CN117170962A - Data detection and maintenance method of software engineering database based on AI intelligence - Google Patents
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Abstract
The invention provides a data detection and maintenance method of a software engineering database based on AI intelligence, which comprises the following steps: acquiring preset operation programs of a software engineering database and corresponding call data thereof, and determining the data call relevance of each preset operation program to establish a data network; program operation characteristics of a preset operation program are obtained, and whether the current operation program has abnormal operation or not is judged according to the program operation characteristics; when the current running program has running abnormality, carrying out abnormality tracing on the current running program, determining abnormal data and acquiring data risk characteristics corresponding to the abnormal data based on a data network; and generating a target maintenance strategy based on the data risk characteristics, and maintaining the abnormal data according to the target maintenance strategy. The method and the device for confirming the abnormal data of the software program have the advantages that whether the abnormal data exist or not is more accurately confirmed, the abnormal data are confirmed, timely maintenance is conducted according to a preset maintenance strategy after the abnormal data are confirmed, the running data safety of the software program is guaranteed to the greatest extent, and user experience is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a data detection and maintenance method of a software engineering database based on AI intelligence.
Background
Databases are organized, sharable collections of data that are stored in a computer for a long period of time. The data in the database is organized, described and stored together in a certain data model, has the characteristics of minimum redundancy, higher data independence and easy expansibility, and can be shared for a plurality of users in a certain range. The data set has the following characteristics: the method is not repeated as much as possible, and is used for managing and controlling various application services of a specific organization in an optimal mode, wherein the data structure is independent of application programs using the data structure, and the data addition, deletion, modification and check are managed and controlled by unified software. With the increase of software users, the data access amount of the software engineering database is greatly increased, and whether the database is abnormal or not is evaluated by detecting whether the user terminal can successfully receive the data in the database, so that the detection burden of the software engineering database is increased, meanwhile, the situation that the database is detected to have errors and cannot be maintained correctly due to the use difference of the user terminal is avoided, and meanwhile, the data detection and maintenance method of the software engineering database based on AI intelligence is provided due to the fact that the abnormality is found and the time difference exists between waiting for management personnel to process and the use risk is large.
Disclosure of Invention
The invention provides a data detection and maintenance method of a software engineering database based on AI intelligence, which is used for solving the problems. The invention realizes intelligent detection and maintenance of the software engineering database, detects the call data in the software engineering database, more objectively detects the software engineering database from the angle of software program operation, effectively avoids detection difference caused by personal use difference of users, more accurately confirms whether abnormal data exists and confirms the abnormal data, timely maintains the abnormal data according to a preset maintenance strategy after confirming the abnormal data, eliminates time difference between abnormal discovery and processing of the database, furthest ensures data safety of the software program operation, and improves user experience.
The invention provides a data detection and maintenance method of a software engineering database based on AI intelligence, which comprises the following steps:
step 1: acquiring preset operation programs of a software engineering database and corresponding call data thereof, determining the data call relevance of each preset operation program, and establishing a data network based on the data call relevance;
step 2: program operation characteristics of a preset operation program are obtained, and whether the current operation program has abnormal operation or not is judged according to the program operation characteristics;
step 3: when the current running program has running abnormality, carrying out abnormality tracing on the current running program, determining abnormal data, and acquiring data risk characteristics corresponding to the abnormal data based on a data network;
step 4: and generating a target maintenance strategy based on the data risk characteristics, and maintaining the abnormal data according to the target maintenance strategy.
Preferably, in the data detection and maintenance method of the software engineering database based on AI intelligence, step 1 includes:
determining the type of a preset running program of the software engineering database based on a pre-stored directory of the software engineering database, and generating a program list;
according to the program list, call data corresponding to a preset operation program are sequentially obtained, and the call data corresponding to the preset operation program are respectively identified based on an identification model, so that an identification result is obtained;
determining the data call relevance of each preset running program based on the identification result;
according to the data call relevance, determining the relevance between each preset running program, and establishing a data network based on the relevance.
