CN112231348B - Rule engine intelligent automatic inspection method, system, electronic equipment and device - Google Patents
Rule engine intelligent automatic inspection method, system, electronic equipment and device Download PDFInfo
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Abstract
The application discloses a rule engine intelligent automatic inspection method, a rule engine intelligent automatic inspection system, electronic equipment and a rule engine intelligent automatic inspection device. The intelligent automatic inspection method of the rule engine comprises the following steps: collecting operation data; analyzing the operating data and calculating a first similarity of the operating data and known error data; judging whether the first similarity is greater than or equal to a first preset value or not; and if the first similarity is larger than or equal to a first preset value, confirming the operation data as error data and sending out an early warning signal. Through comparing the similarity of the collected operation data and the known error data, early warning can be performed before problems occur so as to find and solve potential problems in advance, thereby rapidly recovering field service operation and reducing enterprise cost.
Description
Technical Field
The application belongs to the technical field of intelligent operation and maintenance inspection, and particularly relates to a rule engine intelligent automatic inspection method, a rule engine intelligent automatic inspection system, electronic equipment and a rule engine intelligent automatic inspection device.
Background
At present, with continuous progress of information technology, a large amount of network services are more and more popularized, a large amount of data accumulated by business data processing and social networks is more and more, various operation error problems can occasionally occur in the long-term operation process of the services, the operation and maintenance of the current services are popularized, but the problems are always discovered by the operation and maintenance and reported until the problems occur, the efficiency of discovering the problems by field personnel is improved, the investment of labor cost is reduced, the enterprise cost is reduced, and the problem to be solved is needed urgently.
Disclosure of Invention
The application provides a rule engine intelligent automatic inspection method, a rule engine intelligent automatic inspection system, electronic equipment and a rule engine intelligent automatic inspection device, so that the efficiency of finding problems by field personnel is improved.
In order to solve the technical problem, the application adopts a technical scheme that: an intelligent operation and maintenance automatic inspection method for a rule engine comprises the following steps: collecting operation data, wherein the operation data is real-time data generated by a system; analyzing the operating data and calculating a first similarity of the operating data and known error data; judging whether the first similarity is greater than or equal to a first preset value or not; if the first similarity is larger than or equal to a first preset value, determining the operation data as error data, and sending out an early warning signal; if the first similarity is smaller than the first preset value, judging whether the first similarity is larger than a second preset value, wherein the second preset value is smaller than the first preset value; if the first similarity is larger than a second preset value, acquiring previous operation data before the operation data, and integrating the operation data and the previous operation data to obtain integrated data; analyzing the integrated data and calculating a second similarity of the integrated data and the known error data; judging whether the second similarity is greater than or equal to a first preset value or not; and if the second similarity is larger than or equal to a first preset value, confirming the comprehensive data as error data and sending out an early warning signal.
According to an embodiment of the present application, the method includes: if the first similarity is smaller than or equal to the first preset value, judging whether the acquired scene of the running data is a preset scene; if the acquired scene is a preset scene, improving the acquisition frequency of the operation data; acquiring operating data by using the adjusted acquisition frequency; analyzing the operating data and calculating a third similarity of the operating data to the known error data; judging whether the third similarity is greater than or equal to the first preset value or not; and if the third similarity is larger than or equal to the first preset value, determining the operation data as error data, and sending out an early warning signal.
According to an embodiment of the present application, the method includes: if the third similarity is still smaller than the first preset value, reducing the acquisition frequency of the operation data; acquiring operating data by using the adjusted acquisition frequency; analyzing the operating data and calculating a fourth similarity of the operating data and the known error data; judging whether the fourth similarity is greater than or equal to the first preset value or not; and if the fourth similarity is greater than or equal to the first preset value, determining the operation data as error data, and sending out an early warning signal.
According to an embodiment of the present application, the method includes: and storing the operation data confirmed as error data, and updating the known error data by using specific information of the operation data, wherein the specific information comprises a data rule and a solution of the operation data.
