality of the current application; finally, the rationality sub-set is divided according to the data purposes, and the maximum value of the highest frequency interval in the sub-set is taken as the threshold for auditing. Replacing manual experience decision-making, avoiding excessive or insufficient data calling, and improving the fit degree of auditing and business needs.

[0044] 2. The present application stores the authorization data according to the metadata information after extraction, and the data user needs to provide historical access records when applying, ensuring that data storage and application tracing have reliable basis; when calculating the rationality, the data types and data purposes in the historical access records are combined, and when auditing, the data types, data purposes and rationality of the passed application are recorded, and if not passed, the rejection basis containing the double index, rationality and threshold is sent. Standardize data extraction, storage and access tracing, solve the problem of cross-department data barriers and record chaos, and improve data collaboration compliance and efficiency.

[0045] 3. The present application sets an optimization period and an effective data time, and at the end of each optimization period, all applications within the period are classified into corresponding data purpose sub-sets, and applications beyond the effective data time are excluded to form a new sub-set, and a new rationality threshold U` is calculated. Break through the lagging nature of static audit rules, avoid interference of old data with audit standards, ensure that the audit threshold fits the current business, guarantee the accuracy of data calling audit, support efficient flow of data security, and help maximize data value. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 It is a flowchart of the present application of a multi-source data-based authorized operation collaborative management method;

[0047] Figure 2 It is a structural schematic diagram of the present application of a multi-source data-based authorized operation collaborative management system. DETAILED DESCRIPTION

[0048] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort are within the scope of the present application.

[0049] Embodiment one: as shown in the figure, the present application provides a technical solution, a method for collaborative management of authorized operation based on multi-source data, the method for collaborative management of authorized operation comprises: Figure 1

[0050] Step S1, extracting authorized data of each data authorized party, and obtaining metadata information of the authorized data, the metadata information is structured data, including data type and data use of the authorized data; the database stores the authorized data according to the metadata information; the data user calls the authorized data in the database by application;

[0051] Step S1 further comprises:

[0052] The database divides the authorized data of each data authorized party according to business attributes to obtain data types; the data use is a subcategory contained in the data type, which is divided by the database;

[0053] When the data user applies to call the authorized data in the database, historical call records need to be provided; the historical call records include the data type and data use called each time within a preset statistical time, and the data use of the authorized data called each time is unique.

[0054] Step S2, calculating the rationality of the current application of the data user according to the metadata information;

[0055] Step S2 comprises:

[0056] Step S2-1, extracting a data type set H applied and called by the data user within the statistical time in the historical call records, calculating the historical call frequency proportion of each data type in the data type set H, and obtaining a data type set R` of the authorized information applied and called by the data user;

[0057] Calculating the intersection of the data type set H and the data type set R`, summing the historical frequency proportions of all data types in the intersection to obtain a business correlation index R=∑h i , wherein h i represents the historical call frequency proportion of the i-th data type in the data type set H, i∈H∩R`, 0≤R≤100%;

[0058] ​Step S2-2, in the extraction history call record, all data users apply to call the data type record of the data use of the current application within the statistical time, and integrate it into a call set G; calculate the same use coverage of each data type, the same use coverage is the proportion of the number of data users calling the data type in the number of all data users in the call set G; a preset use coverage threshold is set, and data types with a same use coverage greater than the use coverage threshold are selected to form a data type set F;

[0059] The intersection number m of the data type set R` and the data type set F is obtained T , and the total number of data types in the data type set R` is m A , the data necessity index N is calculated, N=m T / m A , 0≤N≤100%;

[0060] Step S2-3, according to the business relevance index R and the data necessity index N, the rationality C of the current application of the data user is calculated:

[0061] ;

[0062] Where w R and w N are the weights of the business relevance index R and the data necessity index N, w R +w N =1.

