CN112882904A - Big data-based solution recommendation system - Google Patents
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- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
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Abstract
The invention discloses a big data-based solution recommendation system, which comprises an equipment fault information base, a self-checking detection module, a normal starting detection module, an abnormal starting detection module and a solution matching module, wherein the equipment fault information base is used for counting and storing historical abnormal data information, abnormal symptom expression tables and historical solution information of equipment of each batch identifier, one piece of historical abnormal data information corresponds to one abnormal symptom expression table, one piece of historical abnormal data information corresponds to a plurality of pieces of historical solution information, one piece of historical solution information comprises symptom expressions, historical solution content, fault occurrence rate and feedback success rate, and the abnormal symptom expression table is a table in which the historical abnormal data information corresponds to a set of symptom expressions in the plurality of pieces of historical solution information.
Description
Technical Field
The invention relates to the field of big data, in particular to a big data-based solution recommendation system.
Background
The equipment is inevitably out of order during use due to friction, external forces, stress, electrostatic forces, and the like. In the traditional habit, when equipment breaks down, a special equipment technical maintenance personnel can be found to solve the equipment failure. But it is inefficient to find a dedicated service technician for the equipment if the equipment fails. Both time and money are required, so self-service equipment failure repair is a great trend.
Disclosure of Invention
The invention aims to provide a big data-based solution recommendation system to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
a big-data based solution recommendation system, the recommendation system comprising: the equipment failure information base is used for counting and storing historical abnormal data information, abnormal symptom expression tables and historical solution information of equipment with identification of each batch, wherein one piece of historical abnormal data information corresponds to one abnormal symptom expression table, one piece of historical abnormal data information corresponds to a plurality of pieces of historical solution information, one piece of historical solution information comprises symptom expressions, historical solution content, failure occurrence rate and feedback success rate, the abnormal symptom expression table is a table of a set of symptom expressions in the historical abnormal data information corresponding to the plurality of pieces of historical solution information, the self-checking detection module performs self-checking when the equipment to be recommended is started, and when the equipment to be recommended self-checks abnormal conditions, the method comprises the steps of collecting batch identification of equipment to be recommended, taking current abnormal data information as first abnormal data information, continuously monitoring the running state of the equipment by a normal start detection module when the equipment can be normally started, acquiring abnormal data information when the equipment cannot run in a monitoring process, determining abnormal data information to be matched, monitoring the restarting state of the equipment and the abnormal data information during restarting each time by an abnormal start detection module when the equipment cannot be normally started, determining the abnormal data information to be matched according to the abnormal data information, selecting one piece of historical abnormal data information from an equipment fault information base as matched abnormal data information for the abnormal data information to be matched by a solution matching module, and selecting corresponding historical solution content from the historical abnormal data information.
Preferably, the normal start detection module includes an operation detection module, a second abnormal data information collection module, a similarity comparison module and a restart control module, the operation detection module continuously monitors the operation state of the device, and when the device cannot operate in the monitoring process, the second abnormal data information collection module collects current abnormal data information as second abnormal data information, the similarity comparison module is used for comparing the similarity between the first abnormal data information and the second abnormal data information, when the similarity is greater than or equal to a first similarity threshold, the first abnormal data information is used as abnormal data information to be matched, when the similarity is smaller than the first similarity threshold, the restart control module controls the device to automatically restart, and abnormal data information after the device is restarted is used as abnormal data information to be matched.