According to an embodiment of the present application, the method includes: and analyzing and obtaining the solution of the operating data according to the solution of the known error data.
According to an embodiment of the present application, the method includes: receiving a correction instruction of a user on the solution; saving the corrected solution corrected by the user, and checking the corrected solution and integrating the checked corrected solution into the solution of the known error data.
In order to solve the above technical problem, the present application adopts another technical solution: the utility model provides an automatic system of patrolling and examining, includes data acquisition module, data analysis module and early warning module in advance that interconnect, wherein: the data acquisition module acquires operation data, wherein the operation data is real-time data generated by the system; the data analysis module analyzes the operation data, calculates a first similarity between the operation data and known error data, judges whether the first similarity is greater than a first preset value, and determines the operation data as the error data if the first similarity is greater than or equal to the first preset value; if the first similarity is smaller than the first preset value, judging whether the first similarity is larger than a second preset value, wherein the second preset value is smaller than the first preset value; if the first similarity is larger than a second preset value, the data acquisition module acquires previous operation data before the operation data, and the data analysis module synthesizes the operation data and the previous operation data to acquire synthetic data; the data analysis module analyzes the comprehensive data and calculates a second similarity between the comprehensive data and the known error data; judging whether the second similarity is greater than or equal to a first preset value or not; if the second similarity is larger than or equal to a first preset value, confirming the comprehensive data as error data; and the early warning module receives a signal that the data analysis module confirms that the operating data is wrong data, and sends out a early warning signal.
In order to solve the above technical problem, the present application adopts another technical solution: an electronic device comprises a memory and a processor coupled to each other, wherein the processor is used for executing program instructions stored in the memory to realize the method.
In order to solve the above technical problem, the present application adopts another technical solution: a computer-readable storage medium, on which program data are stored, which program data, when being executed by a processor, carry out the above-mentioned method.
The beneficial effect of this application is: according to the method, early warning can be performed in advance before problems occur by comparing the similarity of the collected operation data and the known error data, so that potential problems can be found and solved in advance, the field service operation can be recovered quickly, and the enterprise cost is reduced. The method can synthesize the operation data and the previous operation data, judge the similarity after obtaining the comprehensive data, and improve the efficiency and the accuracy of finding potential problems.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a flow diagram of an embodiment of a rules engine intelligent automatic inspection method of the present application;
FIG. 2 is another schematic flow diagram of one embodiment of a rules engine intelligent automatic inspection method of the present application of FIG. 1;
FIG. 3 is a schematic flow chart diagram illustrating a further embodiment of a rules engine intelligent automatic inspection method of the present application;
FIG. 4 is a block diagram of an embodiment of an automated inspection system of the present application;
FIG. 5 is a block diagram of an embodiment of an electronic device of the present application;
FIG. 6 is a block diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic flow chart of an embodiment of a rule engine intelligent automatic inspection method according to the present application; fig. 2 is another schematic flow diagram illustrating an embodiment of the rule engine intelligent automatic inspection method of fig. 1.
An embodiment of the application provides an intelligent automatic inspection method for a rule engine, which comprises the following steps:
s101: and collecting operation data.
The method comprises the steps of collecting operation data of the system, wherein the operation data can be real-time data generated by the system applying the method or non-real-time data stored by the system, and when the operation data is real-time data or the data generation time is close to the current time, the operation data is used for analyzing and finding data abnormity before problems occur so as to play a role in early warning. The source of the operational data is the various scenarios of the system.
S102: analyzing the operation data, calculating a first similarity between the operation data and the known error data, and judging whether the first similarity is greater than or equal to a first preset value.
And analyzing the data rule of the operating data, and calculating a first similarity between the data rule of the operating data and the data rule of the known error data in the known error database.
And judging whether the first similarity of the operating data and the known error data in the known error database is greater than or equal to a first preset value.
S103: and if the first similarity is larger than or equal to a first preset value, confirming the operation data as error data and sending out an early warning signal.
