Data analysis methods, devices, equipment, media and products

Abstract

The present disclosure provides a data analysis method, device, equipment, medium and product, which are applied to the field of shared systems. The method includes: determining at least two financial institutions; the financial institutions store account information and operating data corresponding to at least one object; obtaining a target object that has account information in any two financial institutions to obtain the operating data corresponding to the target object in at least two financial institutions; performing abnormal analysis on the operating data corresponding to the target object in at least two financial institutions to obtain abnormal analysis results; the abnormal analysis results include whether the target object has operating abnormalities or does not have operating abnormalities; outputting the abnormal analysis results of the target object. The technical solution of the present disclosure improves the accuracy of data analysis.

Description

Technical Field

[0001] The present disclosure relates to the field of sharing systems, and in particular to a data analysis method, device, equipment, medium and product. Background Art

[0002] In the financial field, corporate users will package their own financial reports, statements, cash flow and other data to obtain policy benefits such as credit, tax and fee reductions in order to obtain improper gains. At the same time, in order to improve personal performance, bank account managers may also guide users to adjust their relevant business data in accordance with preferential policies so that the company meets the corresponding policies. The existence of the above phenomenon has a low risk prevention efficiency for enterprises, resulting in a low speed of discovering business anomalies. Summary of the invention

[0003] The present disclosure provides a data analysis method, device, equipment, medium and product for enterprise risk analysis. By performing account intersection analysis on any two financial institutions, the operating conditions of the automatic target object are accurately analyzed, thereby improving the analysis accuracy and efficiency.

[0004] According to a first aspect of the present disclosure, there is provided a data analysis method, comprising:

[0005] Determine at least two financial institutions; the financial institutions store account information and operating data corresponding to at least one object respectively;

[0006] Acquire a target object having account information in any two financial institutions to obtain the operating data corresponding to the target object in at least two financial institutions;

[0007] Performing anomaly analysis on the operating data of the target object corresponding to at least two financial institutions to obtain an anomaly analysis result; the anomaly analysis result includes whether the target object has an operating anomaly or does not have an operating anomaly;

[0008] Output the abnormality analysis result of the target object.

[0009] According to a second aspect of the present disclosure, there is provided a data analysis method, comprising:

[0010] Receiving account information corresponding to at least one object respectively sent by at least two financial institutions; the account information corresponding to at least one object is fed back by the electronic device instructing at least one of the financial institutions;

[0011] Perform account information intersection analysis on at least two financial institutions, and obtain target objects that have account information in any two financial institutions;

[0012] A target object having account information in any two financial institutions is sent to an electronic device so that the electronic device obtains the operating data corresponding to the target object in at least two financial institutions, and performs abnormal analysis on the target object to obtain abnormal analysis results.

[0013] According to a third aspect of the present disclosure, there is provided a data analysis device, comprising:

[0014] A first determining unit is used to determine at least two financial institutions; the financial institutions store account information and operating data corresponding to at least one object respectively;

[0015] A first acquisition unit is used to acquire a target object having account information in any two financial institutions, so as to obtain the operating data corresponding to the target object in at least two financial institutions;

[0016] an abnormality analysis unit, configured to perform abnormality analysis on the operating data of the target object corresponding to at least two financial institutions, respectively, to obtain an abnormality analysis result; the abnormality analysis result includes whether the target object has an operating abnormality or does not have an operating abnormality;

[0017] The result output unit is used to output the abnormality analysis result of the target object.

[0018] According to a fourth aspect of the present disclosure, there is provided a data analysis device, comprising:

[0019] Data analysis device, comprising:

[0020] The object receiving unit is used to receive account information corresponding to at least one object respectively sent by at least two financial institutions; the account information corresponding to at least one object is fed back by the electronic device instructing at least one of the financial institutions;

[0021] An intersection analysis unit, used to perform account information intersection analysis on two financial institutions in at least two financial institutions, and obtain a target object whose account information exists in any two financial institutions;

[0022] The object sending unit is used to send a target object whose account information exists in any two financial institutions to an electronic device, so that the electronic device can obtain the operating data corresponding to the target object in at least two financial institutions, and perform abnormal analysis on the target object to obtain abnormal analysis results.

[0023] According to a fifth aspect of the present disclosure, there is provided an electronic device, including:

[0024] at least one processor; and

[0025] a memory communicatively connected to the at least one processor; wherein,

[0026] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0027] According to a sixth aspect of the present disclosure, there is provided an electronic device, including:

[0028] at least one processor; and

[0029] a memory communicatively connected to the at least one processor; wherein,

[0030] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the second aspect.

[0031] According to a seventh aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the first aspect.

[0032] According to an eighth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the second aspect.

[0033] According to a ninth aspect of the present disclosure, a computer program product is provided, comprising: a computer program, wherein the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program so that the electronic device executes the method described in the first aspect.

[0034] According to the tenth aspect of the present disclosure, a computer program product is provided, comprising: a computer program, wherein the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program so that the electronic device executes the method described in the second aspect.

[0035] The technology disclosed in the present invention solves the problem of low efficiency in discovering business anomalies of enterprises. By determining at least two financial institutions, the account information and business data corresponding to at least one object can be stored in the financial institutions. By obtaining the target object that has account information in any two financial institutions, the empirical data corresponding to the target object in at least two financial institutions can be obtained, so as to perform abnormal analysis on the business data corresponding to the target object in at least two financial institutions, and obtain the abnormal analysis result. The abnormal analysis result includes the existence of business anomalies or the absence of business anomalies, so as to output the abnormal analysis result of the target object. By performing account intersection analysis on any two financial institutions, the target object with multiple financial accounts can be accurately determined to accurately analyze the business status of the target object. In this process, the target object is automatically analyzed, which improves the accuracy and efficiency of the analysis.

