Data source instance evolution analysis method, device, electronic device and storage medium

Abstract

The present invention relates to the field of data processing and discloses a method for analyzing the evolution of data source instances, comprising: receiving data reported by a client, extracting a digital fingerprint of the instance from the data, querying from a preset database whether a historical digital fingerprint corresponding to the digital fingerprint is stored, and if a historical digital fingerprint is stored, grouping the digital fingerprint and the second sequence value corresponding to the same basic information in the historical digital fingerprint into a group, calculating the similarity value of the two second sequence values ​​in each group, adding all the calculated similarity values, and using the addition result as a comparison value between the digital fingerprint and the historical digital fingerprint. When it is determined that the comparison value is less than a preset threshold, generating an instance ID of the comparison value and storing it in a database. The present invention also provides an analysis device, electronic device, and storage medium based on the evolution of data source instances. The present invention enables accurate analysis of the evolution of instances deployed in a private cloud.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, electronic device and storage medium for analyzing the evolution of data source instances. Background Art

[0002] In the Software as a Service (SaaS) business, a service provider provides an instance to a service requester and generates an instance ID. During deployment, the instance ID is written to the service requester's public cloud system to track the instance. Typically, the service provider has access to and control over the data source of the SaaS (public cloud) application and therefore does not record the instance ID.

[0003] Private clouds are usually deployed within the firewall of the service requester's data center. The management authority of the private cloud belongs to the service requester. Therefore, the instance ID deployed in the private cloud must be set by the service requester's internal personnel. There are cases of instance ID errors and omissions and the proliferation of the same instance ID. The instance information included in the data reported to the service provider cannot distinguish the specific sources of different instances and their related situations, resulting in the service provider being unable to accurately analyze the evolution of the instance. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method for analyzing the evolution of data source instances, which aims to solve the technical problem in the existing technology that it is impossible to accurately analyze the evolution of instances deployed in a private cloud.

[0005] The data source instance evolution analysis method provided by the present invention is applied to a client of a service requester, and the method includes:

[0006] Acquire, from a preset data source of the service requester, an information set of an instance deployed by a service provider for the service requester;

[0007] Extracting a preset amount of basic information required by the service requester to use the instance from the information set, concatenating the identification IDs contained in the basic information to generate a first sequence value of the basic information;

[0008] Processing the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information;

[0009] The second sequence values ​​of each of the basic information are arranged and combined to generate a digital fingerprint of the instance.

[0010] Optionally, the step of concatenating the identification IDs included in the basic information to generate a first sequence value of the basic information includes:

[0011] Read all identification IDs contained in the basic information and perform splicing to obtain a splicing result;

[0012] The splicing result is encrypted according to a preset encryption algorithm to generate a first sequence value of the basic information.

[0013] Optionally, the processing the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information includes:

[0014] The length of the first sequence value is read, and the length of the first sequence value is truncated or position-completed according to a preset fixed-length algorithm to generate a second sequence value of the basic information.

[0015] Optionally, the permuting and combining the second sequence values ​​of the respective basic information to generate the digital fingerprint of the instance includes:

[0016] Determine the weight value of each of the basic information according to a preset weight rule;

[0017] All second sequence values ​​are arranged in descending order according to the weight values, and the arranged second sequence values ​​are combined to generate the digital fingerprint of the instance.

[0018] A data source instance evolution analysis method, applied to a service provider's server, includes:

[0019] Receive data reported by the client and extract a digital fingerprint of the instance from the data;

[0020] querying a preset database to determine whether a historical digital fingerprint corresponding to the digital fingerprint is stored; if the historical digital fingerprint is stored, grouping the digital fingerprint and the second sequence values ​​corresponding to the same basic information in the historical digital fingerprint into a group, and calculating a similarity value between the two second sequence values ​​in each group;

[0021] Adding all calculated similarity values, and using the added result as a comparison value between the digital fingerprint and the historical digital fingerprint;

[0022] When it is determined that the comparison value is less than a preset threshold, an instance ID of the comparison value is generated and stored in the database.

[0023] Optionally, grouping the second sequence values ​​corresponding to the same basic information in the digital fingerprint and the historical digital fingerprint into one group, and calculating the similarity value of two second sequence values ​​in each group includes:

[0024] Splitting the permutations and combinations of the digital fingerprint to obtain a plurality of second sequence values ​​of the digital fingerprint;

[0025] Splitting the permutations and combinations of the historical digital fingerprints to obtain a plurality of second sequence values ​​of the historical digital fingerprints;

[0026] The two second sequence values ​​corresponding to the same basic information are grouped into one group, and the similarity value of the two second sequence values ​​in each group is calculated.