If the first similarity is larger than or equal to the first preset value, the operation data is similar to the known error data, the operation data represents that the system is abnormal in operation, errors are likely to occur, and then an early warning signal is sent out to remind a worker to check the system in time, and potential problems are discovered and solved in time.
The first preset value is an empirical value, and can be adjusted according to a large amount of actual data, so that a large amount of error early warning caused by over-low setting of the first preset value is avoided, and the workload of workers is increased; meanwhile, the first preset value is prevented from being set too high, and abnormal data are avoided being omitted.
S104: if the first similarity is smaller than the first preset value, whether the first similarity is larger than a second preset value is judged, and the second preset value is smaller than the first preset value.
If the first similarity is smaller than the first preset value, whether the first similarity is larger than a second preset value is further judged, and the second preset value is smaller than the first preset value.
S105: and if the first similarity is larger than the second preset value, acquiring previous operation data before the operation data, and integrating the operation data and the previous operation data to obtain integrated data.
If the first similarity is greater than the second preset value, the first similarity is similar to the known error data, but the first similarity is still the same as the known error data, and the operating data can be confirmed to be the error data. In order to improve the accuracy of problem discovery, the number of the operation data participating in comparison similarity can be increased, namely, the previous operation data before the operation data is obtained, and the operation data and the previous operation data are synthesized to obtain the comprehensive data. The previous operation data can be operation data of one time or multiple times before the operation data, and the amount of the previous operation data can be preset according to actual needs.
S106: and analyzing the comprehensive data, calculating a second similarity between the comprehensive data and the known error data, and judging whether the second similarity is greater than or equal to a first preset value.
And analyzing the data rule of the comprehensive data, and calculating a second similarity of the comprehensive data and the known error data in the known error database.
And judging whether the second similarity of the comprehensive data and the known error data in the known error database is greater than or equal to a first preset value or not.
S107: and if the second similarity is greater than or equal to the first preset value, determining the comprehensive data as error data, and sending out an early warning signal.
If the second similarity is larger than or equal to the first preset value, the comprehensive data is similar to the known error data, the comprehensive data represents that the system is abnormal in operation, errors are likely to occur, and then an early warning signal is sent out to remind a worker to check the system in time, and potential problems are discovered and solved in time.
S108: and if the second similarity is smaller than the first preset value, the comprehensive data is confirmed to be normal data.
If the second similarity is still smaller than the first preset value, the similarity between the comprehensive data and the known error data in the known error data is very low or not similar, and the comprehensive data is confirmed to be normal data. S109: and if the first similarity is smaller than or equal to a second preset value, judging whether the acquired scene of the operation data is a preset scene.
The acquisition scene of the operation data includes a scene in which the data generation frequency is relatively fixed and a scene in which the data generation frequency is easily changed, such as a third-party service scene. The preset scene is a scene in which the data generation frequency is easy to change.
S110: and if the acquired scene is a preset scene, increasing the acquisition frequency of the operation data, and acquiring the operation data by using the adjusted acquisition frequency.
If the acquired scene is a preset scene, the acquisition frequency of the operation data is possibly too low, so that error data is omitted, the acquisition frequency of the operation data is firstly increased, and the data is operated again by utilizing the increased acquisition frequency, so that the omission of the error data is avoided.
S111: analyzing the operation data, calculating a third similarity between the operation data and the known error data, and judging whether the third similarity is greater than or equal to a first preset value.
And analyzing the data rule of the collected operation data after the collection frequency is increased, and calculating a third similarity between the collected operation data after the collection frequency is increased and the known error data in the known error database.
And judging whether the third similarity between the operation data acquired after the acquisition frequency is increased and the known error data in the known error database is greater than or equal to a first preset value or not.
S112: and if the third similarity is larger than or equal to the first preset value, determining the operation data as error data, and sending out an early warning signal.