[0036] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0038] Figure 1 is a system architecture diagram of a data analysis method provided according to an embodiment of the present disclosure;

[0039] Figure 2 is a flow chart of an embodiment of a data analysis method provided according to an embodiment of the present disclosure;

[0040] Figure 3 is a flow chart of another embodiment of a data analysis method provided according to an embodiment of the present disclosure;

[0041] Figure 4 is a flow chart of another embodiment of a data analysis method provided according to an embodiment of the present disclosure;

[0042] Figure 5 is a flow chart of another embodiment of a data analysis method provided according to an embodiment of the present disclosure;

[0043] Figure 6 is a structural schematic diagram of an embodiment of a data analysis device provided according to an embodiment of the present disclosure;

[0044] Figure 7 is a structural schematic diagram of another embodiment of a data analysis device provided according to an embodiment of the present disclosure;

[0045] Figure 8 It is a block diagram of an electronic device used to implement the data analysis method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0047] The technical solution disclosed in the present invention can be applied to the scenario of rapid discovery of business anomalies in the financial field. By acquiring and analyzing the account information and business data corresponding to at least one object in each of multiple financial institutions, the efficiency of anomaly analysis can be improved.

[0048] In the prior art, the "Standards for Classifying Small and Medium-sized Enterprises" issued by the national ministries and commissions, such as the Ministry of Industry and Information Technology, the National Bureau of Statistics, the National Development and Reform Commission, and the Ministry of Finance, stipulate that the data such as business income, employees, and total assets of enterprises are used as the basis for the classification of enterprise size. Financial institutions then determine the financial preferential policies of enterprises based on the size of enterprises, for example, specifying targeted reserve ratio reduction policies, bank loan interest rate reductions, tax reductions, etc. Usually, corporate users can register with multiple banks to obtain financial preferential policies of multiple banks at the same time. If corporate users have operating risks and basic financial data is not reported in a timely manner, the policy issuance may fail. The data analysis of existing financial institutions is not timely enough, and the analysis process mostly relies on the timeliness of financial institution employees manually querying the operating data of various financial institutions, which is poor. On the basis of manual query, employees are also required to manually perform financial analysis on the operating data, resulting in low timeliness and efficiency of risk discovery of corporate data.

[0049] In order to solve the above technical problems, in the embodiments of the present disclosure, it is considered to use an automatic query method to query data from different financial institutions at the same time. At the same time, in order to ensure the security of various financial data in financial institutions, when considering processing all data, it is not necessary to query data from a single financial institution or multiple financial institutions, but to use account intersection detection between two financial data institutions to perform data analysis on objects that have accounts in two financial institutions at the same time, so as to perform abnormal analysis on the operating data of the object under the condition of high security and confidentiality, and obtain accurate analysis results.

[0050] In the disclosed embodiment, at least two financial institutions can be determined, and the financial institutions can store account information and operating data corresponding to at least one object. By acquiring a target object that has account information in any two financial institutions, the empirical data corresponding to the target object in at least two financial institutions can be obtained, so as to perform abnormal analysis on the operating data corresponding to the target object in at least two financial institutions, and obtain abnormal analysis results, which include the existence of operating abnormalities or the absence of operating abnormalities, so as to output the abnormal analysis results of the target object. By performing account intersection analysis on any two financial institutions, the target object with multiple financial accounts can be accurately determined, so as to accurately analyze the operating conditions of the target object. In this process, the target object is automatically analyzed, which improves the accuracy and efficiency of the analysis.

[0051] The technical solution of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0052] For ease of understanding, Figure 1 This is a system architecture diagram of a data analysis method provided according to an embodiment of the present disclosure, wherein the system architecture may include an electronic device 1 and electronic devices 2 corresponding to at least two financial institutions. The electronic device 1 may establish a communication connection with at least two electronic devices 2 via a local area network or a wide area network. The electronic device 1 or 2 may include, for example: a computer, a notebook, a super personal computer, a terminal device with data processing and storage functions, a common server, a cloud server, etc., and the specific type of the electronic device is not excessively limited in the embodiments of the present disclosure.

[0053] Typically, after the electronic device 1 obtains authorization from at least two financial institutions based on the data analysis method provided by the embodiment of the present disclosure, the corresponding electronic device 2 can obtain the business data of the corresponding target object to perform business abnormality analysis on the target object and obtain abnormality analysis results. Among them, the determination of the target object can be performed by the cloud server 3. In addition, the electronic device can also output the abnormality analysis results of the target object. By automatically obtaining the target object with account information in more than two financial institutions, the target object can be confirmed, and the target object can be automatically analyzed for abnormalities using its business data stored in the corresponding financial institution. This process does not require user participation, and accurate abnormality analysis results can be obtained, thereby improving the efficiency and accuracy of abnormality analysis.

[0054] like Figure 2 FIG. 1 is a flow chart of an embodiment of a data analysis method provided by an embodiment of the present disclosure, and the method may include the following steps:

[0055] 201: Determine at least two financial institutions. The financial institutions store account information and operating data corresponding to at least one object.

[0056] Alternatively, financial institutions may refer to banks, credit unions and other institutions set up in various places.

[0057] Determining at least two financial institutions may include: in response to an institution input operation triggered by a user, obtaining at least two financial institutions input by the user. In order to improve the efficiency of selecting institutions, a selection control for financial institutions may be provided, and a selection control may be set for each candidate financial institution, so that when the user selects a financial institution, the selected financial institution can be obtained by directly selecting the control.

[0058] The object of the present disclosure may refer to an enterprise object. Each financial institution may store object account information and operating data of an enterprise object that has opened an account in the financial institution. The object account information may include, for example, the object name, the financial account identifier set for the object, and other information. The operating data may include: the amount of money stored in the object's account, bank statements, corporate financial reports, and other data.

[0059] 202: Acquire a target object having account information in any two financial institutions to obtain the operating data corresponding to the target object in at least two financial institutions.

[0060] The target object may be an object that has account information in at least two financial institutions. When determining the target object, account intersection processing may be performed on the accounts of any two financial institutions to obtain the corresponding account intersection and implement the corresponding account intersection processing. In practical applications, the target object may be acquired by the electronic device at the same time as the two financial institutions where the accounts exist. The electronic device may acquire relevant information of the target object multiple times, so as to acquire the operating data of the target object in each financial institution from the institutions of the target object acquired multiple times, and after deduplication of the financial institution data, the experience data corresponding to the target object in at least two financial institutions may be acquired.