Preferably, in the data detection and maintenance method of the software engineering database based on the AI intelligence, step 2 includes:
based on the data type and the data logic of the call data corresponding to the preset operation program, respectively obtaining the program operation characteristics corresponding to each preset operation program;
determining a current running program based on real-time detection data of a software engineering database, acquiring current running characteristics corresponding to the current running program, screening all preset running programs based on identification marks of the current running program, and determining a matching program;
comparing the program running characteristics of the matched program with the current running characteristics, and judging that the current running program has running abnormality when the current running program is judged to be the same as the corresponding program running characteristics;
otherwise, judging that the running of the current running program is normal.
Preferably, in step 3 of a data detection and maintenance method of a software engineering database based on AI intelligence, the method includes:
when the running of the current running program is abnormal, taking the current running program as an abnormal program, and confirming first call data corresponding to the abnormal program based on a program list;
marking the first call data in the data network to obtain a plurality of marked data nodes;
acquiring second call data corresponding to the abnormal program, comparing the second call data with the first call data, and confirming the abnormal data;
comparing the abnormal data with the data corresponding to the marked data nodes, confirming the abnormal data nodes, and obtaining node connection characteristics of the abnormal data nodes;
and determining data risk characteristics corresponding to the abnormal data based on the node connection characteristics.
Preferably, the method for acquiring the node connection characteristics of the abnormal data node in the data detection and maintenance method of the software engineering database based on the AI intelligence comprises the following steps:
based on a data network, acquiring an association program of an abnormal data node, determining association complexity of the abnormal data node, and obtaining a first connection characteristic of the abnormal data node;
meanwhile, program operation characteristics corresponding to the associated programs are obtained, logic characteristics of the abnormal data corresponding to the abnormal data nodes in each associated program are determined based on the program operation characteristics, and second connection characteristics are obtained;
the first connection feature and the second connection feature are used as node connection features of the abnormal data node.
Preferably, in a data detection and maintenance method of a software engineering database based on AI intelligence, based on node connection characteristics, data risk characteristics corresponding to abnormal data are extracted, including:
evaluating the risk level of the abnormal data based on the node connection characteristics to obtain an evaluation result;
and generating data risk characteristics by combining the node connection characteristics based on the risk assessment results.
Preferably, in a data detection and maintenance method of a software engineering database based on AI intelligence, the risk level of abnormal data is evaluated based on node connection characteristics, including:
based on the node connection characteristics, logic characteristics of the abnormal data in each associated program are obtained, and based on the logic characteristics, the operation constraint degree of the abnormal data in each associated program is determined;
determining the data attribute of the abnormal data according to the logic characteristics, and judging whether the abnormal data is key call data in the associated program based on the program operation framework;
if yes, taking the association program as a first association program, and temporarily storing the corresponding operation constraint degree of the first association program into a first association set;
if not, using the association program as a second association program, and temporarily storing the corresponding operation constraint degree of the second association program into a second association set;
respectively acquiring data weight distribution values corresponding to the first association set and the second association set, and calculating the data importance degree of the abnormal data based on the data weight distribution values and the corresponding operation constraint degrees to obtain a first risk parameter;
the association complexity corresponding to the abnormal data node is used as a second risk parameter;
and calculating to obtain a risk evaluation value of the abnormal data according to the first risk parameter, the second risk parameter and the corresponding parameter weights.
Preferably, in the data detection and maintenance method of the software engineering database based on AI intelligence, the risk level of the abnormal data is evaluated based on the node connection feature, and the method further includes:
when the risk assessment value is smaller than or equal to a preset value, judging that the abnormal data is primary risk, and sending a conventional early warning signal to the management terminal;
and when the risk assessment value is larger than a preset value, judging the abnormal data as secondary risk, and sending an emergency early warning signal to the management terminal.
Preferably, in step 4 of a data detection and maintenance method of a software engineering database based on AI intelligence, the method includes:
confirming the maintenance level based on the risk assessment level in the data risk characteristics, and judging that the abnormal data maintenance level is primary maintenance when the abnormal data is primary risk;
otherwise, the abnormal data maintenance level is second-level maintenance;
generating a target maintenance strategy according to the abnormal data maintenance level and combining the data risk characteristics;
and maintaining the abnormal data based on the target maintenance strategy.
Preferably, in step 4 of a data detection and maintenance method of a software engineering database based on AI intelligence, the method further comprises:
and generating a maintenance log of the software engineering database while maintaining the abnormal data, and sending the maintenance log to the management terminal.