[0027] In addition, to achieve the above-mentioned purpose, the present invention further provides a data source instance evolution analysis system, which includes:

[0028] The service requester's client is configured to obtain, from a preset data source of the service requester, an information set of an instance deployed for the service requester by the service provider, extract from the information set a preset amount of basic information required for the service requester to use the instance, concatenate the identification IDs contained in the basic information to generate a first sequence value of the basic information, process the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information, and permutate and combine the second sequence values ​​of the basic information to generate a digital fingerprint of the instance;

[0029] The service provider's server side receives data reported by the client, extracts the digital fingerprint of the instance from the data, queries a preset database to determine whether a historical digital fingerprint corresponding to the digital fingerprint is stored; if the historical digital fingerprint is stored, groups the digital fingerprint and the second sequence value corresponding to the same basic information in the historical digital fingerprint into a group, calculates the similarity value of the two second sequence values ​​in each group, adds all the calculated similarity values, and uses the addition result as a comparison value between the digital fingerprint and the historical digital fingerprint; and when it is determined that the comparison value is less than a preset threshold, generates an instance ID for the comparison value and stores it in the database.

[0030] In addition, to achieve the above-mentioned purpose, the present invention also provides an electronic device, which is applied to the client of the service requester. The electronic device includes: a memory, a processor, the processor is electrically connected to the memory, and the memory stores an analysis program for the evolution of a data source instance that can be run on the processor. When the analysis program for the evolution of a data source instance is executed by the processor, the analysis method applied to the evolution of a data source instance as described above is implemented.

[0031] In addition, to achieve the above-mentioned purpose, the present invention also provides an electronic device, which is applied to the server side of a service provider. The electronic device includes: a memory, a processor, the processor is electrically connected to the memory, and the memory stores an analysis program for the evolution of a data source instance that can be run on the processor. When the analysis program for the evolution of a data source instance is executed by the processor, the analysis method applied to the evolution of a data source instance as described above is implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which an analysis program based on the evolution of data source instances is stored. The analysis program based on the evolution of data source instances can be executed by one or more processors to implement the above-mentioned analysis method of data source instance evolution.

[0033] Compared to existing technologies, this invention obtains the information set of the service requester's client about the instance deployed in the private cloud, concatenates, length-processes, and permutes the identification IDs of each basic information in the information set, and generates the instance's digital fingerprint. This automatically generates the instance's digital fingerprint without any input or configuration from the service requester, preventing instances from being missed or duplicated.

[0034] Extract the digital fingerprint from the data reported by the client of the service requester, calculate the similarity value between the digital fingerprint and the historical digital fingerprint corresponding to the digital fingerprint in the database, add up all the similarity values ​​as the comparison value between the digital fingerprint and the historical digital fingerprint, and compare the comparison value with the preset threshold to map the operation status of system instances in different time and space, and realize accurate analysis of the evolution of instances deployed in the private cloud. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a client process of a service requester of a method for analyzing data source instance evolution provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a service-side process flow of a service provider of a method for analyzing the evolution of a data source instance provided in one embodiment of the present invention;

[0037] Figure 3 A schematic diagram of an analysis system for the evolution of data source instances of the present invention;

[0038] Figure 4 A schematic diagram of the structure of an electronic device for implementing a method for analyzing the evolution of data source instances provided by an embodiment of the present invention;

[0039] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0041] It should be noted that the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0042] The present invention provides a method for analyzing the evolution of data source instances. Figure 1 FIG. 1 is a schematic diagram of a client process of a service requester of a method for analyzing evolution of a data source instance provided by an embodiment of the present invention.

[0043] After the service requester purchases the instance provided by the service provider, the service requester installs the instance in the private cloud deployed by the service requester. At the same time, the service provider also installs the instance in the private clouds deployed by other service requesters, and uploads the instance usage data to the service provider as analysis data through the service provider's preset fingerprint module.

[0044] In this embodiment, the data source instance evolution analysis method is applied to the client of the service requester, including:

[0045] S1. Obtaining an information set of an instance deployed by a service provider for the service requester from a preset data source of the service requester;

[0046] In this embodiment, after the service requester purchases the instance provided by the service provider, the service requester installs the instance in the private cloud deployed by the service requester. When the service requester uses the instance, the preset fingerprint module running on the private cloud obtains the information set of the instance deployed by the service provider for the service requester from the preset data source (private cloud) of the service requester; the preset fingerprint module refers to the data collection (data-collector) component developed by the service provider. The fingerprint module is bound to the instance and runs in the private cloud. In addition to collecting data from each instance on the private cloud, the fingerprint module does not collect and store data from the client. In other words, using the fingerprint module to obtain an information set of the client's use of the service provider's instance in the private cloud can be understood as instant calculation when needed. The instance refers to the product or software program run by the service provider in the private cloud. In other embodiments, the preset fingerprint module can also be constructed using other fingerprint collection devices.