[0061] 203: Performing anomaly analysis on the target object's operating data corresponding to at least two financial institutions to obtain an anomaly analysis result. The anomaly analysis result includes whether the target object has an operating anomaly or does not have an operating anomaly.

[0062] The abnormal analysis result may include at least two business data analysis results of the target object.

[0063] When the abnormal analysis result shows that the target object is operating abnormally, the target object can be prompted to be in an abnormal operating state, the operating risks can be prompted in time, and the efficiency of abnormal prompts can be improved.

[0064] When the abnormality analysis result shows that the target object has no operating abnormality, the target object can be prompted to be in a normal operating state, and the normal state of the target object can be prompted in time, so that the target object can quickly obtain approval for various policies and improve approval efficiency. As an optional method, when the target object has no operating abnormality, the target object can be approved based on the target object's operating data and the approval request initiated by the target object.

[0065] 204: Output the abnormality analysis result of the target object.

[0066] Optionally, outputting the abnormality analysis result of the target object may include: generating a result prompt page based on the abnormality analysis result of the target object, and outputting the result prompt page to the user. Specifically, the result prompt page may be output to the user on a display screen, or a page link may be sent to a user terminal in a short message or text message, and the user terminal may display the page to the user when a viewing request triggered by the user is detected.

[0067] In the disclosed embodiment, when at least two financial institutions are determined, account information and operating data corresponding to at least one object may be stored in the financial institution. By acquiring a target object that has account information in any two financial institutions, the empirical data corresponding to the target object in at least two financial institutions can be obtained, so as to perform abnormal analysis on the operating data corresponding to the target object in at least two financial institutions, and obtain abnormal analysis results. The abnormal analysis results include the existence of operating abnormalities or the absence of operating abnormalities, so as to output the abnormal analysis results of the target object. By performing account intersection analysis on any two financial institutions, the target object with multiple financial accounts can be accurately determined, so as to accurately analyze the operating conditions of the target object. In this process, the target object is automatically analyzed, which improves the accuracy and efficiency of the analysis.

[0068] As an embodiment, obtaining a target object having account information in any two financial institutions includes:

[0069] Obtain target objects whose account information exists in any two financial institutions from a specified server.

[0070] The target object is obtained by performing account information intersection analysis on two financial institutions in pairs after the designated server obtains account information corresponding to at least one object respectively sent by at least two financial institutions.

[0071] Optionally, the financial institution's corresponding electronic device obtains the authorization request of at least one object and responds to the authorization request. After confirming the authorization, the financial institution can sequentially query whether each object in the at least one object is registered in the institution. If registered, the registered account information and the business data provided by the object are sent to the designated server. If not registered, the object is skipped and the next object in the at least one object is traversed.

[0072] The designated server can obtain the account information and operating data corresponding to at least one object fed back by each financial institution, and can compare at least two financial institutions in pairs to obtain target objects with account intersections between the two financial institutions.

[0073] In the disclosed embodiment, target objects having account information of any two financial institutions can be obtained from a designated server, and intersection analysis, isolated intersection analysis and abnormal analysis processes of the account information of financial institutions can be performed on the designated server to achieve accurate analysis of the data.

[0074] In a possible design, obtaining a target object having account information in any two financial institutions from a designated server may include:

[0075] Obtain object encryption information from a designated server; the object encryption information is obtained by encrypting a target object having account information in any two financial institutions by the designated server;

[0076] The object encrypted information is decrypted to obtain the target object whose account information exists in any two financial institutions.

[0077] The designated server may be a local server designated by the electronic device, for example, a common server, a cloud server, etc. The specific type of the server is not excessively limited in the embodiments of the present disclosure.

[0078] In a possible design, the designated server and the electronic device may be the same device. The user may use the electronic device to perform account intersection calculations on two financial institutions to obtain intersection calculation results.

[0079] When the designated server obtains the intersection calculation result to determine the target object with account information in two financial institutions, the target object's business data and account information corresponding to the two financial institutions can be encrypted to obtain object encryption information. The object encryption information can include the target object's account information and the business data corresponding to the two financial institutions.

[0080] By decrypting the object encrypted information and obtaining the target object having account information in any two financial institutions, the account information and operating data of the target object in the corresponding two financial institutions can also be obtained.

[0081] In the disclosed embodiment, when obtaining a target object from a designated server, object encryption information can be obtained from the designated server, and the object encryption information can be obtained by encrypting the target object for which account information exists in any two financial institutions. By decrypting the object encryption information, the target object for which account information exists in any two financial institutions is obtained. By encrypting and decrypting information during information transmission with the designated server, secure transmission of information can be achieved, thereby improving the security of object information.

[0082] As another embodiment, obtaining the operating data of the target object in at least two financial institutions respectively includes:

[0083] If it is determined that the target object has account information in any two financial institutions, the operating data stored in all financial institutions where the target object has account information is obtained to obtain the operating data corresponding to the target object in at least two financial institutions.

[0084] Optionally, the target object can be obtained by performing intersection calculation between two financial institutions on at least two financial institutions by the designated server. When determining the target object, the designated server can send the operating data of the target object in the corresponding two financial institutions to the electronic device.

[0085] Optionally, the target object may exist in more than two groups of financial institutions whose accounts overlap, and the account information and business data fed back by each group of financial institutions can be obtained. The business data fed back by multiple groups of financial institutions can be deduplicated to obtain the business data corresponding to the corresponding financial institutions. For example, if the account information of object A exists in both the first financial institution and the second financial institution, it can be determined that the object A exists in the first financial institution and the second financial institution as the target object. If the account information of object A exists in both the second financial institution and the third financial institution, it can be determined that the object A exists in the second financial institution and the third financial institution as the target object. At this time, the designated server can send the first business data of object A in the first financial institution and the second business data of the second financial institution to the electronic device; it can also send the second business data of object A in the second financial institution to the electronic device, and send the third business data of object A in the third financial institution to the electronic device. At this time, the electronic device can receive the first business data and the second business data twice and the third business data, and can deduplicate the two second business data to obtain the first business data, the second business data and the third business data as the business data corresponding to object A in the three financial institutions respectively.