Preferably, the abnormal restart detection module comprises a restart state detection module and a manual restart abnormity detection module, the restart state detection module monitors the restart state of the equipment to be recommended in a preset time period when the equipment cannot be normally started, judges that the equipment is manually restarted when the time interval between two restarts in the preset time period is not less than a first time threshold value, and enables the manual restart abnormity detection module to work, judges that the equipment is automatically restarted when the time interval between two restarts in the preset time period is not less than the first time threshold value, takes abnormal data information in the first restart as abnormal data information to be matched, the manual restart abnormity detection module comprises a restart data information comparison module and a power state detection module, the restart data information comparison module is used for acquiring abnormal data information in each restart when the equipment is judged to be manually restarted, if the abnormal data information during each restart is the same, the abnormal data information during the restart is taken as the abnormal data information to be matched, if the abnormal data information during each restart is different, the power state detection module is enabled to work, the power state detection module monitors the time length of the power state of the equipment after each restart, when the time length of the power state of the equipment after a certain restart is kept unchanged is more than or equal to a time length threshold value, the restart is taken as the last restart, and the abnormal data information during the last restart is taken as the abnormal data information to be matched.
Preferably, the solution matching module comprises a batch identifier selection module, a similarity comparison module, matching abnormal data information and a solution selection module, the batch identifier selecting module is used for acquiring historical abnormal data information of equipment which is the same as the batch identifier of the equipment to be recommended in the equipment fault information base, the similarity comparison module is used for comparing the similarity between each piece of historical abnormal data information acquired by the batch identifier selection module and the abnormal data information to be matched of the equipment to be recommended, the matching abnormal data information sorts the comparison results of the similarity comparison module in a descending order, selects the first sorted historical abnormal data information as the matching abnormal data information, and the solution selecting module selects the abnormal solution content of the equipment to be recommended from the historical solution information corresponding to the matched abnormal data information.
Preferably, the solution selection module comprises a symptom performance returning module, a symptom performance similarity comparison module, a matching degree calculation module and a solution content recommendation module, the symptom expression returning module is used for receiving abnormal symptom expressions corresponding to the equipment to be recommended returned by the user, the symptom performance similarity comparison module is used for comparing the similarity between the abnormal symptom performance returned by the symptom performance return module and the symptom performance in each historical solution information corresponding to the matched abnormal data information, the matching degree calculation module calculates the matching degree of each historical solution information according to the symptom expression similarity, the fault occurrence rate and the feedback success rate of each historical solution information, the solution content recommending module sorts each historical solution information according to the sequence from large matching degree to small matching degree, and selects the historical solution content corresponding to the first sorted historical solution information to recommend to the user.
A big-data based solution recommendation method, the recommendation method comprising:
presetting an equipment fault information base, wherein the equipment fault information base statistically stores equipment historical abnormal data information, abnormal symptom expression tables and historical solution information of each batch identifier, one piece of historical abnormal data information corresponds to one abnormal symptom expression table, one piece of historical abnormal data information corresponds to a plurality of pieces of historical solution information, one piece of historical solution information comprises symptom expressions, historical solution contents, fault occurrence rates and feedback success rates, and the abnormal symptom expression table is a table in which the historical abnormal data information corresponds to a set of symptom expressions in the plurality of pieces of historical solution information;
the method comprises the steps that self-checking is conducted when equipment to be recommended is started, if abnormal conditions of the equipment to be recommended are detected, batch identification of the equipment to be recommended is collected, and current abnormal data information is used as first abnormal data information;
if the equipment can be started normally, continuously monitoring the running state of the equipment, if the equipment cannot run in the monitoring process, acquiring abnormal data information when the equipment cannot run, and determining abnormal data information to be matched;
if the equipment can not be started normally, monitoring the restarting state of the equipment and abnormal data information during each restarting and determining abnormal data information to be matched according to the abnormal data information;
selecting one piece of historical abnormal data information from the equipment fault information base as matched abnormal data information for the abnormal data information to be matched, and selecting corresponding historical solution contents from the historical abnormal data information.
Preferably, the obtaining abnormal data information that the device cannot run, and determining the abnormal data information to be matched includes:
continuously monitoring the running state of the equipment, if the equipment cannot run in the monitoring process, acquiring the current abnormal data information as second abnormal data information, wherein the similarity between the first abnormal data information and the second abnormal data information is a first similarity,
if the first similarity is larger than or equal to the first similarity threshold, the first abnormal data information is taken as the abnormal data information to be matched,
and if the first similarity is smaller than the first similarity threshold, the equipment is controlled to be automatically restarted, and the abnormal data information after the equipment is restarted is used as the abnormal data information to be matched.