[0047] Every time the client reports data to the service provider, the fingerprint module collects and marks the information set of the instance used by the client in the private cloud, processes the information set, and finally generates a digital fingerprint and writes it into the reported data.

[0048] In one embodiment, the basic information included in the information set is: initial effective team basic information of the instance, container environment variables, and the license of the instance;

[0049] The initial valid team basic information of an instance refers to all data of a team that normally uses the service provider's instance. The initial valid team basic information includes the organization's ID and the team's ID.

[0050] Container environment variables refer to the container environment variables that the service provider's instance needs to use when deployed in the private cloud (for example, the basic information of the environment variable includes the container number and the variable value, and the numerical sequence of the container number and the numerical sequence of the variable value are used as the identification ID of the container environment variable respectively);

[0051] The instance license refers to the instance type number and identity sequence. The numerical sequence of the instance type number and the numerical sequence of the identity sequence are used as the identification ID of the instance license. The instance license is used for anti-piracy purposes and has a one-to-one correspondence with the customer's account.

[0052] By obtaining the basic information of these information sets, the data source reported by the client can be ensured. From the reported data deployed by different private clouds, it can be determined which reported data comes from which instance of which private cloud, thereby distinguishing the data source.

[0053] S2. Extracting a preset amount of basic information required by the service requester to use the instance from the information set, concatenating the identification IDs contained in the basic information to generate a first sequence value of the basic information;

[0054] In this embodiment, basic information of a preset number (for example, a preset number of 3) required for the service requester to use the instance is extracted from the information set. Usually, the environmental information on which the instance depends is selected as the basic information. In this embodiment, the initial effective team basic information of the instance, the container environment variables, and the license of the instance are used as the basic information on which the service requester depends to use the instance.

[0055] The basic information of the information set is defined by the service provider. In other embodiments, according to the needs of the business scenario, the team serial number of the instance, the genuine anti-counterfeiting mark, random coding encryption, and the specific encryption algorithm can be obtained as the basic information of the information set. Other information can also be used as the basic information of the information set, which is not limited here.

[0056] The identification ID contained in each basic information is spliced ​​to generate the first sequence value of the basic information; the identification ID is a digital sequence representing the identity of the basic information. Each basic information contains at least two identification IDs. For example, the initial valid team basic information contains the organization's identification ID, the team's identification ID, and the user's identification ID. The digital sequences of the organization's identification ID, the team's identification ID, the user's identification ID, etc. are spliced ​​to generate the first sequence value of the initial valid team basic information.

[0057] In one embodiment, the step of concatenating the identification IDs included in the basic information to generate the first sequence value of the basic information includes:

[0058] Step A10: read all identification IDs included in the basic information and perform splicing to obtain a splicing result;

[0059] In step A20, the splicing result is encrypted according to a preset encryption algorithm to generate a first sequence value of the basic information.

[0060] In this embodiment, the preset encryption algorithm refers to the MD5 encryption algorithm. The MD5 encryption algorithm is a hash function that accepts any input to generate a 32-character hexadecimal string. The MD5 algorithm also has the characteristic of being irreversible.

[0061] The instance's customer structure consists of three levels: organization, team, and customer. Each level is identified by a dedicated random sequence value. The organization ID and team ID are used to ensure the uniqueness of the instance. For example, the organization's ID, team's ID, and user's ID from the instance's initial valid team basic information are read and concatenated in descending order. The concatenated result is encrypted using the MD5 encryption algorithm to generate the first sequence value of the instance's initial valid team basic information.

[0062] The processing steps and logic for each basic information are shared. Because each basic information value is different, the generated sequence value is different. For example, the numerical sequence of the container number and the numerical sequence of the variable value are used as the identification ID of the container environment variable, respectively. The numerical sequence of the instance type number and the numerical sequence of the identity sequence are used as the identification ID of the instance license, respectively. Steps A10-A20 are performed for all identification IDs contained in the respective basic information until the first sequence value of the corresponding basic information is obtained.

[0063] By processing each basic information to generate the first sequence value of each basic information, multiple basic information are processed with minimal or no loss of original data information, thereby reducing the difficulty of data collection and analysis.

[0064] S3. Process the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information;

[0065] Specifically, the S3 step includes:

[0066] The length of the first sequence value is read, and the length of the first sequence value is truncated or position-completed according to a preset fixed-length algorithm to generate a second sequence value of the basic information.

[0067] In this embodiment, the preset fixed-length algorithm refers to a string truncation and completion algorithm. The string length of each first sequence value is different. The length of each first sequence value is truncated or increased to achieve the purpose of consistent length, and a second sequence value of a fixed length is generated.