If the third similarity is larger than or equal to the first preset value, the operation data is similar to the data rule of the known error data, the operation data represents that the system is abnormal in operation, errors are likely to occur, and therefore an early warning signal is sent out to remind a worker to check the system in time, and potential problems are discovered and solved in time.
S113: and if the third similarity is still smaller than the first preset value, reducing the acquisition frequency of the operation data, and acquiring the operation data by using the adjusted acquisition frequency.
And if the third similarity is still smaller than the first preset value, reducing the acquisition frequency of the operation data, and acquiring the operation data by using the reduced acquisition frequency. It should be noted that the reduced acquisition frequency of the operation data needs to be lower than the acquisition frequency when the operation data is initially acquired.
If the similarity of the collected operation data is still smaller than the first preset value after the collection frequency is increased, the collection frequency of the operation data is tried to be reduced, and error data are prevented from being omitted.
In other embodiments, after the acquisition frequency of the operation data is increased, if the third similarity of the operation data is still smaller than the first preset value, the acquisition frequency of the operation data may be further increased, that is, the steps S111 to S113 are repeated at least once, and if the third similarity of the operation data is still smaller than the first preset value, the step S114 may be executed.
S114: analyzing the operation data, calculating a fourth similarity between the operation data and the known error data, and judging whether the fourth similarity is greater than or equal to a first preset value.
And analyzing the data rule of the operation data, and calculating a fourth similarity between the operation data acquired after the acquisition frequency is reduced and the known error data in the known error database.
And judging whether the fourth similarity between the collected operation data after the collection frequency is reduced and the known error data in the known error database is greater than or equal to a first preset value or not.
S115: and if the fourth similarity is larger than or equal to the first preset value, determining the operation data as error data, and sending out an early warning signal.
If the fourth similarity is larger than or equal to the first preset value, the data rule of the running data is similar to that of the known error data, the running data represents that the system runs abnormally, errors are likely to occur, and therefore an early warning signal is sent out to remind a worker to check the system in time, and potential problems are discovered and solved in time.
S116: and if the fourth similarity is smaller than the first preset value, the operation data is confirmed to be normal data.
If the fourth similarity is still smaller than the first preset value, the similarity between the running data and the known error data in the known error data is very low or not, and the running data is determined to be normal data.
S117: and if the acquired scene is not the preset scene, confirming the operation data as normal data.
If the acquired scene is not the preset scene, the generation frequency of the operation data is stable, the frequency of the initially acquired operation data is matched with the generation frequency of the operation data, and the operation data is confirmed to be normal data if no omission exists.
It should be noted that in other embodiments, it may be further determined whether the first similarity is smaller than a first preset value, and the data similarity is determined by adjusting the acquisition frequency of the operating data.
By comparing the similarity of the collected operation data and the known error data, the method can early warn before problems occur so as to find and solve potential problems in advance. In addition, the method can dynamically adjust the acquisition frequency of the operation data according to the similarity comparison result, and improve the discovery efficiency and accuracy of potential problems.
Referring to fig. 3, fig. 3 is a schematic flow chart of yet another embodiment of the rule engine intelligent automatic inspection method according to the present application.
Another embodiment of the present application provides an intelligent automatic inspection method for a rule engine, including the following steps:
s201: and collecting operation data.
The method comprises the steps of collecting operation data of the system, wherein the operation data can be real-time data or non-real-time data stored by the system, and when the operation data is real-time data or the data generation time is close to the current time, the operation data is used for analyzing and finding data abnormity before problems occur so as to play a role in early warning. The source of the operational data is the various scenarios of the system.
S202: analyzing the operation data, calculating a first similarity between the operation data and the known error data, and judging whether the first similarity is greater than or equal to a first preset value.
And analyzing the data rule of the operation data, and calculating a first similarity between the operation data and the known error data in the known error database.
And judging whether the first similarity of the operating data and the known error data in the known error database is greater than or equal to a first preset value.
S203: and if the first similarity is larger than or equal to a first preset value, determining the operation data as error data, and sending out an early warning signal.