Preferably, the monitoring the restart state of the device and the abnormal data information at each restart and determining the abnormal data information to be matched according to the restart state of the device includes:
if the equipment cannot be normally started, monitoring the restarting state of the equipment to be recommended in a preset time period, if the time interval between two restarts in the preset time period is not equal to or more than a first time threshold, acquiring abnormal data information during each restart, if the abnormal data information during each restart is the same, taking the abnormal data information during the restart as abnormal data information to be matched, if the abnormal data information during each restart is not the same, monitoring the time length of the power state of the equipment after each restart, if the time length of the power state of the equipment after a certain restart is not equal to or more than a time threshold, taking the abnormal data information during the last restart as the abnormal data information to be matched; and if the times of the equal time intervals between the two restarts in the preset time period are less than the time threshold, taking the abnormal data information during the first restart as the abnormal data information to be matched.
Preferably, the selecting a piece of historical abnormal data information from the equipment fault information base for the abnormal data information to be matched as the matched abnormal data information comprises:
the method comprises the steps of obtaining historical abnormal data information of equipment with the same batch identification as equipment to be recommended in an equipment fault information base, comparing the similarity of the historical abnormal data information with abnormal data information to be matched of the equipment to be recommended, sorting the compared similarities in descending order, and selecting the first sorted historical abnormal data information as the matched abnormal data information.
Preferably, the extracting the corresponding historical solution content from the historical abnormal data information comprises
Transmitting an abnormal symptom expression table corresponding to the historical abnormal data information to a user, and selecting the corresponding abnormal symptom expression of the equipment to be recommended by the user;
the similarity m between the symptom expression in each historical solution information and the symptom expression selected by the user is compared,
then the degree of match for each historical solution information
Z =0.6 × m +0.3 × n +0.1 × q, where n is a failure occurrence rate of the historical solution information, and q is a feedback success rate of the historical solution information;
and sequencing each historical solution information according to the sequence of the matching degree from large to small, and selecting the historical solution content corresponding to the first sequenced historical solution information to recommend to the user.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the restarting state of the equipment in the preset time period is monitored, and the manual restarting equipment and the self-restarting equipment are distinguished, so that the judgment on the abnormal data information of the equipment is more accurate, and the solution recommended according to the abnormal data information is more accurate.
Drawings
FIG. 1 is a block diagram of a big data based solution recommendation system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Referring to fig. 1, in an embodiment of the present invention, a big data based solution recommendation system includes: the equipment failure information base is used for counting and storing historical abnormal data information, abnormal symptom expression tables and historical solution information of equipment with identification of each batch, wherein one piece of historical abnormal data information corresponds to one abnormal symptom expression table, one piece of historical abnormal data information corresponds to a plurality of pieces of historical solution information, one piece of historical solution information comprises symptom expressions, historical solution content, failure occurrence rate and feedback success rate, the abnormal symptom expression table is a table of a set of symptom expressions in the historical abnormal data information corresponding to the plurality of pieces of historical solution information, the self-checking detection module performs self-checking when the equipment to be recommended is started, and when the equipment to be recommended self-checks abnormal conditions, the method comprises the steps of collecting batch identification of equipment to be recommended, taking current abnormal data information as first abnormal data information, continuously monitoring the running state of the equipment by a normal start detection module when the equipment can be normally started, acquiring abnormal data information when the equipment cannot run in a monitoring process, determining abnormal data information to be matched, monitoring the restarting state of the equipment and the abnormal data information during restarting each time by an abnormal start detection module when the equipment cannot be normally started, determining the abnormal data information to be matched according to the abnormal data information, selecting one piece of historical abnormal data information from an equipment fault information base as matched abnormal data information for the abnormal data information to be matched by a solution matching module, and selecting corresponding historical solution content from the historical abnormal data information.
