CN116562846A - Industrial robot-based production line equipment maintenance prediction method and device - Google Patents
Industrial robot-based production line equipment maintenance prediction method and device Download PDFInfo
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Abstract
The application relates to a production line equipment maintenance prediction method and device based on an industrial robot, comprising the steps of receiving real-time alarm information and equipment special data of the industrial robot; inputting the real-time alarm information and special data into a risk prediction model, and outputting a risk position and a risk level; and determining alarm and risk reasons based on the risk position and risk level data in combination with an expert database, and automatically generating production line equipment maintenance strategy suggestions based on the industrial robot. The method comprises the steps of establishing a device tree model and a structure of subdivision granularity, refining and systemizing device subdivision dimensions, and establishing a scientific expert experience library of subdivision granularity; according to the equipment tree model and subdivision granularity of the production line, data association and attribution are carried out, standardized alarm and special data model analysis and association system expert database are realized, scientificity, rationality and subdivision granularity of predictive maintenance suggestions are ensured, and intelligent decision of equipment predictive maintenance management is achieved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a production line equipment maintenance prediction method and device based on an industrial robot.
Background
The industrial robot is a multi-joint manipulator or a multi-degree-of-freedom machine device facing the industrial field, can automatically execute work, and is a machine which realizes various functions by self power and control capability. The robot can be commanded by human beings, can operate according to a preset program, and can also act according to the principle formulated by artificial intelligence technology.
Industrial robots can replace people in industrial production to do some monotonous, frequent and repeated long-time operations or operations under dangerous and severe environments, such as processes of stamping, pressure casting, heat treatment, welding, coating, plastic product forming, machining, simple assembly and the like, and in departments of atomic energy industry and the like, to carry or process operation of harmful materials to human bodies.
How to deploy maintenance strategies of industrial robots is a technical problem to be solved at present.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problem to be solved by the application is to provide a production line equipment maintenance prediction method and device based on an industrial robot.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the present application provides an industrial robot-based production line equipment maintenance prediction method, the method comprising:
receiving real-time alarm information and equipment special data of the industrial robot;
inputting the real-time alarm message and the equipment special data into a trained risk prediction model, and outputting a risk position and a risk level;
and combining the risk position and the risk level with an expert database to determine alarm and risk reasons, thereby obtaining maintenance strategy suggestions of production line equipment of the industrial robot.
Further, the method further comprises:
acquiring a component composition unit of the production line equipment, wherein the component composition unit comprises a motor, a gear box, a control cabinet and a track, and acquiring all unit components of each component composition unit;
taking the production line equipment as a root node, taking the component forming units as stem nodes, taking all unit components in each component forming unit as leaf nodes, and constructing an equipment tree of the production line equipment;
taking the leaf nodes as objects to obtain original alarm and equipment special data of each leaf node, and taking the stem nodes as objects to obtain the original alarm and equipment special data of each stem node;
dividing the historical alarm and equipment special data information into a training set and a testing set;
creating the risk prediction model based on the equipment tree, dividing the training set into a plurality of subsets, each subset serving as a branch of a current node, and training the risk prediction model;
and pruning the risk prediction model by using the test set.
Further, the step of combining the risk position and the risk level with an expert database to determine an alarm and a risk reason so as to obtain a maintenance strategy suggestion of the production line equipment of the industrial robot specifically includes:
acquiring a first device sending out the real-time alarm message, and acquiring a second device where the alarm fault and the risk position are located;
if the first equipment and the second equipment are different, respectively sending a probing instruction to the first equipment and the second equipment;
when the response message sent by the first device is not received within a preset time period, the first device is used as the device to be checked;
when the response message sent by the second device is not received within a preset time period, the second device is used as the device to be checked;
when the response message sent by the first device is not received within a preset time period, and the response message sent by the first device is not received, the first device and the second device are used as devices to be checked;
and sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment.
Further, the sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment specifically includes:
acquiring historical operation data of the equipment to be checked, determining equipment with fault early warning according to the historical operation data, the real-time alarm information, the equipment special data, the risk level, the alarm fault and risk positions and the alarm reason data, and performing maintenance strategy suggestion on the equipment with the fault early warning.
Further, the historical operating data includes: the equipment to be checked is started and stopped times, equipment OEE, special fault rate, equipment special data change trend, application scene, working condition parameters of the equipment to be checked and environment parameters of the equipment to be checked are started.