If the first similarity is larger than or equal to the first preset value, the operation data is similar to the known error data, the operation data represents that the system is abnormal in operation, errors are likely to occur, and then an early warning signal is sent out to remind a worker to check the system in time, and potential problems are discovered and solved in time.
The first preset value is an empirical value, and can be adjusted according to a large amount of actual data, so that a large amount of error early warning caused by over-low setting of the first preset value is avoided, and the workload of workers is increased; meanwhile, the situation that the first preset value is set too high and error data are omitted is avoided.
S204: if the first similarity is smaller than the first preset value, whether the first similarity is larger than a second preset value or not is judged, and the second preset value is smaller than the first preset value.
If the first similarity is smaller than the first preset value, whether the first similarity is larger than a second preset value is further judged, and the second preset value is smaller than the first preset value.
S205: and if the first similarity is larger than the second preset value, acquiring previous operation data before the operation data, and integrating the operation data and the previous operation data to obtain integrated data.
If the first similarity is greater than the second preset value, the first similarity is similar to the known error data, but the first similarity is still the same as the known error data, and the operating data can be confirmed to be the error data. In order to improve the accuracy of problem discovery, the number of the operation data participating in comparison similarity can be increased, namely, the previous operation data before the operation data is obtained, and the operation data and the previous operation data are synthesized to obtain the comprehensive data. The previous operation data can be operation data of one time or multiple times before the operation data, and the amount of the previous operation data can be preset according to actual needs.
S206: and if the first similarity is smaller than or equal to a second preset value, the running data is confirmed to be normal data.
S207: and analyzing the comprehensive data, calculating a second similarity of the comprehensive data and the known error data, and judging whether the second similarity is greater than or equal to a first preset value.
And analyzing the data rule of the comprehensive data, and calculating a second similarity of the comprehensive data and the known error data in the known error database.
And judging whether the second similarity of the comprehensive data and the known error data in the known error database is greater than or equal to a first preset value or not.
S208: and if the second similarity is greater than or equal to the first preset value, determining the comprehensive data as error data, and sending out an early warning signal.
If the second similarity is larger than or equal to the first preset value, the comprehensive data is similar to the known error data, the comprehensive data represents that the system is abnormal in operation, errors are likely to occur, and then an early warning signal is sent out to remind a worker to check the system in time, and potential problems are discovered and solved in time.
S209: and if the second similarity is smaller than the first preset value, the comprehensive data is confirmed to be normal data.
If the second similarity is still smaller than the first preset value, the similarity between the comprehensive data and the known error data in the known error data is very low or not similar, and the comprehensive data is confirmed to be normal data.
S210: and analyzing the solution of the obtained operation data according to the solution of the known error data.
According to the known solution of the error data, the specific situation of the operation data confirmed as the error data is combined, and the solution of the operation data is obtained through intelligent analysis, so that the idea of solving the problem is rapidly provided for the working personnel, and the field service operation is rapidly recovered. The solution may be a specific solution or solution direction, or a solution instruction that can only restart the system, etc.
It should be noted that the solution may be obtained from a solution of known error data stored in the system, or may be obtained by analyzing after intelligently sorting external data from the cloud server.
S211: and storing the operation data confirmed as the error data, and updating the known error data by using the specific information of the operation data.
If the operation data is confirmed to be error data, the error data is stored, and the known error data is updated by using the specific information of the operation data, so that the known error data in the known error database is more accurate and perfect, and the subsequent further judgment on the operation data is facilitated. The specific information comprises a data rule and a solution of the operation data, the data rule of the operation data is used for updating known error data, the finding efficiency and accuracy of potential problems can be improved, and the solution of the operation data can improve the efficiency and accuracy of solving problems by workers, so that the field service operation is quickly recovered, and the enterprise cost is reduced.
S212: and receiving a correction instruction of the solution from a user.
The user, namely the staff can modify the solution given after the intelligent analysis by combining the experience of the user and submit a better solution mode of certain wrong operation data so as to update and perfect the solution.