Compared with the prior art, the invention at least comprises the following beneficial effects:
the method comprises the steps of determining the data call relevance of each preset operation program for acquiring the preset operation program and the corresponding call data of the software engineering database, and establishing a data network based on the data call relevance; program operation characteristics of a preset operation program are taken, and whether the current operation program has abnormal operation or not is judged according to the program operation characteristics; when the current running program has running abnormality, the current running program carries out abnormality tracing, abnormal data is determined, a target maintenance strategy is generated based on data risk characteristics, the abnormal data is maintained according to the target maintenance strategy, intelligent detection and maintenance of a software engineering database are realized, call data are detected in the software engineering database, the software engineering database is more objectively detected from the angle of running the software program, detection difference caused by personal use difference of a user is effectively avoided, whether the abnormal data exist or not and whether the abnormal data are confirmed are more accurately confirmed, and after the abnormal data are confirmed, timely maintenance is carried out according to a preset maintenance strategy, so that the time difference between abnormal discovery and processing of the database is eliminated, the running data safety of the software program is ensured to the greatest extent, and the user experience is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method for detecting and maintaining data of an AI-intelligent-based software engineering database of the present invention;
FIG. 2 is a flowchart of the data detection and maintenance method step 1 of the AI-intelligent-based software engineering database of the present invention;
FIG. 3 is a flowchart of the data detection and maintenance method step 2 of the AI-intelligent-based software engineering database of the present invention;
FIG. 4 is a flowchart of the data detection and maintenance method step 3 of the AI-intelligent-based software engineering database of the present invention;
fig. 5 is a flowchart of the data detection and maintenance method step 4 of the AI-intelligent-based software engineering database of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the invention provides a data detection and maintenance method of a software engineering database based on AI intelligence, as shown in figure 1, comprising the following steps:
step 1: acquiring preset operation programs of a software engineering database and corresponding call data thereof, determining the data call relevance of each preset operation program, and establishing a data network based on the data call relevance;
step 2: program operation characteristics of a preset operation program are obtained, and whether the current operation program has abnormal operation or not is judged according to the program operation characteristics;
step 3: when the current running program has running abnormality, carrying out abnormality tracing on the current running program, determining abnormal data, and acquiring data risk characteristics corresponding to the abnormal data based on a data network;
step 4: and generating a target maintenance strategy based on the data risk characteristics, and maintaining the abnormal data according to the target maintenance strategy.
In this embodiment, the preset operation program is an operation program of all operations that can be completed by the software engineering database.
In this embodiment, the data call correlation refers to the correlation between data to be called when the preset running program runs.
In this embodiment, the data network is a data network that generates a call data chain according to call data of a preset running program, and connects related parts of the data chain in an overlapping manner based on data call relevance, so as to generate the call data chain.
In this embodiment, the program running feature includes a call data type and a logical relationship of call data.
In this embodiment, the current running program refers to a preset running program currently running in the software engineering database.
In this embodiment, the anomaly tracing refers to an anomaly data node that confirms an anomaly operation, and the anomaly data refers to call data corresponding to the anomaly data node.
In this embodiment, the data risk features include risk level evaluation results of the abnormal data and node connection features of abnormal data nodes corresponding to the abnormal data.
In this embodiment, the target maintenance policy refers to a policy selected from a maintenance policy library and suitable for maintaining current abnormal data.
The beneficial effects of the technical scheme are that: the method comprises the steps of determining the data call relevance of each preset operation program for acquiring the preset operation program and the corresponding call data of the software engineering database, and establishing a data network based on the data call relevance; program operation characteristics of a preset operation program are taken, and whether the current operation program has abnormal operation or not is judged according to the program operation characteristics; when the current running program has running abnormality, the current running program carries out abnormality tracing, abnormal data is determined, a target maintenance strategy is generated based on data risk characteristics, the abnormal data is maintained according to the target maintenance strategy, intelligent detection and maintenance of a software engineering database are realized, call data are detected in the software engineering database, the software engineering database is more objectively detected from the angle of running the software program, detection difference caused by personal use difference of a user is effectively avoided, whether the abnormal data exist or not and whether the abnormal data are confirmed are more accurately confirmed, and after the abnormal data are confirmed, timely maintenance is carried out according to a preset maintenance strategy, so that the time difference between abnormal discovery and processing of the database is eliminated, the running data safety of the software program is ensured to the greatest extent, and the user experience is improved.