[0063] Step S3, according to the rationality, the current application of the data user is audited, and the audit result of the current application is obtained;

[0064] The process of auditing in step S3 includes:

[0065] Obtain all the rationality of the data user applied within the preset statistical time, divide the rationality into several sub-sets according to the data use, denoted as S1, S2,..., Sm m , where m is the total number of data use types applied by the data user within the statistical time;

[0066] Divide all the rationality in the sub-set to which the data use of the current application belongs into a histogram of a preset interval, and calculate the rationality threshold U; the rationality threshold U is the maximum value of the interval with the highest frequency of rationality in the sub-set to which the data use of the current application belongs;

[0067] If the rationality C of the current application is greater than or equal to U, it is determined that the audit result of the current application is passed, the database sends the authorized data to the data user, and records the data type, data use and rationality of the current application.

[0068] If the rationality C of the current application is less than the threshold U, it is determined that the current application fails the audit, and a rejection basis is sent to the data user, the rejection basis including the business relevance index R, the data necessity index N, the rationality of the current application, and the rationality threshold U, and the data user resubmits the application.

[0069] Step S4, optimizing the audit according to the audit result;

[0070] The process of optimizing the audit in step S4 includes:

[0071] A preset optimization period and an effective data time, at the end of each optimization period, all applications of data users in the current optimization period are classified into corresponding sub-sets, and applications exceeding the effective data time in the sub-set are removed to form a new sub-set, and a new rationality threshold U' is calculated.

[0072] For example:

[0073] The data authorized party and the authorized data are determined, the data authorized party is the enterprise sales department and the customer department, the sales department provides the sales details of the last 12 months as the authorized data, and the customer department provides the customer information of the last 12 months as the authorized data. The database stores the authorized data according to the metadata information, the data type in the metadata information is divided into monthly sales, regional sales, customer basic information, and customer consumption frequency, and the data use is a sub-classification of the data type, the monthly sales and regional sales correspond to the data use of quarterly performance analysis, and the customer basic information and customer consumption frequency correspond to the data use of customer portrait construction.

[0074] The data user is the enterprise marketing department, and the authorized data is applied to be retrieved for quarterly performance analysis, and the historical retrieval record of the preset statistical time of the last 6 months needs to be provided. The historical retrieval record includes 5 applications:

[0075] The first retrieval is monthly sales, and the use is quarterly performance analysis;

[0076] The second retrieval is regional sales, and the use is quarterly performance analysis;

[0077] The third retrieval is customer basic information, and the use is customer portrait construction;

[0078] The fourth retrieval is monthly sales, and the use is quarterly performance analysis;

[0079] The fifth retrieval is customer consumption frequency, and the use is customer portrait construction;

[0080] The data use of the authorized data of each application is unique.

[0081] Extract the data type set H applied for data retrieval by the market department within the statistical time of nearly 6 months, set H contains monthly sales regional sales customer basic information customer consumption frequency. According to the historical retrieval record, 5 times of application is calculated for the historical retrieval frequency ratio of each data type, monthly sales retrieval 2 times accounts for 40%, regional sales retrieval 1 time accounts for 20%, customer basic information retrieval 1 time accounts for 20%, and customer consumption frequency retrieval 1 time accounts for 20%.

[0082] Get the data type set R of the authorized information of the data user's current application, the current application is used for quarterly performance analysis, so R contains monthly sales regional sales.

[0083] Calculate the intersection of data type set H and R, the intersection is monthly sales regional sales. Sum the historical frequency ratio of all data types in the intersection, the business relevance index R is equal to 40% plus 20% equal to 60%.

[0084] Extract all data type records of the data use purpose of quarterly performance analysis applied for data retrieval by all data users including the market department, sales department, customer service department and other 10 departments within the statistical time of nearly 6 months, and integrate them into the calling set G. In the calling set G, the data types retrieved by each department when applying for quarterly performance analysis include monthly sales, regional sales and product inventory data. Among them, there are 8 departments that retrieve monthly sales, 7 departments that retrieve regional sales, and 3 departments that retrieve product inventory data.