Further, the steps of inputting the real-time alarm message and the equipment special data into a trained risk prediction model, and outputting a risk position and a risk level include:
traversing from the root node of the risk prediction model, traversing the child nodes of the root node of the risk prediction model if the root node of the risk prediction model and the risk level in the real-time alarm information and the equipment special data are the same, entering the child node branches if the alarm equipment of the real-time alarm information and the equipment special data is the same as the child nodes of the root node, and traversing the child node branches to obtain the risk reasons corresponding to the change trend of the real-time alarm information and the special data.
In a second aspect, the present application provides an industrial robot-based production line equipment maintenance prediction apparatus, the apparatus comprising:
the first processing module is used for receiving real-time alarm information and equipment special data of the industrial robot;
the second processing module is used for inputting the real-time alarm message and the equipment special data into a trained risk prediction model and outputting a risk position and a risk level;
and the third processing module is used for combining the risk position and the risk level with an expert database to determine alarm and risk reasons so as to obtain maintenance strategy suggestions of production line equipment of the industrial robot.
Further, the device also comprises a fourth processing module, a second processing module and a third processing module, wherein the fourth processing module is used for acquiring a component composition unit of the production line device, the component composition unit comprises a motor, a gear box, a control cabinet and a track, and all unit components of each component composition unit are acquired;
taking the production line equipment as a root node, taking the component forming units as stem nodes, taking all unit components in each component forming unit as leaf nodes, and constructing an equipment tree of the production line equipment;
taking the leaf nodes as objects to obtain original alarm and equipment special data of each leaf node, and taking the stem nodes as objects to obtain the original alarm and equipment special data of each stem node;
dividing the historical alarm and equipment special data information into a training set and a testing set;
creating the risk prediction model based on the equipment tree, dividing the training set into a plurality of subsets, each subset serving as a branch of a current node, and training the risk prediction model;
and pruning the risk prediction model by using the test set.
Further, the third processing module is specifically configured to obtain a first device that sends the real-time alarm message, and obtain a second device where the alarm fault and the risk location are located;
if the first equipment and the second equipment are different, respectively sending a probing instruction to the first equipment and the second equipment;
when the response message sent by the first device is not received within a preset time period, the first device is used as the device to be checked;
when the response message sent by the second device is not received within a preset time period, the second device is used as the device to be checked;
when the response message sent by the first device is not received within a preset time period, and the response message sent by the first device is not received, the first device and the second device are used as devices to be checked;
and sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment.
Further, the third processing module is specifically configured to obtain historical operation data of the device to be checked, determine a device with a fault early warning according to the historical operation data, a real-time alarm message, device specific data, the risk level, the alarm fault and risk location, and the alarm reason data, and perform a maintenance policy suggestion step on the device with the fault early warning.
The beneficial effects of this application are: the industrial robot-based production line equipment maintenance prediction method comprises the steps of receiving real-time alarm information and equipment special data of an industrial robot; inputting the real-time alarm information and the equipment special data into a risk prediction model, and outputting a risk position and a risk level; and determining alarm and risk reasons based on the risk position and risk level data in combination with an expert database, and automatically generating production line equipment maintenance strategy suggestions based on the industrial robot. The method establishes a device tree model and a framework with subdivision granularity, and can infinitely refine and systemize the subdivision dimension of the production line device, for example, the method can realize: production bases, workshops, production lines, process sections, stations, complete equipment, devices, components, assembly kits, assemblies, and the like; establishing a scientific and finely-divided expert experience library; according to the equipment tree model and subdivision granularity of the production line, data association and attribution are carried out, standardized mechanism models, application scenes, environmental parameters, large data discrete model analysis of special data and alarm data are achieved according to types, and a system expert database is associated, so that automatic generation and pushing of predictive diagnosis, risk suggestion and maintenance plan are achieved, scientific, reasonable, subdivision granularity and multidimensional tree-shaped report systems of the predictive maintenance suggestion are ensured, and intelligent decision of equipment predictive maintenance management is achieved.
Additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the embodiments of the present application or the description of the prior art will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a production line equipment maintenance prediction method based on an industrial robot according to an embodiment of the present application;
fig. 2 is a schematic block diagram of a production line equipment maintenance prediction device based on an industrial robot according to another embodiment of the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
As shown in fig. 1, the method for predicting the maintenance of production line equipment based on an industrial robot according to the embodiment of the application includes the following steps:
110. receiving real-time alarm information and equipment special data of the industrial robot;
120. inputting the real-time alarm message and the equipment special data into a trained risk prediction model, and outputting a risk position and a risk level;
130. and combining the risk position and the risk level with an expert database to determine alarm and risk reasons, thereby obtaining maintenance strategy suggestions of production line equipment of the industrial robot.
Based on the above embodiment, further comprising:
acquiring a component composition unit of the production line equipment, wherein the component composition unit comprises a motor, a gear box, a control cabinet and a track, and acquiring all unit components of each component composition unit;
taking the production line equipment as a root node, taking the component forming units as stem nodes, taking all unit components in each component forming unit as leaf nodes, and constructing an equipment tree of the production line equipment;
taking the leaf nodes as objects to obtain original alarm and equipment special data of each leaf node, and taking the stem nodes as objects to obtain the original alarm and equipment special data of each stem node;
dividing the historical alarm and equipment special data information into a training set and a testing set;
creating the risk prediction model based on the equipment tree, dividing the training set into a plurality of subsets, each subset serving as a branch of a current node, and training the risk prediction model;
and pruning the risk prediction model by using the test set.
Based on the above embodiment, further, the combining the risk location and the risk level with an expert database determines an alarm and a risk reason, so as to obtain a maintenance policy suggestion of the production line equipment of the industrial robot, which specifically includes:
acquiring a first device sending out the real-time alarm message, and acquiring a second device where the alarm fault and the risk position are located;
if the first equipment and the second equipment are different, respectively sending a probing instruction to the first equipment and the second equipment;
when the response message sent by the first device is not received within a preset time period, the first device is used as the device to be checked;
when the response message sent by the second device is not received within a preset time period, the second device is used as the device to be checked;
when the response message sent by the first device is not received within a preset time period, and the response message sent by the first device is not received, the first device and the second device are used as devices to be checked;
and sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment.
Based on the above embodiment, further, the sending the real-time alarm and equipment special data, the risk level, the risk location, the alarm reason data and the equipment information to be checked to the expert database, determining an alarm and a risk reason, and obtaining a maintenance policy suggestion of the industrial robot production line equipment specifically includes:
acquiring historical operation data of the equipment to be checked, determining equipment with fault early warning according to the historical operation data, the real-time alarm information, the equipment special data, the risk level, the alarm fault and risk positions and the alarm reason data, and performing maintenance strategy suggestion on the equipment with the fault early warning.
Based on the above embodiment, further, the historical operating data includes: the equipment to be checked is started and stopped times, equipment OEE, special fault rate, equipment special data change trend, application scene, working condition parameters of the equipment to be checked and environment parameters of the equipment to be checked are started.
Based on the above embodiment, further, the inputting the real-time alarm message and the device specific data into the trained risk prediction model, and outputting the risk location and the risk level, the specific process includes:
traversing from the root node of the risk prediction model, traversing the child nodes of the root node of the risk prediction model if the root node of the risk prediction model and the risk level in the real-time alarm information and the equipment special data are the same, entering the child node branches if the alarm equipment of the real-time alarm information and the equipment special data is the same as the child nodes of the root node, and traversing the child node branches to obtain the risk reasons corresponding to the change trend of the real-time alarm information and the special data.
As shown in fig. 2, the present application provides an industrial robot-based production line equipment maintenance prediction apparatus, the apparatus comprising:
the first processing module is used for receiving real-time alarm information and equipment special data of the industrial robot;
the second processing module is used for inputting the real-time alarm message and the equipment special data into a trained risk prediction model and outputting a risk position and a risk level;
and the third processing module is used for combining the risk position and the risk level with an expert database to determine alarm and risk reasons so as to obtain maintenance strategy suggestions of production line equipment of the industrial robot.