The normal start detection module comprises an operation detection module, a second abnormal data information acquisition module, a similarity comparison module and a restart control module, wherein the operation detection module continuously monitors the operation state of the equipment, and when the equipment cannot operate in the monitoring process, the second abnormal data information acquisition module acquires current abnormal data information as second abnormal data information, the similarity comparison module is used for comparing the similarity between the first abnormal data information and the second abnormal data information, when the similarity is greater than or equal to a first similarity threshold, the first abnormal data information is used as abnormal data information to be matched, when the similarity is smaller than the first similarity threshold, the restart control module controls the equipment to be automatically restarted, and abnormal data information after the equipment is restarted is used as abnormal data information to be matched.
The abnormal restarting detection module comprises a restarting state detection module and a manual restarting abnormity detection module, the restarting state detection module monitors the restarting state of the equipment to be recommended in a preset time period when the equipment can not be normally started, judges that the equipment is manually restarted when the time interval between two times of restarting in the preset time period is not less than a first time threshold value, enables the manual restarting abnormity detection module to work, judges that the equipment is automatically restarted when the time interval between two times of restarting in the preset time period is not less than the first time threshold value, takes abnormal data information in the first restarting as abnormal data information to be matched, the manual restarting abnormity detection module comprises a restarting data information comparison module and a power state detection module, the restarting data information comparison module is used for acquiring abnormal data information in each restarting when the equipment is judged to be manually restarted, if the abnormal data information during each restart is the same, the abnormal data information during the restart is taken as the abnormal data information to be matched, if the abnormal data information during each restart is different, the power state detection module is enabled to work, the power state detection module monitors the time length of the power state of the equipment after each restart, when the time length of the power state of the equipment after a certain restart is kept unchanged is more than or equal to a time length threshold value, the restart is taken as the last restart, and the abnormal data information during the last restart is taken as the abnormal data information to be matched.
The solution matching module comprises a batch identification selection module, a similarity comparison module, matching abnormal data information and a solution selection module, the batch identifier selecting module is used for acquiring historical abnormal data information of equipment which is the same as the batch identifier of the equipment to be recommended in the equipment fault information base, the similarity comparison module is used for comparing the similarity between each piece of historical abnormal data information acquired by the batch identifier selection module and the abnormal data information to be matched of the equipment to be recommended, the matching abnormal data information sorts the comparison results of the similarity comparison module in a descending order, selects the first sorted historical abnormal data information as the matching abnormal data information, and the solution selecting module selects the abnormal solution content of the equipment to be recommended from the historical solution information corresponding to the matched abnormal data information.
The solution selection module comprises a symptom expression return module, a symptom expression similarity comparison module, a matching degree calculation module and a solution content recommendation module, the symptom expression returning module is used for receiving abnormal symptom expressions corresponding to the equipment to be recommended returned by the user, the symptom performance similarity comparison module is used for comparing the similarity between the abnormal symptom performance returned by the symptom performance return module and the symptom performance in each historical solution information corresponding to the matched abnormal data information, the matching degree calculation module calculates the matching degree of each historical solution information according to the symptom expression similarity, the fault occurrence rate and the feedback success rate of each historical solution information, the solution content recommending module sorts each historical solution information according to the sequence from large matching degree to small matching degree, and selects the historical solution content corresponding to the first sorted historical solution information to recommend to the user.