Example 2:
on the basis of embodiment 1, as shown in fig. 2, step 1 includes:
step 101: determining the type of a preset running program of the software engineering database based on a pre-stored directory of the software engineering database, and generating a program list;
step 102: according to the program list, call data corresponding to a preset operation program are sequentially obtained, and the call data corresponding to the preset operation program are respectively identified based on an identification model, so that an identification result is obtained;
step 103: determining the data call relevance of each preset running program based on the identification result;
step 104: according to the data call relevance, determining the relevance between each preset running program, and establishing a data network based on the relevance.
In this embodiment, the prediction directory refers to a program storage directory in the software engineering database.
In this embodiment, the program list refers to a corresponding list including all preset running programs generated according to a directory pre-stored in a database.
In this embodiment, the recognition result is to confirm whether the same call data exists in each preset running program.
The beneficial effects of the technical scheme are that: according to the method, a program catalog containing all preset programs is generated according to a pre-stored catalog, each program in a software engineering database is guaranteed to be detected, the comprehensiveness of database detection is guaranteed, call data corresponding to the preset operation programs are sequentially obtained according to a program list, the call data corresponding to the preset operation programs are respectively identified based on an identification model, the call relevance of the data of each preset operation program is determined based on an identification result, the relevance between the preset operation programs is determined, a data network is established based on the relevance, and reference basis is provided for risk assessment of abnormal data by relevance between the data in the software engineering database and direct relevance of each operation program.
Example 3:
on the basis of embodiment 1, as shown in fig. 3, step 2 includes:
step 201: based on the data type and the data logic of the call data corresponding to the preset operation program, respectively obtaining the program operation characteristics corresponding to each preset operation program;
step 202: determining a current running program based on real-time detection data of a software engineering database, acquiring current running characteristics corresponding to the current running program, screening all preset running programs based on identification marks of the current running program, and determining a matching program;
step 203: comparing the program running characteristics of the matched program with the current running characteristics, and judging that the current running program has running abnormality when the current running program is judged to be the same as the corresponding program running characteristics;
otherwise, judging that the running of the current running program is normal.
In this embodiment, the matching program refers to a program that is the same as the current running program in the preset running program.
In this embodiment, the current operation feature refers to a program operation feature of a program currently operated.
In this embodiment, the identification mark refers to a program identification mark carried by each running program in the software engineering database, so as to facilitate confirmation of the identity of the running program.
The beneficial effects of the technical scheme are that: according to the method, the program running characteristics of the matched program are compared with the current running characteristics to judge whether the current running program is abnormal or not, and the corresponding running data is detected in the data calling stage of the program running, so that intelligent detection of a database is realized, abnormal data is effectively prevented from accessing the user terminal, and the risk of tampering of the user data is reduced.
Example 4:
on the basis of embodiment 1, as shown in fig. 4, step 3 includes:
step 301: when the running of the current running program is abnormal, taking the current running program as an abnormal program, and confirming first call data corresponding to the abnormal program based on a program list;
step 302: marking the first call data in the data network to obtain a plurality of marked data nodes;
step 303: acquiring second call data corresponding to the abnormal program, comparing the second call data with the first call data, and confirming the abnormal data;
step 304: comparing the abnormal data with the data corresponding to the marked data nodes, confirming the abnormal data nodes, and obtaining node connection characteristics of the abnormal data nodes;
step 305: and determining data risk characteristics corresponding to the abnormal data based on the node connection characteristics.
In this embodiment, the abnormal program refers to a current running program having an abnormal running.
In this embodiment, the first call data refers to data currently called by the abnormal program; the second call data refers to call data corresponding to a normal preset running program corresponding to the abnormal program.
In this embodiment, the marked data node is a node where the abnormal program call data is located in the data network.
In this embodiment, the node connection feature includes a first connection feature and a second connection feature.