Based on the above embodiment, further comprising a fourth processing module, configured to obtain a component assembly unit of the production line device, where the component assembly unit includes a motor, a gearbox, a control cabinet, and a rail, and obtain all unit components of each component assembly unit;
taking the production line equipment as a root node, taking the component forming units as stem nodes, taking all unit components in each component forming unit as leaf nodes, and constructing an equipment tree of the production line equipment;
taking the leaf nodes as objects to obtain original alarm and equipment special data of each leaf node, and taking the stem nodes as objects to obtain the original alarm and equipment special data of each stem node;
dividing the historical alarm and equipment special data information into a training set and a testing set;
creating the risk prediction model based on the equipment tree, dividing the training set into a plurality of subsets, each subset serving as a branch of a current node, and training the risk prediction model;
and pruning the risk prediction model by using the test set.
Based on the above embodiment, further, the third processing module is specifically configured to obtain a first device that sends the real-time alarm message, and obtain a second device where the alarm fault and the risk location are located;
if the first equipment and the second equipment are different, respectively sending a probing instruction to the first equipment and the second equipment;
when the response message sent by the first device is not received within a preset time period, the first device is used as the device to be checked;
when the response message sent by the second device is not received within a preset time period, the second device is used as the device to be checked;
when the response message sent by the first device is not received within a preset time period, and the response message sent by the first device is not received, the first device and the second device are used as devices to be checked;
and sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment.
The industrial robot-based production line equipment maintenance prediction method provided by the embodiment comprises the steps of receiving real-time alarm information and special data of an industrial robot; inputting the real-time alarm information and the equipment special data into a risk prediction model, and outputting a risk position and a risk level; and determining alarm and risk reasons based on the risk position and risk level data in combination with an expert database, and automatically generating production line equipment maintenance strategy suggestions based on the industrial robot. The method establishes a device tree model and a framework with subdivision granularity, and can infinitely refine and systemize the subdivision dimension of the production line device, for example, the method can realize: production bases, workshops, production lines, process sections, stations, complete equipment, devices, components, assembly kits, assemblies, and the like; establishing a scientific and finely-divided expert experience library; according to the equipment tree model and subdivision granularity of the production line, data association and attribution are carried out, standardized mechanism models, application scenes, environmental parameters, large data discrete model analysis of special data and alarm data are achieved according to types, and a system expert database is associated, so that automatic generation and pushing of predictive diagnosis, risk suggestion and maintenance plan are achieved, scientific, reasonable, subdivision granularity and multidimensional tree-shaped report systems of the predictive maintenance suggestion are ensured, and intelligent decision of equipment predictive maintenance management is achieved.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium.
Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any equivalent modifications or substitutions will be apparent to those skilled in the art within the scope of the present application, and these modifications or substitutions should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An industrial robot-based production line equipment maintenance prediction method, comprising:
receiving real-time alarm information and equipment special data of the industrial robot;
inputting the real-time alarm message and the equipment special data into a trained risk prediction model, and outputting a risk position and a risk level;
and combining the risk position and the risk level with an expert database to determine alarm and risk reasons, thereby obtaining maintenance strategy suggestions of production line equipment of the industrial robot.
2. The industrial robot-based production line equipment maintenance prediction method of claim 1, further comprising:
acquiring a component composition unit of the production line equipment, wherein the component composition unit comprises a motor, a gear box, a control cabinet and a track, and acquiring all unit components of each component composition unit;
taking the production line equipment as a root node, taking the component forming units as stem nodes, taking all unit components in each component forming unit as leaf nodes, and constructing an equipment tree of the production line equipment;
taking the leaf nodes as objects to obtain original alarm and equipment special data of each leaf node, and taking the stem nodes as objects to obtain the original alarm and equipment special data of each stem node;
dividing the historical alarm and equipment special data information into a training set and a testing set;
creating the risk prediction model based on the equipment tree, dividing the training set into a plurality of subsets, each subset serving as a branch of a current node, and training the risk prediction model;
and pruning the risk prediction model by using the test set.
3. The industrial robot-based production line equipment maintenance prediction method according to claim 2, wherein the step of combining the risk location and the risk level with an expert database to determine an alarm and a risk cause, thereby obtaining maintenance policy advice of the industrial robot-based production line equipment, specifically comprises the steps of:
acquiring a first device sending out the real-time alarm message, and acquiring a second device where the alarm fault and the risk position are located;
if the first equipment and the second equipment are different, respectively sending a probing instruction to the first equipment and the second equipment;
when the response message sent by the first device is not received within a preset time period, the first device is used as the device to be checked;
when the response message sent by the second device is not received within a preset time period, the second device is used as the device to be checked;
when the response message sent by the first device is not received within a preset time period, and the response message sent by the first device is not received, the first device and the second device are used as devices to be checked;
and sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment.