[0068] For example, since multiple first sequence values ​​are obtained after encryption, the first sequence values ​​of some basic information are 32-bit strings, and the first sequence values ​​of some basic information are 8-bit strings, but the preset fixed-length algorithm is an operation algorithm for 16-bit strings. Then, for the 32-bit string, the middle 16 bits are selected for truncation and the redundant length string is deleted; then for the 8-bit string, it is increased to 16 bits, so that all first sequence values ​​are generated as second sequence values ​​of 16-bit strings.

[0069] By generating a second sequence value of a fixed length, data specification uniformity can be improved, subsequent calculation of digital fingerprint-related loads can be avoided, the access mode in the compiler of the fingerprint module is optimized, and redundant length strings are deleted.

[0070] S4. Arrange and combine the second sequence values ​​of each of the basic information to generate a digital fingerprint of the instance.

[0071] Specifically, the S4 step includes:

[0072] Determine the weight value of each of the basic information according to a preset weight rule;

[0073] All second sequence values ​​are arranged in descending order according to the weight values, and the arranged second sequence values ​​are combined to generate the digital fingerprint of the instance.

[0074] In this embodiment, the preset weight rule refers to a rule defined by the service provider for arranging the positions of the second sequence values ​​in the combination according to the weight value from the time when the code is modified to the time when the program is run and becomes effective.

[0075] After the fixed-length algorithm is applied, the second sequence values ​​for each piece of basic information are obtained. Some of these second sequence values ​​have complex codes, while others are simpler. For complex second sequence values, code modifications require publishing before the program can run, but publishing is often slow and cautious, and partial modifications to these second sequence values ​​are implemented using configuration files. For simple second sequence values, after the fixed-length algorithm is applied, code modifications to these first sequence values ​​take effect without publishing. Each second sequence value is ranked according to its ease of publishing and the weighted value of the program's runtime effectiveness. These ranked second sequence values ​​are then combined to generate the instance's digital fingerprint.

[0076] By permuting and combining the weight values ​​of the second sequence values, the efficiency of generating and publishing digital fingerprints can be improved.

[0077] Through the above steps S1-S4, the digital fingerprint of the data source can be accurately obtained. The implementation is convenient and low-cost. No investment or deployment is required from the customer in advance. The digital fingerprint formed afterwards will not cause any intrusion or impact on the original private cloud system and client.

[0078] The present invention provides a method for analyzing the evolution of data source instances. Figure 2 FIG. 1 is a flow chart of a service provider's service-side process of a method for analyzing the evolution of a data source instance provided by an embodiment of the present invention. The method is executed by an electronic device.

[0079] In this embodiment, the data source instance evolution analysis method is applied to the service provider's server, including:

[0080] S5. Receive data reported by the client, and extract a digital fingerprint of the instance from the data.

[0081] In this embodiment, the digital fingerprint refers to the collection and labeling of data on instances of use of the service requester's client in the private cloud by the fingerprint module, and the processing of the data of these instances to generate a combination of sequence values; each time the service provider receives data reported by the service requester's client, the data is parsed to obtain the instance information set and the digital fingerprint generated by the fingerprint module.

[0082] By obtaining the digital fingerprint, it is ensured that the sensitive information of the client of the service requester is not obtained, thereby ensuring the security of the data of the client of the service requester.

[0083] S6. Querying a preset database to determine whether a historical digital fingerprint corresponding to the digital fingerprint is stored; if the historical digital fingerprint is stored, grouping the digital fingerprint and the second sequence values ​​corresponding to the same basic information in the historical digital fingerprint into a group, and calculating a similarity value between the two second sequence values ​​in each group.

[0084] In this embodiment, the preset database refers to a database independently constructed by the service provider, and other databases may also be used, which is not limited here; the historical digital fingerprint refers to the fingerprint of the previous version of the digital fingerprint; after the service requester deploys the service provider's instance in the private cloud, each time the client initializes the instance, the digital fingerprints generated at different time points are marked with different timestamps to distinguish the versions of the fingerprint.

[0085] Initialization occurs at the beginning of an instance's lifecycle and occurs only once during a lifecycle. Instance initialization obtains necessary global variables, which can be used to compare subtle changes in various areas of the digital fingerprint between versions or at different points in time. A lifecycle spans from the moment an application is opened to the moment it ceases operation.

[0086] In one embodiment, querying a preset database to determine whether a historical digital fingerprint corresponding to the digital fingerprint is stored includes:

[0087] Reading the information set of the instance corresponding to the digital fingerprint;

[0088] The database is queried based on the information set to determine whether any historical digital fingerprints related to the information set are stored.