[0085] Calculate the same purpose coverage of each data type, the same purpose coverage of monthly sales is equal to 8 divided by 10, which is 80%, the same purpose coverage of regional sales is equal to 7 divided by 10, which is 70%, and the same purpose coverage of product inventory data is equal to 3 divided by 10, which is 30%. The preset purpose coverage threshold is 60%, and the data types with a same purpose coverage greater than the threshold are selected to form the data type set F, which contains monthly sales and regional sales.

[0086] Get the intersection number m of data type set R and data type set F of the authorized information of the data user's current application T , m T is equal to 2. The total number of data types in data type set R m A is equal to 2. Calculate the data necessity index N equal to 2 divided by 2, which is 100%.

[0087] Set the weight w R of business relevance index R to 0.5, the weight w N of data necessity index N to 0.5, which satisfies w R plus w N is equal to 1. According to the formula C is equal to R multiplied by w RN times w N , substituting the data gives C equals 60% times 0.5 plus 100% times 0.5 equals 80%.

[0088] Obtain the rationality of all applications of the data user market department within the last 6 months, and divide it into sub-sets according to data use. The sub-set S1 for quarterly performance analysis contains 3 application rationalities of 75%, 78%, and 82%, respectively. The sub-set S2 for customer portrait construction contains 2 application rationalities of 72% and 76%, respectively. The data use of this application is quarterly performance analysis, corresponding to the sub-set S1.

[0089] Divide all rationalities 75%, 78%, and 82% in the sub-set S1 into a histogram of preset intervals 70%-75%, 76%-80%, and 81%-85%. The interval 70%-75% contains one rationality of 75%, the interval 76%-80% contains one rationality of 78%, and the interval 81%-85% contains one rationality of 82%. Since the frequencies of the three intervals are the same, take the maximum value of the interval 81%-85% with the highest rationality value as the rationality threshold U, which equals 85%.

[0090] Compare the rationality C=80% of this application with the rationality threshold U=85%. Since 80% is less than 85%, determine that the review result of this application is not passed. Send the rejection basis to the data user market department, which includes the business relevance index R=60%, the data necessity index N=100%, the rationality C=80% of this application, and the rationality threshold U=85%. The data user needs to resubmit the application.

[0091] The preset optimization period is 3 months, and the effective data time is 6 months. At the end of the first optimization period, collect all applications of the 10 departments of the data user within this optimization period, and classify them into corresponding sub-sets according to data use. The sub-set for quarterly performance analysis adds 5 application rationalities of 79%, 83%, 81%, 77%, and 84%, respectively.

[0092] Remove the applications in the sub-set that exceed the effective data time of 6 months. In the sub-set S1 for quarterly performance analysis, the applications corresponding to the rationalities of 75%, 78%, and 82% have exceeded the effective data time, so they are removed. Form a new sub-set containing 5 rationalities of 79%, 83%, 81%, 77%, and 84%.

[0093] The reasonable degrees in the new subset are divided into a histogram of preset intervals 76% to 80% and 81% to 85%, the interval 76% to 80% contains two reasonable degrees 79% and 77%, and the interval 81% to 85% contains three reasonable degrees 83%, 81% and 84%. The interval 81% to 85% with the highest frequency of reasonable degrees is the maximum value of the new reasonable degree threshold value U, and U is equal to 85%.

[0094] Embodiment two: as shown in the figure, the application provides an authorized operation collaborative management system based on multi-source data, which comprises a data storage module, a reasonable degree calculation module, an application audit module and an audit optimization module. Figure 2

[0095] The data storage module is used for extracting authorized data, obtaining metadata information and sending the metadata information to a database for storage.

[0096] The authorized data processing unit is used for extracting authorized data of each data authorization party, obtaining metadata information of the authorized data, and sending the metadata information to the database for storage of the authorized data according to the metadata information.

[0097] The record processing unit is used for obtaining historical access records provided by the data user when the data user applies to access the authorized data in the database.

[0098] The reasonable degree calculation module is used for calculating the reasonable degree of the current application of the data user; the reasonable degree calculation module comprises an index calculation unit and a reasonable degree calculation unit.