The beneficial effects of the technical scheme are that: when confirming that the current running program has running abnormality, the method takes the current running program as an abnormal program, compares the abnormal program with call data of a normal program corresponding to the abnormal program to trace the abnormality, determines abnormal data, effectively improves the maintenance accuracy of a database, compares the abnormal data with data corresponding to marked data nodes, confirms the abnormal data nodes, acquires node connection characteristics of the abnormal data nodes, determines data risk characteristics corresponding to the abnormal data based on the node connection characteristics, provides screening basis for determining maintenance strategies of the abnormal data, and ensures that the abnormal data can be timely and accurately maintained.
Example 5:
on the basis of embodiment 4, acquiring node connection characteristics of the abnormal data node includes:
based on a data network, acquiring an association program of an abnormal data node, determining association complexity of the abnormal data node, and obtaining a first connection characteristic of the abnormal data node;
meanwhile, program operation characteristics corresponding to the associated programs are obtained, logic characteristics of the abnormal data corresponding to the abnormal data nodes in each associated program are determined based on the program operation characteristics, and second connection characteristics are obtained;
the first connection feature and the second connection feature are used as node connection features of the abnormal data node.
In this embodiment, the association program refers to all preset running programs including data on the data node corresponding to the abnormal data.
In this embodiment, the first connection feature refers to the association complexity of the abnormal data node.
In this embodiment, the second connection feature refers to a logic feature of the abnormal data corresponding to the abnormal data node in each associated program, where the logic feature includes a data connection logic and a data execution logic.
The beneficial effects of the technical scheme are that: the method comprises the steps of acquiring an associated program of an abnormal data node from a data network, determining the association complexity of the abnormal data node, obtaining a first connection characteristic of the abnormal data node, simultaneously acquiring a program operation characteristic corresponding to the associated program, and determining logic characteristics of abnormal data corresponding to the abnormal data node in each associated program based on the program operation characteristic to obtain a second connection characteristic; the first connection feature and the second connection feature are used as node connection features of the abnormal data nodes, the connection features of the abnormal data in the software engineering database are considered from two dimensions, the importance degree of the abnormal data nodes can be comprehensively and objectively confirmed, and a basis is provided for acquiring a target maintenance strategy.
Example 6:
based on embodiment 4, based on the node connection feature, extracting a data risk feature corresponding to the abnormal data includes:
evaluating the risk level of the abnormal data based on the node connection characteristics to obtain an evaluation result;
and generating data risk characteristics by combining the node connection characteristics based on the risk assessment results.
The beneficial effects of the technical scheme are that: according to the method, the risk level of the abnormal data is evaluated based on the node connection characteristics, the evaluation result is obtained, the node connection characteristics are combined to generate the data risk characteristics, a screening basis is provided for maintenance of a target strategy, the abnormal data is ensured to be maintained in accordance with the risk characteristics, and the data safety of the data in the software engineering database is ensured.
Example 7:
on the basis of embodiment 6, evaluating the risk level of the abnormal data based on the node connection characteristics includes:
based on the node connection characteristics, logic characteristics of the abnormal data in each associated program are obtained, and based on the logic characteristics, the operation constraint degree of the abnormal data in each associated program is determined;
determining the data attribute of the abnormal data according to the logic characteristics, and judging whether the abnormal data is key call data in the associated program based on the program operation framework;
if yes, taking the association program as a first association program, and temporarily storing the corresponding operation constraint degree of the first association program into a first association set;
if not, using the association program as a second association program, and temporarily storing the corresponding operation constraint degree of the second association program into a second association set;
respectively acquiring data weight distribution values corresponding to the first association set and the second association set, and calculating the data importance degree of the abnormal data based on the data weight distribution values and the corresponding operation constraint degrees to obtain a first risk parameter;
the association complexity corresponding to the abnormal data node is used as a second risk parameter;
and calculating to obtain a risk evaluation value of the abnormal data according to the first risk parameter, the second risk parameter and the corresponding parameter weights.
In this embodiment, the operation constraint degree refers to a data length ratio of all call data of the associated program corresponding to the abnormal data and a comprehensive evaluation value of the degree of influence of the abnormal data on the implementation of each associated program.
In this embodiment, the key call data refers to the implementation key point of the current running program.
In this embodiment, the first association program refers to an association program in which abnormal data is key call data; the second associated program refers to an associated program in which the exception data is not critical call data.