[0089] Read one or more basic information contained in the digital fingerprint information set (the instance's initial valid team basic information, container environment variables, and the instance's license basic information), and query the database based on the one or more basic information whether historical digital fingerprints related to these basic information are stored. For example, through the instance's license basic information, query the database whether historical version fingerprints (historical digital fingerprints) related to the instance's license basic information are stored.

[0090] By comparing the timestamp query of the digital fingerprint with the historical digital fingerprint, it is possible to clearly distinguish the time point at which the service requester's client reports data each time, and the data comes from which instance in which private cloud device. This avoids the problem in the existing technology that the instance ID of the private cloud deployment has to rely on the internal personnel of the service requester to set it, which may cause instance ID errors and the flooding of the same instance ID.

[0091] In one embodiment, the method further comprises:

[0092] If the historical digital fingerprint is not stored, an instance ID is generated for the digital fingerprint and stored in the database.

[0093] If the query finds that the database does not store any historical digital fingerprints, it means that the digital fingerprint is the first time that the service requester reports data to the service provider after deploying the service provider's instance in the private cloud, that is, the digital fingerprint is the first version of the fingerprint. An instance ID is generated for the digital fingerprint according to the preset ID generation algorithm and stored in the database. The preset ID generation algorithm refers to the snowflake algorithm, which uses the machine ID of the snowflake algorithm, the high-precision timestamp of the current time, and the randomly generated sequence value to perform splicing operations on the digital fingerprint to obtain the instance ID of the digital fingerprint.

[0094] By using the snowflake algorithm to generate instance IDs from digital fingerprints, it can avoid duplication in a highly concurrent distributed environment, with extremely high generation efficiency. In addition, based on the timestamp, it can ensure that the instance ID is an ordered and increasing sequence value, ensuring that each instance ID stored in the database is unique.

[0095] In one embodiment, grouping the second sequence values ​​corresponding to the same basic information in the digital fingerprint and the historical digital fingerprint into one group, and calculating the similarity value of the two second sequence values ​​in each group includes:

[0096] Splitting the permutations and combinations of the digital fingerprint to obtain a plurality of second sequence values ​​of the digital fingerprint;

[0097] Splitting the permutations and combinations of the historical digital fingerprints to obtain a plurality of second sequence values ​​of the historical digital fingerprints;

[0098] The two second sequence values ​​corresponding to the same basic information are grouped into one group, and the similarity value of the two second sequence values ​​in each group is calculated.

[0099] The permutations and combinations of digital fingerprints are split to obtain multiple second sequence values ​​of fixed length for the digital fingerprints; and the permutations and combinations of historical digital fingerprints are split to obtain multiple second sequence values ​​of fixed length for the historical digital fingerprints; based on the same basic information, two second sequence values ​​belonging to the same basic information are grouped together. For example, the two second sequence values ​​corresponding to the initial valid team basic information of the instance are grouped together, the two second sequence values ​​corresponding to the container environment variables are grouped together, and the two second sequence values ​​corresponding to the instance license are grouped together. The similarity between the two second sequence values ​​in each group is then calculated to obtain a similarity value for each group.

[0100] By calculating the similarity between the two second sequence values ​​in each group, we can identify whether the digital fingerprint has undergone fundamental changes relative to the historical digital fingerprint. For example, fundamental changes refer to whether the service requester has replaced a server in the private cloud, added a new server to the private cloud, migrated the service provider's instance to another server in the private cloud, redeployed an instance, or added a new deployed instance. The operational status of system instances in different time and space is mapped to track the instances and record the range of changes in instance data.

[0101] S7. Add all calculated similarity values ​​and use the added result as a comparison value between the digital fingerprint and the historical digital fingerprint.

[0102] In this embodiment, the calculated similarity values ​​of each group are added together, and a double-precision comparison value between 0 and 1 is generated after the addition.

[0103] Adding the similarity values ​​of each group can save system computing resources, avoid the slow efficiency and consumption of system resources caused by complex algorithms, and obtain the comparison value of the digital fingerprint with the historical digital fingerprint, which can effectively determine the actual usage of the instance by the client of the service requester.

[0104] S8. When it is determined that the comparison value is less than a preset threshold, an instance ID of the comparison value is generated and stored in the database.

[0105] In this embodiment, the comparison value between the digital fingerprint and the historical digital fingerprint is compared with a preset value (for example, the preset value is 0.8). If the comparison value is judged to be less than the preset value, it means that the basic change of the digital fingerprint relative to the historical digital fingerprint has exceeded the allowable evolution range, that is, the change of the digital fingerprint relative to the historical digital fingerprint is relatively large. It is possible that the service requester has replaced the server in the private cloud or added a new server in the private cloud, causing it to exceed the allowable evolution range.