[0099] The index calculation unit is used for extracting a data type set H applied by the data user in the statistical time in the historical access records, obtaining a data type set R` of the authorized information applied by the data user, obtaining a business correlation index R according to the data type set H and the data type set R`, extracting a calling set G in the historical access records, calculating a use coverage, forming a data type set F, and calculating a data necessity index N.

[0100] The reasonable degree calculation unit is used for calculating the reasonable degree C of the current application according to the correlation index R and the data necessity index N.

[0101] The application audit module is used for calculating a reasonable degree threshold value and obtaining an audit result of the current application according to the reasonable degree threshold value; the application audit module comprises an audit threshold calculation unit and an audit result execution unit.

[0102] ​The audit threshold calculation unit is configured to divide the rationality into several subsets according to data usage, divide all rationality in the subset to which the data usage of the current application belongs into a histogram of preset intervals, and calculate a rationality threshold U;

[0103] The audit result execution unit is configured to obtain an audit result of the current application according to the rationality of the current application and the rationality threshold.

[0104] The audit optimization module is configured to optimize the audit according to the audit result; the audit optimization module comprises an optimization parameter setting unit and an audit data updating unit.

[0105] The optimization parameter setting unit is configured to preset an optimization period and an effective data time.

[0106] The audit data updating unit is configured to form a new subset and calculate a new rationality threshold U' according to the new subset.

[0107] An output end of the data storage module is connected to an input end of the rationality calculation module; an output end of the rationality calculation module is connected to an input end of the application audit module; and an output end of the application audit module is connected to an input end of the audit optimization module.

[0108] It is apparent for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all aspects as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and range of the equivalent elements of the claims are intended to be embraced in the present application. Any reference signs in the claims should not be considered as limiting the claims to which they belong.

Claims

1. A collaborative management method for authorized operation based on multi-source data, characterized in that: The method includes the following steps: Step S1: Extract the authorized data of each data authorizer and obtain the metadata information of the authorized data. The metadata information is structured data, including the data type and data use of the authorized data; the database stores the authorized data according to the metadata information; the data user applies to retrieve the authorized data in the database; Step S2: Calculate the rationality of the current application of the data user according to the metadata information; Step S3: Review the current application of the data user according to the rationality and obtain the review result of the current application; Step S4: Optimize the review according to the review result; The said Step S2 includes: Step S2-1: Extract the set H of data types applied by the data user within the statistical time from the historical retrieval records, and calculate the historical retrieval frequency ratio of each data type in the set H of data types; obtain the set R` of data types of the authorized information applied by the data user; Calculate the intersection of data type set H and data type set R', sum the historical frequency percentages of all data types within the intersection, and obtain the business relevance index R = ∑h i , where h i The percentage of historical retrieval frequencies of the i-th data type in the data type set H, where i∈H∩R`, 0≤R≤100%; Step S2-2: Extract the records of data types for the data use of the current application applied by all data users within the statistical time from the historical retrieval records, and integrate them into the call set G; calculate the same-use coverage of each data type, and the same-use coverage is the ratio of the number of data users retrieving this data type to the number of all data users in the call set G; preset a use coverage threshold, and screen out the data types with the same-use coverage greater than the use coverage threshold to form the set F of data types; The number of intersections m between the data type set R` and the data type set F of the authorization information requested by the data user. T The total number of data types m in the data type set R` A Calculate the data necessity index N, N=m T / m A , 0≤N≤100%; Step S2-3: Calculate the rationality C of the current application of the data user according to the business relevance index R and the data necessity index N: ； Where w R With w N These are the weights of the business relevance index R and the data necessity index N, respectively, w R +w N =1.

2. The authorized operation collaborative management method based on multi-source data according to claim 1, characterized in that: The said Step S1 includes: The database divides the authorized data of each data authorizer according to the business attributes to obtain data types; the data use is a sub-classification included in the data type, which is divided by the database; When the data user applies to retrieve the authorized data in the database, it needs to provide historical retrieval records; the historical retrieval records include the data type and data use retrieved each time within the preset statistical time, and the data use of the authorized data retrieved each application is unique.