In this embodiment, the first association set refers to a data set for temporarily storing operation constraint degree data corresponding to the first association program; the second association set refers to a data set for temporarily storing the operation constraint degree data corresponding to the second association program. The data in the first and second association sets are automatically cleared after the risk assessment value calculation is completed.
In this embodiment, the data weight distribution value refers to a calculated weight value corresponding to a preset first association set and a second association set.
In this embodiment, the first risk parameter refers to a data importance degree calculation result of the abnormal data. And classifying the associated programs, calculating according to the data weight distribution values to obtain the data importance degree of the abnormal data, and improving the maintenance processing efficiency of the abnormal data and accelerating the abnormal book processing process while ensuring that each associated program is considered.
In this embodiment, the second risk parameter refers to the association complexity of the abnormal data in the node of the data network.
In this embodiment, the parameter weight refers to a calculated weight value of a preset first risk parameter and a preset second risk parameter.
The beneficial effects of the technical scheme are that: the invention obtains the risk assessment value of the abnormal data from the association complexity degree of the abnormal data in the nodes of the data network and the data importance degree of the abnormal data, and comprehensively and intuitively reflects the influence of the abnormal data on the safety of the database.
Example 8:
on the basis of embodiment 7, evaluating the risk level of the abnormal data based on the node connection feature further includes:
when the risk assessment value is smaller than or equal to a preset value, judging that the abnormal data is primary risk, and sending a conventional early warning signal to the management terminal;
and when the risk assessment value is larger than a preset value, judging the abnormal data as secondary risk, and sending an emergency early warning signal to the management terminal.
The beneficial effects of the technical scheme are that: according to the risk assessment method, the risk grade assessment is carried out on the abnormal data based on the risk assessment value, intelligent grading is carried out, meanwhile, early warning is carried out on management staff, the database is prevented from being attacked maliciously, the existing risk cannot be solved by the maintenance strategies in the maintenance strategy library, in addition, early warning signals are distinguished according to the risk grade during abnormal early warning, the management staff can conveniently and rapidly confirm the emergency degree of risk processing through the early warning signals, and therefore the viewing sequence of the rapid risk maintenance results is determined.
Example 9:
on the basis of example 6, as shown in fig. 5, step 4 includes:
step 401: confirming the maintenance level based on the risk assessment level in the data risk characteristics, and judging that the abnormal data maintenance level is primary maintenance when the abnormal data is primary risk;
otherwise, the abnormal data maintenance level is second-level maintenance;
step 402: generating a target maintenance strategy according to the abnormal data maintenance level and combining the data risk characteristics;
step 403: and maintaining the abnormal data based on the target maintenance strategy.
In this embodiment, the primary maintenance is intelligent autonomous maintenance of the database.
In this embodiment, the secondary maintenance is intelligent autonomous maintenance and manual intervention maintenance of the database, and after the intelligent autonomous maintenance of the database is completed according to the target policy, the database needs to wait for manual checking confirmation before the maintenance of the abnormal data can be ended.
In this embodiment, generating the target maintenance policy based on the abnormal data maintenance level in combination with the data risk feature includes:
when the maintenance level corresponding to the abnormal data is primary maintenance, acquiring first backup data of the current running program, and replacing call data of the abnormal program corresponding to the abnormal data based on the first backup data;
when the maintenance level corresponding to the abnormal data is the second-level maintenance, acquiring second backup data of all associated programs, comparing the second backup data, and determining the same call data;
comparing the same call data with the abnormal data, determining an abnormal data segment, replacing the abnormal data segment based on the same call data, acquiring coding characters of the abnormal data segment, and determining abnormal coding characters;
determining the current data abnormality reason based on the abnormality code character, acquiring historical abnormality data and the data abnormality reason thereof, and determining the abnormality frequency corresponding to the current data abnormality reason;
and when the abnormal frequency is greater than a threshold value, generating a firewall upgrade strategy based on the current data abnormal reason, and sending the firewall upgrade strategy to a management terminal to request the firewall upgrade.
The first backup data refers to backup data corresponding to all call data of the current running program; the second backup data is backup data corresponding to all call data corresponding to all associated programs corresponding to the abnormal data.