[0086] The operating data corresponding to the target object in at least two financial institutions may be operating data stored in all account information of the target object.

[0087] In the embodiment of the present disclosure, when determining the target object, the operating data stored in all financial institutions where the target object has account information can be obtained to obtain the operating data corresponding to the target object in at least two financial institutions respectively. The operating data can be comprehensively obtained from all financial institutions where the operating data exists, so as to analyze all the operating data of the target object as a whole and obtain more comprehensive and accurate analysis results.

[0088] like Figure 3 FIG. 1 is a flowchart of another embodiment of a data analysis method provided by an embodiment of the present disclosure, and the method may include the following steps:

[0089] 301: Determine at least two financial institutions; the financial institutions store account information and operating data corresponding to at least one object.

[0090] 302: Acquire a target object having account information in any two financial institutions to obtain the operating data corresponding to the target object in at least two financial institutions.

[0091] 303: Determine the target object level of the target object.

[0092] 304: According to the multi-level object management policy, determine at least one abnormality analysis parameter matching the target object level from the object management policy; the object management policy includes at least one abnormality analysis parameter corresponding to each of the multiple object levels.

[0093] 305: Determine analysis data corresponding to at least one abnormal analysis parameter of the target object according to the operating data corresponding to the target object in at least two financial institutions.

[0094] 306: Determine an abnormality analysis result of the target object using the analysis data corresponding to the at least one abnormality analysis parameter.

[0095] 307: Output the abnormality analysis result of the target object.

[0096] Optionally, different exception analysis strategies may be set according to different object levels. The object management strategy may be composed of at least one exception analysis strategy corresponding to each object level. The exception analysis strategy for any object level may include at least one exception analysis parameter corresponding to the object level, and each exception analysis parameter may be set with a corresponding exception threshold.

[0097] According to the multi-level object management strategy, determining at least one exception analysis parameter that matches the target object level from the object management strategy may include: determining the exception analysis strategy that matches the target object level according to the exception analysis strategies corresponding to the multiple object levels respectively, so as to obtain at least one exception analysis parameter corresponding to the exception analysis strategy.

[0098] Abnormal analysis parameters may refer to abnormal indicators used when abnormal analysis is performed on business data, for example, cash flow analysis parameters, which may specifically analyze the direction and amount of cash flow to determine whether cash flow abnormalities occur. Abnormal analysis parameters may also include operating parameters that characterize the operating status of the enterprise. In the embodiments of the present disclosure, there are no excessive restrictions on the specific types of abnormal analysis parameters, and any parameters that can characterize the operation and management of the enterprise may be used as abnormal analysis parameters.

[0099] In the disclosed embodiment, when performing abnormal analysis on a target object, the target object level of the target object can be determined, and at least one abnormal analysis parameter matching the target object level can be determined from the object management strategy according to the multi-level object management strategy, so as to determine the analysis data corresponding to at least one abnormal analysis parameter of the target object according to the operating data corresponding to the target object in at least two financial institutions. The operating data of the target object is converted into corresponding parameter data by using the abnormal analysis parameters to the abnormal analysis parameters, so as to realize the quantification of the abnormal analysis parameters, and then the abnormal analysis result of the target object is determined by using the analysis data corresponding to at least one abnormal analysis parameter, so as to realize the quantitative analysis of the abnormality of the target object and obtain accurate abnormal analysis results.

[0100] In a possible design, using analysis data corresponding to at least one abnormal analysis parameter to determine an abnormal analysis result of the target object includes:

[0101] Determine the parameter state of the target object corresponding to the at least one abnormal analysis parameter using the analysis data corresponding to the at least one abnormal analysis parameter; the parameter state includes an abnormal state or a normal state;

[0102] If the number of abnormal analysis parameters in the abnormal state in the parameter states respectively corresponding to at least one abnormal analysis parameter is greater than a predetermined number, it is determined that the abnormal analysis result of the target data is that an operating abnormality exists.

[0103] Optionally, using the analysis data corresponding to at least one abnormal analysis parameter to determine the parameter state of the target object corresponding to at least one abnormal analysis parameter may include: performing abnormality judgment on the analysis data corresponding to any abnormal analysis parameter to obtain the parameter state corresponding to the abnormal analysis parameter. By confirming the parameter state of the target object at the abnormal analysis parameter according to the analysis data of each abnormal analysis parameter, an accurate analysis result can be obtained.

[0104] In the embodiment of the present disclosure, when determining the abnormal analysis result of the target object using the analysis data corresponding to at least one abnormal analysis parameter, the parameter state of each abnormal analysis parameter can be confirmed for the analysis data of the abnormal analysis parameter. By accurately determining the parameter state of each abnormal analysis parameter, when the number of abnormal analysis parameters in the parameter state corresponding to at least one abnormal analysis parameter that are in an abnormal state is greater than a predetermined number, it can be accurately determined that the abnormal analysis parameter is in an abnormal state, thereby improving the accuracy of determining the abnormal state.

[0105] In some embodiments, according to the multi-level object management policy, determining at least one anomaly analysis parameter matching the target object level from the object management policy includes:

[0106] According to the multi-level object management strategy, determining from the object management strategy at least one abnormality analysis parameter matching the target object level and an abnormality threshold value corresponding to each abnormality analysis parameter;

[0107] Determining the parameter state of the target object corresponding to the at least one abnormal analysis parameter using the analysis data corresponding to the at least one abnormal analysis parameter, including:

[0108] If the analysis data of the abnormal analysis parameter meets the abnormal threshold corresponding to the abnormal analysis parameter, it is determined that the target object is in an abnormal state in the abnormal analysis parameter;

[0109] If the analysis data of the abnormal analysis parameter does not meet the abnormal threshold corresponding to the abnormal analysis parameter, it is determined that the target object is in a normal state under the abnormal analysis parameter;

[0110] A parameter state corresponding to at least one abnormal analysis parameter is obtained.