S213: and storing the corrected solution corrected by the user, and checking the corrected solution and integrating the checked corrected solution into the solution with known error data.
And storing the corrected solution after the user corrects, checking the corrected solution, and integrating the corrected solution into the solution with known error data after confirming the feasibility and the legality of the corrected solution.
According to the method, the similarity between the collected operation data and the known error data is compared, early warning can be performed before the problem occurs, and the potential problem can be found and solved in advance. In addition, the method can also obtain a solution for determining the operation data of the error data, and improve the efficiency and accuracy of solving problems by workers, so that the field service operation is quickly recovered, and the enterprise cost is reduced.
Referring to fig. 4, fig. 4 is a schematic diagram of a framework of an embodiment of an automatic inspection system according to the present application.
Yet another embodiment of the present application provides an automatic inspection system 30, which includes a data acquisition module 31, a data analysis module 32, an early warning module 33, a frequency calculation module 34, a knowledge database module 35, and an error learning module 36, which are coupled to each other. Wherein the data acquisition module 31 acquires operational data. The data analysis module 32 analyzes the operation data, calculates a first similarity between the operation data and known error data, judges whether the first similarity is greater than a first preset value, and determines the operation data as the error data if the first similarity is greater than or equal to the first preset value; and if the first similarity is smaller than a first preset value, judging whether the acquired scene of the operation data is a preset scene. The early warning module 33 receives the signal that the data analysis module 32 confirms that the operation data is error data, and sends out a warning signal. The frequency calculation module 34 receives a signal indicating that the acquisition scene of the operation data is a preset scene determined by the data analysis module 32, and adjusts the acquisition frequency of the operation data. The knowledge database module 35 analyzes the solution to obtain operational data based on the solution to known error data. The user can correct the solution through the error learning module 36, the error learning module 36 stores the operation data confirmed as the error data, and updates the known error data by using the specific information of the operation data, wherein the specific information includes the data rule and the solution of the operation data.
The automated inspection system 30 further includes an external learning module 37 that is coupled to the knowledge graph base module 35, and the external learning module 37 integrates the corrected solutions into known error data solutions after verification of the corrected solutions for saving the corrected solutions by the user.
The system can early warn in advance before problems occur by comparing the similarity of the collected operation data and known error data so as to find and solve potential problems in advance; furthermore, the system can dynamically adjust the acquisition frequency of the operation data according to the similarity comparison result, and the discovery efficiency and accuracy of potential problems are improved. In addition, the system can also obtain a solution of operation data confirmed as error data, and improve the efficiency and accuracy of solving problems by workers, so that the field service operation is quickly recovered, and the enterprise cost is reduced.
Referring to fig. 5, fig. 5 is a schematic diagram of a frame of an embodiment of an electronic device according to the present application.
Yet another embodiment of the present application provides an electronic device 40, which includes a memory 41 and a processor 42 coupled to each other, wherein the processor 42 is configured to execute program instructions stored in the memory 41 to implement the rule engine intelligent automatic inspection method according to any one of the above embodiments. In one particular implementation scenario, electronic device 40 may include, but is not limited to: a microcomputer, a server, and the electronic device 40 may also include a mobile device such as a notebook computer, a tablet computer, and the like, which is not limited herein.
In particular, processor 42 is configured to control itself and memory 41 to implement the steps of any of the above-described rule engine intelligent automated inspection method embodiments. Processor 42 may also be referred to as a CPU (Central Processing Unit). The processor 42 may be an integrated circuit chip having signal processing capabilities. The Processor 42 may also be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. In addition, the processor 42 may be commonly implemented by an integrated circuit chip.
Referring to fig. 6, fig. 6 is a block diagram illustrating an embodiment of a computer-readable storage medium according to the present application.