4. The industrial robot-based production line equipment maintenance prediction method according to claim 3, wherein the sending the real-time alarm and equipment specific data, the risk level, the risk location, the alarm reason data and the equipment information to be checked to the expert database determines an alarm and risk reason, and obtains maintenance strategy suggestions of the industrial robot-based production line equipment, specifically including:
acquiring historical operation data of the equipment to be checked, determining equipment with fault early warning according to the historical operation data, the real-time alarm information, the equipment special data, the risk level, the alarm fault and risk positions and the alarm reason data, and performing maintenance strategy suggestion on the equipment with the fault early warning.
5. The industrial robot-based production line equipment maintenance prediction method of claim 4, wherein,
the historical operating data includes: the equipment to be checked is started and stopped times, equipment OEE, special fault rate, equipment special data change trend, application scene, working condition parameters of the equipment to be checked and environment parameters of the equipment to be checked are started.
6. The industrial robot-based production line equipment maintenance prediction method of claim 5, wherein the inputting the real-time alarm message and the equipment specific data into the trained risk prediction model, outputting a risk location and a risk level comprises:
traversing from the root node of the risk prediction model, traversing the child nodes of the root node of the risk prediction model if the root node of the risk prediction model and the risk level in the real-time alarm information and the equipment special data are the same, entering the child node branches if the alarm equipment of the real-time alarm information and the equipment special data is the same as the child nodes of the root node, and traversing the child node branches to obtain the risk reasons corresponding to the change trend of the real-time alarm information and the special data.
7. An industrial robot-based production line equipment maintenance prediction device, the device comprising:
the first processing module is used for receiving real-time alarm information and equipment special data of the industrial robot;
the second processing module is used for inputting the real-time alarm message and the equipment special data into a trained risk prediction model and outputting a risk position and a risk level;
and the third processing module is used for combining the risk position and the risk level with an expert database to determine alarm and risk reasons so as to obtain maintenance strategy suggestions of production line equipment of the industrial robot.
8. The industrial robot-based production line equipment maintenance prediction device of claim 7,
the system further comprises a fourth processing module, a second processing module and a third processing module, wherein the fourth processing module is used for acquiring a component composition unit of the production line equipment, the component composition unit comprises a motor, a gear box, a control cabinet and a track, and all unit components of each component composition unit are acquired;
taking the production line equipment as a root node, taking the component forming units as stem nodes, taking all unit components in each component forming unit as leaf nodes, and constructing an equipment tree of the production line equipment;
taking the leaf nodes as objects to obtain original alarm and equipment special data of each leaf node, and taking the stem nodes as objects to obtain the original alarm and equipment special data of each stem node;
dividing the historical alarm and equipment special data information into a training set and a testing set;
creating the risk prediction model based on the equipment tree, dividing the training set into a plurality of subsets, each subset serving as a branch of a current node, and training the risk prediction model;
and pruning the risk prediction model by using the test set.
9. The industrial robot-based production line equipment maintenance prediction device of claim 8,
the third processing module is specifically configured to obtain a first device that sends the real-time alarm message, and obtain a second device where the alarm fault and the risk location are located;
if the first equipment and the second equipment are different, respectively sending a probing instruction to the first equipment and the second equipment;
when the response message sent by the first device is not received within a preset time period, the first device is used as the device to be checked;
when the response message sent by the second device is not received within a preset time period, the second device is used as the device to be checked;
when the response message sent by the first device is not received within a preset time period, and the response message sent by the first device is not received, the first device and the second device are used as devices to be checked;
and sending the real-time alarm and equipment special data, the risk level, the risk position, the alarm reason data and the equipment information to be checked to the expert database, determining the alarm and risk reasons, and obtaining maintenance strategy suggestions of the industrial robot production line equipment.
10. The industrial robot-based production line equipment maintenance prediction device of claim 9,
the third processing module is specifically configured to obtain historical operation data of the device to be checked, determine a device with a fault early warning according to the historical operation data, a real-time alarm message, device specific data, the risk level, the alarm fault and risk position, and the alarm reason data, and perform a maintenance policy suggestion step on the device with the fault early warning.
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