[0106] When the comparison value is determined to be less than a preset threshold, an instance ID is generated based on the comparison value according to a preset ID generation algorithm (e.g., the snowflake algorithm) and stored in the database. Since the timestamp of the instance ID generated by the snowflake algorithm is an ordered, increasing, and unique sequence value, by generating a new instance ID based on the comparison value with a large basic change, it is possible to classify which digital fingerprints have significantly evolved relative to the historical digital fingerprints based on the instance ID version, and then derive the usage status changes of the service requester's client based on the timestamps of different instance IDs, thereby improving the service provider's efficiency in subsequent maintenance of the instance and the follow-up and analysis of the instance service.

[0107] In one embodiment, the method further comprises:

[0108] When it is determined that the comparison value is greater than or equal to a preset threshold, the instance ID of the historical digital fingerprint is used as the instance ID of the comparison value and stored in the database.

[0109] The comparison value between the digital fingerprint and the historical digital fingerprint is compared with a preset value (for example, the preset value is 0.8). If the comparison value is greater than or equal to the preset value, it means that the basic change of the digital fingerprint relative to the historical digital fingerprint is within the allowable evolution range, that is, the change of the digital fingerprint relative to the historical digital fingerprint is relatively small, which may be the case that the service requester has redeployed the instance in the private cloud. The timestamp of the instance ID of the historical digital fingerprint is updated according to the preset ID generation algorithm (for example, the snowflake algorithm) to generate a new timestamp. Since the instance ID of the comparison value is the same as the instance ID of the historical digital fingerprint, but the timestamp is different, the different timestamps of the same instance ID can be used to classify which digital fingerprints have not evolved significantly relative to the historical digital fingerprint.

[0110] By comparing the comparison value with the preset value, the service provider can learn about the evolution of the instance deployed in the service requester's private cloud, so that it can accurately track and analyze the instance and provide corresponding service strategies.

[0111] Through the above steps S5-S8, it is possible to effectively and accurately track the instance usage of the data source and map the operation status of the system instances in different time and space. Even after the function of the private cloud system is upgraded, the instances can still be tracked and the change range of the instance data can be recorded.

[0112] Through steps S1-S8 above, the present invention invokes the fingerprint module to obtain and process the information set of the service requester's client regarding the private cloud-deployed instance, generating a digital fingerprint for the instance. This automatically generates the instance's digital fingerprint without requiring any prior input or configuration from the service requester, thus preventing instances from being misidentified or duplicated.

[0113] Extract the digital fingerprint from the data reported by the client of the service requester, calculate the similarity value between the digital fingerprint and the historical digital fingerprint corresponding to the digital fingerprint in the database, add up all the similarity values ​​as the comparison value between the digital fingerprint and the historical digital fingerprint, and compare the comparison value with the preset threshold to map the operation status of system instances in different time and space, so as to accurately track and analyze the evolution of instances deployed in the private cloud.

[0114] like Figure 3 FIG. 1 is a schematic diagram of an analysis system for the evolution of data source instances according to the present invention.

[0115] In an embodiment of the present invention, a data source instance evolution analysis system 100 includes a client 110 of a service requester and a server 120 of a service provider. illustratively:

[0116] The service requester's client is configured to obtain, from a preset data source of the service requester, an information set of an instance deployed for the service requester by the service provider, extract from the information set a preset amount of basic information required for the service requester to use the instance, concatenate the identification IDs contained in the basic information to generate a first sequence value of the basic information, process the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information, and permutate and combine the second sequence values ​​of the basic information to generate a digital fingerprint of the instance;

[0117] The service provider's server side receives data reported by the client, extracts the digital fingerprint of the instance from the data, queries a preset database to determine whether a historical digital fingerprint corresponding to the digital fingerprint is stored; if the historical digital fingerprint is stored, groups the digital fingerprint and the second sequence value corresponding to the same basic information in the historical digital fingerprint into a group, calculates the similarity value of the two second sequence values ​​in each group, adds all the calculated similarity values, and uses the addition result as a comparison value between the digital fingerprint and the historical digital fingerprint; and when it is determined that the comparison value is less than a preset threshold, generates an instance ID for the comparison value and stores it in the database.

[0118] In one embodiment, the step of concatenating the identification IDs included in the basic information to generate the first sequence value of the basic information includes:

[0119] Read all identification IDs contained in the basic information and perform splicing to obtain a splicing result;

[0120] The splicing result is encrypted according to a preset encryption algorithm to generate a first sequence value of the basic information.

[0121] In one embodiment, processing the length of the first sequence value according to a preset fixed-length algorithm to generate the second sequence value of the basic information includes:

[0122] The length of the first sequence value is read, and the length of the first sequence value is truncated or position-completed according to a preset fixed-length algorithm to generate a second sequence value of the basic information.