[0111] Optionally, the analysis process of the target object at any abnormal analysis parameter may specifically be to compare the analysis data corresponding to the abnormal analysis parameter of the target object with the abnormal threshold value corresponding to the abnormal analysis parameter; if the abnormal threshold value is met, it is determined that the target object is in an abnormal state at the abnormal analysis parameter; if the abnormal threshold value is not met, it is determined that the target object is in a normal state at the abnormal analysis parameter.

[0112] In the disclosed embodiment, at least one abnormal analysis parameter corresponding to the target object and the abnormal threshold of each abnormal analysis parameter can be specifically determined to achieve accurate acquisition of abnormal judgment conditions. Then, the obtained abnormal analysis parameters and the corresponding abnormal threshold are used to accurately judge the abnormal state of the analysis data corresponding to at least one abnormal analysis parameter, obtain accurate parameter abnormality judgment results, and improve the accuracy of abnormal judgment.

[0113] like Figure 4 FIG. 1 is a flowchart of another embodiment of an abnormality analysis method provided by an embodiment of the present disclosure, and the method may include the following steps:

[0114] 401: Determine at least two financial institutions; the financial institutions store account information and operating data corresponding to at least one object.

[0115] 402: Acquire at least one object set by the user, and generate an object analysis request corresponding to the at least one object.

[0116] 403: Send an object analysis request to at least one financial institution respectively; the object analysis request instructs the financial institution to authorize object analysis on the object.

[0117] 404: Obtain object analysis authorization from at least one financial institution for at least one object.

[0118] 405: Acquire a target object having account information in any two financial institutions to obtain the operating data corresponding to the target object in at least two financial institutions.

[0119] 406: Performing anomaly analysis on the target object's operating data corresponding to at least two financial institutions to obtain an anomaly analysis result; the anomaly analysis result includes whether the target object has an operating anomaly or does not have an operating anomaly.

[0120] 407: Output the abnormality analysis results of the target object.

[0121] Optionally, the object analysis request may be an authorization request for at least one object. The object analysis request is sent to at least one financial institution. Each financial institution that receives the object analysis request may authorize the at least one object to perform analysis, so as to ensure the legitimacy of the electronic device's analysis of the object.

[0122] After the financial institution performs object analysis authorization on at least one object, the account information and business data of the at least one object can be sent to a server designated by the electronic device.

[0123] In the disclosed embodiment, after determining at least two financial institutions, at least one object set by the user can also be obtained, and an object analysis request corresponding to at least one object can be generated, so as to send the object analysis request to at least one financial institution respectively. The financial institution can use the object analysis request to authorize the analysis of at least one object, so as to obtain the object analysis authorization of at least one financial institution for at least one object respectively after the authorization. After the authorization, an abnormal analysis is performed on at least one object. By authorizing the use of at least one financial institution, the financial institution's secure management of at least one object can be ensured, and the management security of the financial institution can be effectively improved.

[0124] In one possible design, it also includes:

[0125] determining, according to the multi-level object management strategy, a data analysis request when performing data analysis on at least one object;

[0126] Sending a data analysis request to at least one financial institution respectively, using the data analysis request to instruct the financial institution to conduct a data analysis authorization for confirming the business data analysis of at least one object;

[0127] Obtain data analysis authorization from at least one financial institution to perform business data analysis on at least one object.

[0128] In the disclosed embodiment, when analyzing the data of an object, in order to ensure the security of the analysis, a data analysis request for at least one object can be initiated to at least one financial institution to ensure that the data analysis, that is, the analysis of the business data, is authorized to ensure the security of the data analysis of at least one object.

[0129] As a possible implementation method, outputting the abnormal analysis results of the target object may include:

[0130] The abnormality analysis result of the target object is sent to at least two financial institutions where the target object has account information.

[0131] The at least two financial institutions are all financial institutions for which the target object has account information.

[0132] In the disclosed embodiment, when the abnormal analysis results are obtained, the abnormal analysis results of the target object can be fed back to the financial institutions where the account information is stored, so that when the financial institutions receive the abnormal analysis results of the target object, the abnormal analysis results can be accurately prompted to avoid financial risks of the target object.

[0133] like Figure 5 FIG. 1 is a flowchart of another embodiment of a data analysis method provided by an embodiment of the present disclosure, and the method may include the following steps:

[0134] 501: receiving account information corresponding to at least one object respectively sent by at least two financial institutions; the account information corresponding to at least one object is fed back by the electronic device instructing at least one financial institution.

[0135] The data analysis method in the disclosed embodiment may be a designated server that performs intersection analysis of two financial institutions to obtain target objects that have account intersections between any two financial institutions.

[0136] For the description related to the designated server, reference may be made to the solutions described in the above embodiments.

[0137] 502: Perform account information intersection analysis on two financial institutions in at least two financial institutions to obtain a target object having account information in any two financial institutions;

[0138] 503: Sending a target object having account information in any two financial institutions to an electronic device, so that the electronic device obtains the operating data corresponding to the target object in at least two financial institutions, and performs abnormal analysis on the target object to obtain abnormal analysis results.

[0139] Optionally, sending the target object whose account information exists in any two financial institutions to the electronic device may include: sending the account information and business data corresponding to the target object in the two financial institutions to the electronic device. Both financial institutions are financial institutions where the target object's account information exists.

[0140] In the disclosed embodiment, the designated server may receive account information corresponding to at least one object respectively sent by at least two financial institutions, and the account information corresponding to at least one object is obtained by the electronic device instructing at least one financial institution to feedback. The designated server may obtain the account information corresponding to at least one object to accurately analyze the data on the designated server, isolate account intersection analysis and isolation of abnormal analysis, so that the designated server can accurately analyze the electronic device, reduce the analysis pressure of the electronic device, and improve the analysis efficiency.

[0141] As an embodiment, sending a target object whose account information exists in any two financial institutions to an electronic device includes:

[0142] Encrypting a target object that has account information in two arbitrary financial institutions to obtain encrypted information of the object;

[0143] The object encryption information is sent to the electronic device; the object encryption instructs the electronic device to decrypt the object encryption information to obtain the target object whose account information exists in any two financial institutions.