A big-data based solution recommendation method, the recommendation method comprising:
presetting an equipment fault information base, wherein the equipment fault information base statistically stores equipment historical abnormal data information, abnormal symptom expression tables and historical solution information of each batch identifier, one piece of historical abnormal data information corresponds to one abnormal symptom expression table, one piece of historical abnormal data information corresponds to a plurality of pieces of historical solution information, one piece of historical solution information comprises symptom expressions, historical solution contents, fault occurrence rates and feedback success rates, and the abnormal symptom expression table is a table in which the historical abnormal data information corresponds to a set of symptom expressions in the plurality of pieces of historical solution information;
the method comprises the steps that self-checking is conducted when equipment to be recommended is started, if abnormal conditions of the equipment to be recommended are detected, batch identification of the equipment to be recommended is collected, and current abnormal data information is used as first abnormal data information;
if the equipment can be started normally, continuously monitoring the running state of the equipment, if the equipment cannot run in the monitoring process, acquiring abnormal data information when the equipment cannot run, and determining abnormal data information to be matched;
the acquiring abnormal data information when the equipment cannot run, and the determining abnormal data information to be matched comprises the following steps:
continuously monitoring the running state of the equipment, if the equipment cannot run in the monitoring process, acquiring the current abnormal data information as second abnormal data information, wherein the similarity between the first abnormal data information and the second abnormal data information is a first similarity,
if the first similarity is larger than or equal to the first similarity threshold, the first abnormal data information is taken as the abnormal data information to be matched,
and if the first similarity is smaller than the first similarity threshold, the equipment is controlled to be automatically restarted, and the abnormal data information after the equipment is restarted is used as the abnormal data information to be matched.
If the equipment can not be started normally, monitoring the restarting state of the equipment and abnormal data information during each restarting and determining abnormal data information to be matched according to the abnormal data information;
the monitoring of the restarting state of the device and the abnormal data information during each restarting and the determining of the abnormal data information to be matched according to the restarting state of the device comprise:
if the equipment cannot be normally started, monitoring the restarting state of the equipment to be recommended in a preset time period, if the time interval between two restarts in the preset time period is not equal to or more than a first time threshold, acquiring abnormal data information during each restart, if the abnormal data information during each restart is the same, taking the abnormal data information during the restart as abnormal data information to be matched, if the abnormal data information during each restart is not the same, monitoring the time length of the power state of the equipment after each restart, if the time length of the power state of the equipment after a certain restart is not equal to or more than a time threshold, taking the abnormal data information during the last restart as the abnormal data information to be matched; and if the times of the equal time intervals between the two restarts in the preset time period are less than the time threshold, taking the abnormal data information during the first restart as the abnormal data information to be matched.
Selecting one piece of historical abnormal data information from an equipment fault information base as matched abnormal data information for the abnormal data information to be matched, and selecting corresponding historical solution contents from the historical abnormal data information:
the selecting of one piece of historical abnormal data information from the equipment fault information base as the matched abnormal data information for the abnormal data information to be matched comprises the following steps:
the method comprises the steps of obtaining historical abnormal data information of equipment with the same batch identification as equipment to be recommended in an equipment fault information base, comparing the similarity of the historical abnormal data information with abnormal data information to be matched of the equipment to be recommended, sorting the compared similarities in descending order, and selecting the first sorted historical abnormal data information as the matched abnormal data information.
The selecting corresponding historical solution content from the historical abnormal data information comprises:
transmitting an abnormal symptom expression table corresponding to the historical abnormal data information to a user, and selecting the corresponding abnormal symptom expression of the equipment to be recommended by the user;
the similarity m between the symptom expression in each historical solution information and the symptom expression selected by the user is compared,
then the degree of match for each historical solution information
Z =0.6 × m +0.3 × n +0.1 × q, where n is a failure occurrence rate of the historical solution information, and q is a feedback success rate of the historical solution information;
sorting each historical solution information according to the sequence of the matching degrees from large to small, selecting the historical solution content corresponding to the first sorted historical solution information, and recommending the historical solution content to the user
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (3)