[0123] In one embodiment, the permuting and combining the second sequence values ​​of the respective basic information to generate the digital fingerprint of the instance includes:

[0124] Determine the weight value of each of the basic information according to a preset weight rule;

[0125] All second sequence values ​​are arranged in descending order according to the weight values, and the arranged second sequence values ​​are combined to generate the digital fingerprint of the instance.

[0126] In one embodiment, grouping the second sequence values ​​corresponding to the same basic information in the digital fingerprint and the historical digital fingerprint into one group, and calculating the similarity value of the two second sequence values ​​in each group includes:

[0127] Splitting the permutations and combinations of the digital fingerprint to obtain a plurality of second sequence values ​​of the digital fingerprint;

[0128] Splitting the permutations and combinations of the historical digital fingerprints to obtain a plurality of second sequence values ​​of the historical digital fingerprints;

[0129] The two second sequence values ​​corresponding to the same basic information are grouped into one group, and the similarity value of the two second sequence values ​​in each group is calculated.

[0130] like Figure 4 , which is a schematic structural diagram of an electronic device for implementing a method for analyzing the evolution of data source instances provided by an embodiment of the present invention.

[0131] In this embodiment, the electronic device 1 includes, but is not limited to, a memory 11, a processor 12, and a network interface 13, which can be interconnected through a system bus. The memory 11 stores an analysis program 10 based on the evolution of data source instances, and the analysis program 10 based on the evolution of data source instances can be executed by the processor 12. Figure 3 Only the electronic device 1 having components 11-13 and the analysis program 10 based on the evolution of the data source instance is shown. It can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and the electronic device 1 may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0132] The memory 11 includes internal memory and at least one type of readable storage medium. The memory provides a cache for the operation of the electronic device 1; the readable storage medium can be a non-volatile storage medium such as flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the readable storage medium can be an internal storage unit of the electronic device 1; in other embodiments, the non-volatile storage medium can also be an external storage device of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. In this embodiment, the readable storage medium of the memory 11 is generally used to store the operating system and various application software installed on the electronic device 1, such as the code of the analysis program 10 based on the evolution of data source instances in one embodiment of the present invention. In addition, the memory 11 can also be used to temporarily store various types of data that have been output or are about to be output.

[0133] In some embodiments, the processor 12 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 12 is generally used to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication with other devices. In this embodiment, the processor 12 is used to execute program code stored in the memory 11 or process data, such as executing the analysis program 10 based on the evolution of the data source instance.

[0134] The network interface 13 may include a wireless network interface or a wired network interface, and the network interface 13 is used to establish a communication connection between the electronic device 1 and a terminal (not shown in the figure).

[0135] Optionally, the electronic device 1 may further include a service requester interface, which may include a display and an input unit such as a keyboard. The optional service requester interface may also include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual service requester interface.

[0136] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0137] The analysis program 10 based on the evolution of data source instances stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 12, it can achieve the following:

[0138] Obtaining, from a preset data source of the service requester, an information set of an instance deployed by a service provider for the service requester, extracting a preset amount of basic information required for the service requester to use the instance from the information set, concatenating identification IDs included in the basic information to generate a first sequence value of the basic information, processing the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information, and permuting and combining the second sequence values ​​of the basic information to generate a digital fingerprint of the instance;

[0139] Receive data reported by the client, extract a digital fingerprint of the instance from the data, query whether a historical digital fingerprint corresponding to the digital fingerprint is stored in a preset database, if the historical digital fingerprint is stored, group the digital fingerprint and the second sequence value corresponding to the same basic information in the historical digital fingerprint, calculate the similarity value of the two second sequence values ​​in each group, add all the calculated similarity values, and use the addition result as the comparison value of the digital fingerprint and the historical digital fingerprint. When it is determined that the comparison value is less than a preset threshold, generate an instance ID of the comparison value and store it in the database.

[0140] Specifically, the specific implementation method of the processor 12 for the above-mentioned analysis program 10 based on the evolution of the data source instance can be referred to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0141] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable medium can be non-volatile or non-volatile. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0142] The computer-readable storage medium stores an analysis program 10 based on the evolution of a data source instance. The analysis program 10 based on the evolution of a data source instance can be executed by one or more processors. The specific implementation of the computer-readable storage medium of the present invention is basically the same as the various embodiments of the analysis method of the above-mentioned data source instance evolution, and will not be repeated here.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0144] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0145] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0146] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0147] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0148] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data source instance evolution analysis method, applied to a service requester's client, characterized in that: The method comprises: Obtaining, from the private cloud of the service requester, an information set of an instance deployed by the service provider for the service requester, where the instance refers to a product or software program running on the private cloud of the service provider; Extracting a preset amount of basic information required by the service requester to use the instance from the information set, concatenating the identification IDs contained in the basic information to generate a first sequence value of the basic information; Processing the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information; According to a preset weight rule, a weight value of each basic information is determined; according to the weight value, all the second sequence values ​​are arranged from high to low, and the arranged second sequence values ​​are combined to generate the digital fingerprint of the instance.