[0144] Optionally, the encryption and decryption algorithms can be preset according to actual encryption requirements, for example, the Message-Digest Algorithm (MD5) and other algorithms can be used for encryption and decryption. The encryption and decryption algorithms can correspond to each other.

[0145] In the disclosed embodiment, encryption processing is performed on target objects of account information respectively existing in any two financial institutions to obtain object encryption information, thereby achieving accurate encryption of account information, avoiding data loss during data transmission, and improving the security of information transmission.

[0146] In a possible design, account information intersection analysis is performed on at least two financial institutions in pairs, including:

[0147] A differential privacy algorithm is used to perform account information intersection analysis on at least two financial institutions.

[0148] Optionally, the Private Set intersection (PSI) algorithm can perform account intersection analysis on two financial institutions to find target objects with account information in both financial institutions. During the intersection analysis process, the object information corresponding to the object in any financial institution will not be leaked, including the object's account information and financial data, ensuring data security.

[0149] In the disclosed embodiment, a differential privacy algorithm may be used to perform intersection analysis of account information of at least two financial institutions, thereby improving the accuracy of the intersection analysis.

[0150] like Figure 6 FIG. 1 is a flowchart of another embodiment of a data analysis device provided by an embodiment of the present disclosure. The data analysis device may include the following units:

[0151] The first determining unit 601 is used to determine at least two financial institutions; the financial institutions store account information and operating data corresponding to at least one object.

[0152] The first acquisition unit 602 is used to acquire a target object having account information in any two financial institutions, so as to obtain the operating data corresponding to the target object in at least two financial institutions;

[0153] The abnormality analysis unit 603 is used to perform abnormality analysis on the operating data of the target object corresponding to at least two financial institutions to obtain abnormality analysis results; the abnormality analysis results include whether the target object has an operating abnormality or does not have an operating abnormality.

[0154] Result output unit 604: used to output the abnormality analysis result of the target object.

[0155] In the disclosed embodiment, when at least two financial institutions are determined, account information and operating data corresponding to at least one object may be stored in the financial institution. By acquiring a target object that has account information in any two financial institutions, the empirical data corresponding to the target object in at least two financial institutions can be obtained, so as to perform abnormal analysis on the operating data corresponding to the target object in at least two financial institutions, and obtain abnormal analysis results. The abnormal analysis results include the existence of operating abnormalities or the absence of operating abnormalities, so as to output the abnormal analysis results of the target object. By performing account intersection analysis on any two financial institutions, the target object with multiple financial accounts can be accurately determined, so as to accurately analyze the operating conditions of the target object. In this process, the target object is automatically analyzed, which improves the accuracy and efficiency of the analysis.

[0156] As an embodiment, the first acquisition unit includes:

[0157] A designated acquisition module is used to acquire a target object having account information in any two financial institutions from a designated server; the target object is obtained by performing account information intersection analysis on two financial institutions in at least two financial institutions after the designated server acquires account information corresponding to at least one object respectively sent by at least two financial institutions.

[0158] In a possible design, the acquisition module is specified, including:

[0159] The encryption acquisition submodule is used to obtain object encryption information from a designated server; the object encryption information is obtained by encrypting the target object whose account information exists in any two financial institutions respectively by the designated server;

[0160] The information decryption submodule is used to decrypt the object encrypted information and obtain the target object whose account information exists in any two financial institutions.

[0161] As yet another embodiment, the first acquisition unit includes:

[0162] The data acquisition module is used to acquire the operating data stored in all financial institutions where the target object has account information if it is determined that the target object has account information in any two financial institutions, so as to obtain the operating data corresponding to the target object in at least two financial institutions.

[0163] In some embodiments, the abnormality analysis unit includes:

[0164] A level determination module, used to determine a target object level of a target object;

[0165] A parameter determination module, configured to determine at least one abnormality analysis parameter matching the target object level from the object management strategy according to the multi-level object management strategy; the object management strategy includes at least one abnormality analysis parameter corresponding to each of the multiple object levels;

[0166] A data conversion module, used to determine the analysis data corresponding to at least one abnormal analysis parameter of the target object according to the operating data corresponding to the target object in at least two financial institutions;

[0167] The abnormality judgment module is used to determine the abnormality analysis result of the target object by using the analysis data corresponding to at least one abnormality analysis parameter.

[0168] In a possible design, the abnormality judgment module includes:

[0169] A state determination submodule, for determining a parameter state of a target object corresponding to at least one abnormal analysis parameter using analysis data corresponding to at least one abnormal analysis parameter; the parameter state includes an abnormal state or a normal state;

[0170] The result judgment submodule is used to determine that the abnormal analysis result of the target data is that there is an operating abnormality if the number of abnormal analysis parameters in the parameter state corresponding to at least one abnormal analysis parameter is greater than a predetermined number.

[0171] In some embodiments, the parameter determination module includes:

[0172] A parameter determination submodule, configured to determine, according to the multi-level object management strategy, at least one abnormality analysis parameter matching the target object level and an abnormality threshold value corresponding to each abnormality analysis parameter from the object management strategy;

[0173] The state determination submodule is specifically used for:

[0174] If the analysis data of the abnormal analysis parameter meets the abnormal threshold corresponding to the abnormal analysis parameter, it is determined that the target object is in an abnormal state in the abnormal analysis parameter;

[0175] If the analysis data of the abnormal analysis parameter does not meet the abnormal threshold corresponding to the abnormal analysis parameter, it is determined that the target object is in a normal state under the abnormal analysis parameter;

[0176] A parameter state corresponding to at least one abnormal analysis parameter is obtained.

[0177] In one possible design, it also includes:

[0178] A request acquisition unit, configured to acquire at least one object set by a user and generate an object analysis request corresponding to the at least one object;

[0179] A first sending unit is used to send an object analysis request to at least one financial institution respectively; the object analysis request instructs the financial institution to authorize object analysis on the object;

[0180] The first authorization unit is used to obtain object analysis authorization from at least one financial institution for at least one object.