The same call data refers to the same part of call data corresponding to all associated programs.
The abnormal data segment refers to a data segment in which an abnormality actually occurs in the abnormal data.
The abnormal frequency refers to the number of times that the cause of the current data abnormality occurs within a preset time.
According to the method, the accuracy of data maintenance is guaranteed by adopting different maintenance strategies according to different abnormal risks, when the maintenance level corresponding to the abnormal data is primary maintenance, the first backup data of the current running program is obtained, the calling data of the abnormal program corresponding to the abnormal data is replaced based on the first backup data, the abnormal data is replaced rapidly, and the maintenance efficiency of the database is guaranteed; when the maintenance level corresponding to the abnormal data is the second-level maintenance, the abnormal data is replaced and maintained by combining all associated programs, the accuracy of data replacement is ensured, errors caused by single program replacement are avoided, after the abnormal data replacement is completed, whether the protection wall needs to be conveniently updated on the current data abnormal reason or not is judged according to the abnormal frequency of the current data abnormal reason, and when the abnormal frequency is greater than a threshold value, a firewall upgrading strategy is generated based on the current data abnormal reason and sent to a management terminal, firewall upgrading is requested, and the data security level of a software engineering database is improved.
The beneficial effects of the technical scheme are that: confirming the maintenance level based on the risk assessment level in the data risk characteristics, and judging that the abnormal data maintenance level is primary maintenance when the abnormal data is primary risk; otherwise, the abnormal data maintenance level is the second-level maintenance, and a target maintenance strategy is generated according to the abnormal data maintenance level and the data risk characteristics; and the abnormal data is maintained based on the target maintenance strategy, so that the autonomous maintenance of the software engineering database is realized, the time difference between the abnormal discovery and the processing of the database is eliminated, the abnormal data is ensured to be processed in time, the guarantee is provided for the normal operation of the software engineering database program, and the use risk of a user is reduced.
Example 10:
on the basis of embodiment 1, step 4 further includes:
and generating a maintenance log of the software engineering database while maintaining the abnormal data, and sending the maintenance log to the management terminal.
The beneficial effects of the technical scheme are that: the invention generates the maintenance log of the software engineering database while maintaining the abnormal data, and sends the maintenance log to the management terminal, thereby being beneficial to the manager to check the maintenance process of the current abnormal data, facilitating the intervention of manual maintenance, simultaneously completing the reservation of maintenance trace and being beneficial to the manager to know the abnormal condition of the database in time.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The data detection and maintenance method of the software engineering database based on the AI intelligence is characterized by comprising the following steps:
step 1: acquiring preset operation programs of a software engineering database and corresponding call data thereof, determining the data call relevance of each preset operation program, and establishing a data network based on the data call relevance;
step 2: program operation characteristics of a preset operation program are obtained, and whether the current operation program has abnormal operation or not is judged according to the program operation characteristics;
step 3: when the current running program has running abnormality, carrying out abnormality tracing on the current running program, determining abnormal data, and acquiring data risk characteristics corresponding to the abnormal data based on a data network;
step 4: and generating a target maintenance strategy based on the data risk characteristics, and maintaining the abnormal data according to the target maintenance strategy.
2. The method for detecting and maintaining data in an AI-intelligent-based software engineering database according to claim 1, wherein step 1 comprises:
determining the type of a preset running program of the software engineering database based on a pre-stored directory of the software engineering database, and generating a program list;
according to the program list, call data corresponding to a preset operation program are sequentially obtained, and the call data corresponding to the preset operation program are respectively identified based on an identification model, so that an identification result is obtained;
determining the data call relevance of each preset running program based on the identification result;
according to the data call relevance, determining the relevance between each preset running program, and establishing a data network based on the relevance.
3. The method for detecting and maintaining data in a software engineering database based on AI intelligence of claim 1, wherein step 2 comprises:
based on the data type and the data logic of the call data corresponding to the preset operation program, respectively obtaining the program operation characteristics corresponding to each preset operation program;
determining a current running program based on real-time detection data of a software engineering database, acquiring current running characteristics corresponding to the current running program, screening all preset running programs based on identification marks of the current running program, and determining a matching program;
comparing the program running characteristics of the matched program with the current running characteristics, and judging that the current running program has running abnormality when the current running program is judged to be the same as the corresponding program running characteristics;
otherwise, judging that the running of the current running program is normal.