Yet another embodiment of the present application provides a computer-readable storage medium 50 having stored thereon program data 51, the program data 51 when executed by a processor implementing the rules engine intelligent automatic inspection method of any of the above embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is only one type of logical division, and other divisions may be implemented in practice, for example, the unit or component may be combined or integrated with another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on network elements. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium 50. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium 50 and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium 50 includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (9)
1. An intelligent operation and maintenance automatic inspection method for a rule engine is characterized by comprising the following steps:
collecting operation data, wherein the operation data is real-time data generated by a system;
analyzing the operating data and calculating a first similarity of the operating data and known error data;
judging whether the first similarity is greater than or equal to a first preset value or not;
if the first similarity is larger than or equal to a first preset value, determining the operation data as error data, and sending out an early warning signal;
if the first similarity is smaller than the first preset value, judging whether the first similarity is larger than a second preset value, wherein the second preset value is smaller than the first preset value;
if the first similarity is larger than a second preset value, acquiring previous operation data before the operation data, and integrating the operation data and the previous operation data to obtain integrated data;
analyzing the integrated data and calculating a second similarity of the integrated data and the known error data;
judging whether the second similarity is greater than or equal to a first preset value or not;
and if the second similarity is larger than or equal to a first preset value, confirming the comprehensive data as error data and sending out an early warning signal.
2. The method of claim 1, comprising:
if the first similarity is smaller than or equal to the second preset value, judging whether the acquired scene of the operation data is a preset scene;
if the acquired scene is a preset scene, improving the acquisition frequency of the operation data;
acquiring operating data by using the adjusted acquisition frequency;
analyzing the operating data and calculating a third similarity of the operating data to the known error data;
judging whether the third similarity is greater than or equal to the first preset value or not;
and if the third similarity is larger than or equal to the first preset value, determining the operation data as error data, and sending out an early warning signal.
3. The method of claim 2, comprising:
if the third similarity is still smaller than the first preset value, reducing the acquisition frequency of the operation data;
acquiring operating data by using the adjusted acquisition frequency;
analyzing the operating data and calculating a fourth similarity of the operating data and the known error data;
judging whether the fourth similarity is greater than or equal to the first preset value or not;
and if the fourth similarity is greater than or equal to the first preset value, determining the operation data as error data, and sending out an early warning signal.
4. The method of claim 1, comprising:
and storing the operation data confirmed as error data, and updating the known error data by using specific information of the operation data, wherein the specific information comprises a data rule and a solution of the operation data.
5. The method of claim 1, comprising:
and analyzing and obtaining the solution of the operating data according to the solution of the known error data.
6. The method of claim 4, comprising:
receiving a correction instruction of the solution from a user;
saving the corrected solution corrected by the user, and checking the corrected solution and integrating the checked corrected solution into the solution of the known error data.
7. The utility model provides an automatic system of patrolling and examining which characterized in that, includes data acquisition module, data analysis module and early warning module in advance that interconnect, wherein:
the data acquisition module acquires operation data, wherein the operation data is real-time data generated by the system;
the data analysis module analyzes the operation data, calculates a first similarity between the operation data and known error data, judges whether the first similarity is greater than a first preset value, and determines the operation data as the error data if the first similarity is greater than or equal to the first preset value; if the first similarity is smaller than the first preset value, judging whether the first similarity is larger than a second preset value, wherein the second preset value is smaller than the first preset value; if the first similarity is larger than a second preset value, the data acquisition module acquires previous operation data before the operation data, and the data analysis module synthesizes the operation data and the previous operation data to acquire synthetic data; the data analysis module analyzes the comprehensive data and calculates a second similarity between the comprehensive data and the known error data; judging whether the second similarity is greater than or equal to a first preset value or not; if the second similarity is larger than or equal to a first preset value, confirming the comprehensive data as error data;
and the early warning module receives a signal that the data analysis module confirms that the operating data is wrong data, and sends out a early warning signal.
8. An electronic device comprising a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which program data are stored, which program data, when being executed by a processor, carry out the method of any one of claims 1 to 6.
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