[0181] In some embodiments, further comprising:

[0182] A request generation unit, configured to determine a data analysis request when performing data analysis on at least one object according to a multi-level object management policy;

[0183] A second sending unit is used to send a data analysis request to at least one financial institution, respectively, and the data analysis request is used to instruct the financial institution to perform a data analysis authorization for confirming the business data analysis of at least one object;

[0184] The second authorization unit is used to obtain data analysis authorization from at least one financial institution to perform business data analysis on at least one object.

[0185] In a possible design, the result output unit includes:

[0186] The result sending module is used to send the abnormal analysis result of the target object to at least two financial institutions where the target object has account information.

[0187] Figure 6 The data analysis device shown can perform Figure 1 For the data analysis method of the embodiments, the specific steps performed by each unit, module or sub-module in the device can be referred to the specific description of the above method, which will not be repeated here.

[0188] like Figure 7 FIG. 1 is a schematic diagram of a structure of another embodiment of a data analysis device provided by an embodiment of the present disclosure. The data analysis device may include the following units:

[0189] The object receiving unit 701 is used to receive account information corresponding to at least one object respectively sent by at least two financial institutions; the account information corresponding to at least one object is fed back by the electronic device instructing at least one financial institution;

[0190] Intersection analysis unit 702: used to perform account information intersection analysis on two financial institutions in at least two financial institutions, and obtain target objects with account information in any two financial institutions;

[0191] The object sending unit 703 is used to send a target object having account information in any two financial institutions to an electronic device, so that the electronic device can obtain the operating data corresponding to the target object in at least two financial institutions, and perform abnormal analysis on the target object to obtain abnormal analysis results.

[0192] In the disclosed embodiment, the designated server may receive account information corresponding to at least one object respectively sent by at least two financial institutions, and the account information corresponding to at least one object is obtained by the electronic device instructing at least one financial institution to feedback. The designated server may obtain the account information corresponding to at least one object to accurately analyze the data on the designated server, isolate account intersection analysis and isolation of abnormal analysis, so that the designated server can accurately analyze the electronic device, reduce the analysis pressure of the electronic device, and improve the analysis efficiency.

[0193] In a possible design, the object sending unit includes:

[0194] An information encryption module is used to encrypt a target object having account information in two arbitrary financial institutions to obtain encrypted information of the object;

[0195] The information sending module is used to send the object encryption information to the electronic device; the object encryption instructs the electronic device to decrypt the object encryption information to obtain the target object whose account information exists in any two financial institutions.

[0196] In some embodiments, the intersection analysis unit comprises:

[0197] The score query and analysis module is used to use a differential privacy algorithm to perform account information intersection analysis on at least two financial institutions.

[0198] Figure 7 The data analysis device shown can execute the data analysis method. For the specific steps executed by each unit, module or sub-module in the device, reference can be made to the specific description of the above method, which will not be repeated here.

[0199] It should be noted that the account information and business data in this embodiment are not information and data for a specific user, and do not reflect the personal information of a specific user. In the technical solution disclosed in this disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0200] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0201] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0202] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0203] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0204] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0205] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as data analysis methods. For example, in some embodiments, the data analysis method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the data analysis method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the data analysis method in any other appropriate manner (e.g., by means of firmware).

[0206] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0207] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0208] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0209] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0210] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0211] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0212] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0213] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data analysis method, characterized in that: include: Identify at least two financial institutions; The financial institution stores account information and operating data corresponding to at least one object respectively; Acquire a target object having account information in any two financial institutions to obtain the operating data corresponding to the target object in at least two financial institutions; determining a target object level of the target object; According to a multi-level object management strategy, determining at least one abnormality analysis parameter matching the target object level from the object management strategy; the object management strategy includes at least one abnormality analysis parameter corresponding to each of the multiple object levels; Determining the analysis data corresponding to at least one of the abnormal analysis parameters of the target object according to the operating data corresponding to the target object in at least two financial institutions; Determine the parameter state of the target object corresponding to at least one of the abnormal analysis parameters using the analysis data corresponding to at least one of the abnormal analysis parameters; The parameter state includes an abnormal state or a normal state; If the number of abnormal analysis parameters in the abnormal state in the parameter state respectively corresponding to at least one of the abnormal analysis parameters is greater than a predetermined number, determining that the abnormal analysis result of the target object is that there is an operational abnormality; Output the abnormality analysis result of the target object.

2. The method according to claim 1, characterized in that The target object whose account information exists in any two financial institutions is obtained, including: A target object having account information in any two financial institutions is obtained from a designated server; the target object is obtained by performing account information intersection analysis on two financial institutions in at least two financial institutions after the designated server obtains account information corresponding to at least one of the objects respectively sent by at least two financial institutions.

3. The method according to claim 2, characterized in that The step of obtaining, from a designated server, a target object whose account information exists in any two financial institutions includes: Obtain object encryption information from a designated server; the object encryption information is obtained by the designated server performing encryption processing on a target object having account information in any two financial institutions; The object encrypted information is decrypted to obtain the target object having account information in any two financial institutions.

4. The method according to claim 1, characterized in that The obtaining of the operating data of the target object in at least two financial institutions respectively includes: If it is determined that the target object has account information in any two financial institutions, the operating data stored in all financial institutions where the target object has account information is obtained to obtain the operating data corresponding to the target object in at least two financial institutions.

5. The method according to claim 1, characterized in that The determining, according to the multi-level object management strategy, at least one abnormality analysis parameter matching the target object level from the object management strategy comprises: According to the multi-level object management strategy, determining from the object management strategy at least one abnormality analysis parameter matching the target object level and an abnormality threshold corresponding to each abnormality analysis parameter; The determining the parameter state of the target object corresponding to at least one of the abnormal analysis parameters by using the analysis data corresponding to at least one of the abnormal analysis parameters respectively includes: If the analysis data of the abnormal analysis parameter meets the abnormal threshold corresponding to the abnormal analysis parameter, it is determined that the target object is in an abnormal state with respect to the abnormal analysis parameter; If the analysis data of the abnormal analysis parameter does not meet the abnormal threshold corresponding to the abnormal analysis parameter, it is determined that the target object is in a normal state with respect to the abnormal analysis parameter; Obtain a parameter state corresponding to at least one of the abnormal analysis parameters.