4. The method for detecting and maintaining data in a software engineering database based on AI intelligence of claim 1, wherein step 3 comprises:
when the running of the current running program is abnormal, taking the current running program as an abnormal program, and confirming first call data corresponding to the abnormal program based on a program list;
marking the first call data in the data network to obtain a plurality of marked data nodes;
acquiring second call data corresponding to the abnormal program, comparing the second call data with the first call data, and confirming the abnormal data;
comparing the abnormal data with the data corresponding to the marked data nodes, confirming the abnormal data nodes, and obtaining node connection characteristics of the abnormal data nodes;
and determining data risk characteristics corresponding to the abnormal data based on the node connection characteristics.
5. The AI-intelligence-based data detection and maintenance method of a software engineering database of claim 4, wherein obtaining node connection characteristics of an abnormal data node comprises:
based on a data network, acquiring an association program of an abnormal data node, determining association complexity of the abnormal data node, and obtaining a first connection characteristic of the abnormal data node;
meanwhile, program operation characteristics corresponding to the associated programs are obtained, logic characteristics of the abnormal data corresponding to the abnormal data nodes in each associated program are determined based on the program operation characteristics, and second connection characteristics are obtained;
the first connection feature and the second connection feature are used as node connection features of the abnormal data node.
6. The method for detecting and maintaining data in a software engineering database based on AI intelligence of claim 5, wherein extracting data risk features corresponding to abnormal data based on node connection features comprises:
evaluating the risk level of the abnormal data based on the node connection characteristics to obtain an evaluation result;
and generating data risk characteristics by combining the node connection characteristics based on the risk assessment results.
7. The AI-intelligence-based data detection and maintenance method of a software engineering database of claim 6, wherein evaluating risk levels of abnormal data based on node connection characteristics includes:
based on the node connection characteristics, logic characteristics of the abnormal data in each associated program are obtained, and based on the logic characteristics, the operation constraint degree of the abnormal data in each associated program is determined;
determining the data attribute of the abnormal data according to the logic characteristics, and judging whether the abnormal data is key call data in the associated program based on the program operation framework;
if yes, taking the association program as a first association program, and temporarily storing the corresponding operation constraint degree of the first association program into a first association set;
if not, using the association program as a second association program, and temporarily storing the corresponding operation constraint degree of the second association program into a second association set;
respectively acquiring data weight distribution values corresponding to the first association set and the second association set, and calculating the data importance degree of the abnormal data based on the data weight distribution values and the corresponding operation constraint degrees to obtain a first risk parameter;
the association complexity corresponding to the abnormal data node is used as a second risk parameter;
and calculating to obtain a risk evaluation value of the abnormal data according to the first risk parameter, the second risk parameter and the corresponding parameter weights.
8. The AI-intelligent-based data detection and maintenance method of a software engineering database of claim 7, wherein the risk level of the abnormal data is assessed based on node connection characteristics, further comprising:
when the risk assessment value is smaller than or equal to a preset value, judging that the abnormal data is primary risk, and sending a conventional early warning signal to the management terminal;
and when the risk assessment value is larger than a preset value, judging the abnormal data as secondary risk, and sending an emergency early warning signal to the management terminal.
9. The method for detecting and maintaining data in a software engineering database based on AI intelligence as claimed in claim 6, wherein step 4 comprises:
confirming the maintenance level based on the risk assessment level in the data risk characteristics, and judging that the abnormal data maintenance level is primary maintenance when the abnormal data is primary risk;
otherwise, the abnormal data maintenance level is second-level maintenance;
generating a target maintenance strategy according to the abnormal data maintenance level and combining the data risk characteristics;
and maintaining the abnormal data based on the target maintenance strategy.
10. The method for detecting and maintaining data in a software engineering database based on AI intelligence of claim 1, step 4, further comprising:
and generating a maintenance log of the software engineering database while maintaining the abnormal data, and sending the maintenance log to the management terminal.
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| CN117873786B (en) * | 2024-01-12 | 2024-07-05 | 北京华乐思教育科技有限公司 | Intelligent maintenance and update system and method for education platform |
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