2. The method for analyzing data source instance evolution according to claim 1, characterized in that: The step of concatenating the identification IDs included in the basic information to generate a first sequence value of the basic information includes: Read all identification IDs contained in the basic information and perform splicing to obtain a splicing result; The splicing result is encrypted according to a preset encryption algorithm to generate a first sequence value of the basic information.

3. The method for analyzing the evolution of a data source instance according to claim 1, wherein: The processing of the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information includes: The length of the first sequence value is read, and the length of the first sequence value is truncated or position-completed according to a preset fixed-length algorithm to generate a second sequence value of the basic information.

4. A data source instance evolution analysis method, applied to a service provider's server, characterized in that: The method comprises: receiving data reported by the client, and extracting from the data the digital fingerprint of the instance generated by the method of claim 1; querying a preset database to determine whether a historical digital fingerprint corresponding to the digital fingerprint is stored; if the historical digital fingerprint is stored, grouping the digital fingerprint and the second sequence values ​​corresponding to the same basic information in the historical digital fingerprint into a group, and calculating a similarity value between the two second sequence values ​​in each group; Adding all calculated similarity values, and using the added result as a comparison value between the digital fingerprint and the historical digital fingerprint; When it is determined that the comparison value is less than a preset threshold, an instance ID of the comparison value is generated and stored in the database.

5. The method for analyzing the evolution of a data source instance according to claim 4, wherein: The step of grouping the second sequence values ​​corresponding to the same basic information in the digital fingerprint and the historical digital fingerprint into one group and calculating the similarity value of two second sequence values ​​in each group includes: Splitting the permutations and combinations of the digital fingerprint to obtain a plurality of second sequence values ​​of the digital fingerprint; Splitting the permutations and combinations of the historical digital fingerprints to obtain a plurality of second sequence values ​​of the historical digital fingerprints; The two second sequence values ​​corresponding to the same basic information are grouped into one group, and the similarity value of the two second sequence values ​​in each group is calculated.

6. A data source instance evolution analysis system, characterized in that: The system includes: The client of the service requester is used to obtain an information set of an instance deployed by the service provider for the service requester from the private cloud of the service requester, extract a preset amount of basic information required for the service requester to use the instance from the information set, concatenate the identification IDs contained in the basic information to generate a first sequence value of the basic information, process the length of the first sequence value according to a preset fixed-length algorithm to generate a second sequence value of the basic information, and determine a weight value of each piece of the basic information according to a preset weight rule; arrange all the second sequence values ​​in descending order according to the weight value, and combine the arranged second sequence values ​​to generate a digital fingerprint of the instance, wherein the instance refers to a product or software program run by the service provider in the private cloud; The service provider's server side: receives data reported by the client, extracts the digital fingerprint of the instance generated by the service requester's client from the data, queries whether the historical digital fingerprint corresponding to the digital fingerprint is stored in a preset database, and if the historical digital fingerprint is stored, groups the digital fingerprint and the second sequence value corresponding to the same basic information in the historical digital fingerprint into a group, calculates the similarity value of the two second sequence values ​​in each group, adds all the calculated similarity values, and uses the addition result as the comparison value between the digital fingerprint and the historical digital fingerprint; when it is determined that the comparison value is less than a preset threshold, generates an instance ID for the comparison value and stores it in the database.

7. An electronic device, applied to a client of a service requester, characterized in that: The electronic device includes: a memory and a processor, the processor being electrically connected to the memory, the memory storing an analysis program for the evolution of a data source instance that can be run on the processor, and the analysis program for the evolution of a data source instance, when executed by the processor, implementing the analysis method for the evolution of a data source instance as described in any one of claims 1-3.

8. An electronic device, applied to a service provider's service end, characterized in that: The electronic device includes: a memory and a processor, the processor being electrically connected to the memory, the memory storing an analysis program for the evolution of a data source instance that can be run on the processor, and the analysis program for the evolution of a data source instance, when executed by the processor, implements the analysis method for the evolution of a data source instance as described in any one of claims 4-5.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an analysis program based on the evolution of a data source instance. The analysis program based on the evolution of a data source instance can be executed by one or more processors to implement the analysis method for the evolution of a data source instance as described in any one of claims 1 to 5.

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