6. The method according to any one of claims 1 to 5, characterized in that: Also includes: Acquire at least one object set by a user, and generate at least one object analysis request corresponding to the object; sending the object analysis request to at least one of the financial institutions respectively; the object analysis request instructs the financial institution to authorize object analysis on the object; Obtain object analysis authorization from at least one of the financial institutions for at least one of the objects.

7. The method according to any one of claims 1 to 5, characterized in that: Also includes: determining, according to the multi-level object management strategy, a data analysis request when performing data analysis on at least one of the objects; Sending the data analysis request to at least one of the financial institutions respectively, using the data analysis request to instruct the financial institution to conduct data analysis authorization for confirming business data analysis on at least one of the objects; Obtain data analysis authorization from at least one of the financial institutions to perform business data analysis on at least one of the objects.

8. The method according to any one of claims 1 to 5, characterized in that: The outputting the abnormality analysis result of the target object includes: The abnormality analysis result of the target object is sent to at least two financial institutions where the target object has account information.

9. A data analysis method, characterized in that: include: Receiving account information corresponding to at least one object respectively sent by at least two financial institutions; The account information respectively corresponding to the at least one object is fed back by the electronic device instructing at least one of the financial institutions; Perform account information intersection analysis on at least two financial institutions, and obtain target objects that have account information in any two financial institutions; A target object having account information in any two financial institutions is sent to an electronic device so that the electronic device obtains the operating data corresponding to the target object in at least two financial institutions, and determines the target object level of the target object; according to a multi-level object management strategy, at least one abnormal analysis parameter matching the target object level is determined from the object management strategy, the object management strategy includes at least one abnormal analysis parameter corresponding to multiple object levels; according to the operating data corresponding to the target object in at least two financial institutions, the analysis data corresponding to at least one abnormal analysis parameter of the target object is determined; using the analysis data corresponding to at least one abnormal analysis parameter, a parameter state of the target object corresponding to at least one abnormal analysis parameter is determined, the parameter state includes an abnormal state or a normal state; when the number of abnormal analysis parameters in the abnormal state in the parameter state corresponding to at least one abnormal analysis parameter is greater than a predetermined number, it is determined that the abnormal analysis result of the target object is that an operating abnormality exists.

10. The method according to claim 9, characterized in that The sending of the target object having account information in any two financial institutions to the electronic device includes: Encrypting a target object that has account information in two arbitrary financial institutions to obtain encrypted information of the object; The object encryption information is sent to the electronic device, and the electronic device is instructed to decrypt the object encryption information to obtain the target object whose account information exists in any two financial institutions.

11. The method according to claim 9 or 10, characterized in that: The performing account information intersection analysis on two financial institutions in pairs of at least two financial institutions includes: A differential privacy algorithm is used to perform account information intersection analysis on at least two financial institutions.

12. A data analysis device, characterized in that: include: A first determining unit, configured to determine at least two financial institutions; The financial institution stores account information and operating data corresponding to at least one object respectively; A first acquisition unit is used to acquire a target object having account information in any two financial institutions, so as to obtain the operating data corresponding to the target object in at least two financial institutions; an abnormality analysis unit, configured to perform abnormality analysis on the operating data of the target object corresponding to at least two financial institutions, respectively, to obtain an abnormality analysis result; the abnormality analysis result includes whether the target object has an operating abnormality or does not have an operating abnormality; A result output unit, used to output the abnormality analysis result of the target object; The abnormality analysis unit comprises: A level determination module, used to determine the target object level of the target object; A parameter determination module, configured to determine at least one abnormality analysis parameter matching the target object level from the object management strategy according to a multi-level object management strategy; the object management strategy includes at least one abnormality analysis parameter corresponding to each of the multiple object levels; A data conversion module, used to determine the analysis data corresponding to at least one of the abnormal analysis parameters of the target object according to the operating data corresponding to the target object in at least two financial institutions; The abnormality judgment module is used to determine the parameter state of the target object corresponding to at least one of the abnormal analysis parameters using the analysis data corresponding to at least one of the abnormal analysis parameters; the parameter state includes an abnormal state or a normal state; if the number of abnormal analysis parameters in the abnormal state in the parameter state corresponding to at least one of the abnormal analysis parameters is greater than a predetermined number, it is determined that the abnormal analysis result of the target object is that there is an operating abnormality.

13. A data analysis device, characterized in that: include: An object receiving unit, configured to receive account information corresponding to at least one object respectively sent by at least two financial institutions; The account information respectively corresponding to the at least one object is fed back by the electronic device instructing at least one of the financial institutions; An intersection analysis unit, used to perform account information intersection analysis on two financial institutions in at least two financial institutions, and obtain a target object whose account information exists in any two financial institutions; An object sending unit is used to send a target object for which account information exists in any two financial institutions to an electronic device, so that the electronic device obtains the operating data corresponding to the target object in at least two financial institutions, and determines the target object level of the target object; according to a multi-level object management strategy, at least one abnormal analysis parameter matching the target object level is determined from the object management strategy, the object management strategy includes at least one abnormal analysis parameter corresponding to multiple object levels; according to the operating data corresponding to the target object in at least two financial institutions, the analysis data corresponding to at least one abnormal analysis parameter of the target object is determined; using the analysis data corresponding to at least one abnormal analysis parameter, a parameter state of the target object corresponding to at least one abnormal analysis parameter is determined, the parameter state includes an abnormal state or a normal state; when the number of abnormal analysis parameters in the parameter state corresponding to at least one abnormal analysis parameter that is in an abnormal state is greater than a predetermined number, it is determined that the abnormal analysis result of the target object is that an operating abnormality exists.

14. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

15. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 9 to 11.

16. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

17. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 9-11.

18. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 8 when being executed by a processor.

19. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 9 to 11 when being executed by a processor.

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