3. The authorized operation collaborative management method based on multi-source data according to claim 1, characterized in that: The process of review in the said Step S3 includes: Obtain the reasonableness of all applications from data users within a preset statistical time period, and divide the reasonableness into several subsets according to the data usage, denoted as S1, S2, ..., S... m , where m is the total number of data usage applications requested by the data user within the statistical period; Divide all the rationalities in the subset to which the data use of the current application belongs into a histogram of preset intervals, and calculate the rationality threshold U; the rationality threshold U is the maximum value of the interval with the highest frequency of occurrence of the rationality in the subset to which the data use of the current application belongs; If the rationality C of the current application ≥ U, determine that the review result of the current application is passed, the database sends the authorized data to the data user, and records the data type, data use and rationality of the current application; If the rationality C of the current application < U, determine that the review result of the current application is not passed, and send the rejection basis to the data user. The rejection basis includes the business relevance index R, the data necessity index N, the rationality of the current application, and the rationality threshold U, and the data user resubmits the application.

4. The authorized operation collaborative management method based on multi-source data according to claim 1, characterized in that: The process of optimizing the review in the said Step S4 includes: The optimization cycle and the effective data time are preset. At the end of each optimization cycle, all applications from data users within the optimization cycle are classified into the corresponding subsets. At the same time, applications that have exceeded the effective data time in the subsets are removed to form a new subset, and a new reasonableness threshold U` is calculated.

5. A multi-source data-based authorization operation collaborative management system, applied to the multi-source data-based authorization operation collaborative management method described in any one of claims 1-4, characterized in that: The system includes a data storage and retrieval module, a reasonableness calculation module, an application review module, and a review optimization module. The data storage and retrieval module is used to extract authorized data, obtain metadata information, and send it to the database for storage. The reasonableness calculation module is used to calculate the reasonableness of the data user's application. The application review module is used to calculate the reasonableness threshold and obtain the review result of the application based on the reasonableness threshold. The review optimization module is used to optimize the review based on the review result. The output of the data storage and adjustment module is connected to the input of the rationality calculation module; the output of the rationality calculation module is connected to the input of the application review module; and the output of the application review module is connected to the input of the review optimization module.

6. The authorized operation and collaborative management system based on multi-source data according to claim 5, characterized in that: The data storage and retrieval module includes an authorized data processing unit and a retrieval record processing unit; The authorized data processing unit is used to extract the authorized data from each data licensor, obtain the metadata information of the authorized data, and send it to the database, whereby the database stores the authorized data according to the metadata information. The retrieval record processing unit is used to obtain historical retrieval records provided by the data user when the data user requests to retrieve authorized data from the database.

7. The authorized operation and collaborative management system based on multi-source data according to claim 5, characterized in that: The rationality calculation module includes an index calculation unit and a rationality calculation unit; The index calculation unit is used to extract the data type set H requested by the data user within the statistical time period from the historical retrieval records, obtain the data type set R' of the authorization information requested by the data user, and obtain the business relevance index R based on the data type set H and the data type set R'; extract the call set G from the historical retrieval records, calculate the usage coverage, form the data type set F, and calculate the data necessity index N. The reasonableness calculation unit is used to calculate the reasonableness C of this application based on the relevance index R and the data necessity index N.

8. The authorized operation and collaborative management system based on multi-source data according to claim 5, characterized in that: The application review module includes a review threshold calculation unit and a review result execution unit; The review threshold calculation unit is used to divide the reasonableness into several subsets according to the data usage, and divide all the reasonableness in the subset to which the data usage of this application belongs into a histogram of preset intervals, and calculate the reasonableness threshold U; The review result execution unit is used to obtain the review result of this application based on the reasonableness of this application and the reasonableness threshold.

9. The authorized operation and collaborative management system based on multi-source data according to claim 5, characterized in that: The audit optimization module includes an optimization parameter setting unit and an audit data update unit; The optimization parameter setting unit is used to preset the optimization period and the effective data time. The audit data update unit is used to form a new subset and calculate a new reasonableness threshold U` based on the new subset.

Images