1. A big-data-based solution recommendation system, characterized by: the recommendation system comprises an equipment failure information base, a self-checking detection module, a normal start detection module, an abnormal start detection module and a solution matching module, wherein the equipment failure information base is used for counting and storing historical abnormal data information, abnormal symptom expression tables and historical solution information of equipment of each batch identifier, one piece of historical abnormal data information corresponds to one abnormal symptom expression table, one piece of historical abnormal data information corresponds to a plurality of pieces of historical solution information, one piece of historical solution information comprises symptom expressions, historical solution content, failure occurrence rate and feedback success rate, the abnormal symptom expression table is a table of a set of symptom expressions in the historical abnormal data information corresponding to the plurality of pieces of historical solution information, the self-checking detection module performs self-checking when equipment to be recommended is started, and when the equipment to be recommended is subjected to self-checking abnormal conditions, the method comprises the steps that batch identification of equipment to be recommended is collected, current abnormal data information is used as first abnormal data information, a normal start detection module continuously monitors the running state of the equipment when the equipment can be started normally, the abnormal data information when the equipment cannot run in the monitoring process is obtained, the abnormal data information to be matched is determined, an abnormal start detection module monitors the restarting state of the equipment and the abnormal data information when the equipment cannot be started normally, the abnormal data information to be matched is determined according to the abnormal data information, a solution matching module selects one piece of historical abnormal data information from an equipment fault information base as matched abnormal data information for the abnormal data information to be matched, and corresponding historical solution content is selected from the historical abnormal data information;
the abnormal restarting detection module comprises a restarting state detection module and a manual restarting abnormity detection module, the restarting state detection module monitors the restarting state of the equipment to be recommended in a preset time period when the equipment can not be normally started, judges that the equipment is manually restarted when the time interval between two times of restarting in the preset time period is not less than a first time threshold value, enables the manual restarting abnormity detection module to work, judges that the equipment is automatically restarted when the time interval between two times of restarting in the preset time period is not less than the first time threshold value, takes abnormal data information in the first restarting as abnormal data information to be matched, the manual restarting abnormity detection module comprises a restarting data information comparison module and a power state detection module, the restarting data information comparison module is used for acquiring abnormal data information in each restarting when the equipment is judged to be manually restarted, if the abnormal data information during each restart is the same, the abnormal data information during the restart is taken as the abnormal data information to be matched, if the abnormal data information during each restart is different, the power state detection module is enabled to work, the power state detection module monitors the time length of the power state of the equipment after each restart, when the time length of the power state of the equipment after a certain restart is kept unchanged is more than or equal to a time length threshold value, the restart is taken as the last restart, and the abnormal data information during the last restart is taken as the abnormal data information to be matched.
2. The big-data based solution recommendation system according to claim 1, wherein: the solution matching module comprises a batch identifier selection module, an abnormal similarity comparison module, a matching abnormal data information and solution selection module, the batch identifier selecting module is used for acquiring historical abnormal data information of equipment which is the same as the batch identifier of the equipment to be recommended in the equipment fault information base, the abnormal similarity comparison module is used for comparing the similarity between each piece of historical abnormal data information acquired by the batch identifier selection module and the abnormal data information to be matched of the equipment to be recommended, the matching abnormal data information sorts the comparison results of the similarity comparison module in a descending order, selects the first sorted historical abnormal data information as the matching abnormal data information, and the solution selecting module selects the abnormal solution content of the equipment to be recommended from the historical solution information corresponding to the matched abnormal data information.
3. The big-data based solution recommendation system according to claim 2, wherein: the solution selection module comprises a symptom expression return module, a symptom expression similarity comparison module, a matching degree calculation module and a solution content recommendation module, the symptom expression returning module is used for receiving abnormal symptom expressions corresponding to the equipment to be recommended returned by the user, the symptom performance similarity comparison module is used for comparing the similarity between the abnormal symptom performance returned by the symptom performance return module and the symptom performance in each historical solution information corresponding to the matched abnormal data information, the matching degree calculation module calculates the matching degree of each historical solution information according to the symptom expression similarity, the fault occurrence rate and the feedback success rate of each historical solution information, the solution content recommending module sorts each historical solution information according to the sequence from large matching degree to small matching degree, and selects the historical solution content corresponding to the first sorted historical solution information to recommend to the user.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114095536A (en) * | 2021-11-17 | 2022-02-25 | 国网山东省电力公司电力科学研究院 | Electric power transmission and transformation safety prompting system and method based on Internet of things |
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| CN111913825A (en) | 2020-11-10 |
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