CN117764563A - Equipment maintenance time prediction method, system, electronic equipment and medium - Google Patents
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Abstract
The invention discloses a device maintenance time prediction method, a system, electronic equipment and a medium, and relates to the field of device maintenance, wherein the method comprises the following steps: constructing an application body facing to a maintenance process; extracting maintenance data based on the application ontology depending on different carriers and channels; based on the maintenance data, a MODAPMS method is adopted to determine human body actions of each stage of maintenance operation, and maintenance operation time is obtained according to the human body actions; constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time; and predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph. The invention can improve the efficiency and accuracy of maintenance time prediction.
Description
Technical Field
The present invention relates to the field of equipment maintenance technologies, and in particular, to a method, a system, an electronic device, and a medium for predicting equipment maintenance time.
Background
With the gradual display of the characteristics of complex structure and compact layout of the existing equipment, the maintenance difficulty is increased, and the attention of designers on maintainability is also increased. Therefore, in the equipment design and verification stage, priority should be given to maintenance quantitative index, i.e., prediction of maintenance time. Maintenance time is a basic quantitative parameter in a serviceability design, defined as the standard working time for a skilled maintenance person to perform maintenance tasks under a prescribed maintenance schedule. The maintenance time prediction method in the time span is mainly divided into two types: theoretical calculation method based on experience data and virtual reality simulation method.
Traditional theoretical calculation methods based on empirical data are mainly focused on the application of mathematical theory in the calculation process, and are suitable for equipment with a large amount of data of the same type. These theoretical calculation methods include probabilistic simulation prediction, systematic simulation, monte carlo simulation, and color Petri nets, some of which have been used in common standards.
In recent years, the contemporary theoretical calculation method based on empirical data is more focused on the splitting process of the fundamental action based on experience and the fundamental action time set based on empirical data. These methods are applicable to devices lacking sufficient empirical time data for the same or similar products. These methods, also known as predetermined exercise time standards (Predetermined Motion Time Standards, PMTS), have been used by many industries as production standards for product maintenance and assembly issues, and have extended the development of more than 50 methods, such as exercise time analysis (Motion Time Analysis, MTA), time measurement methods (Time Measurement Method, MTM), the modular arrangement of the melboude operation sequence technique and predetermined time standards (Modular Arrangement of Predetermined Time Standards, MODAPTS/MOD). In recent years, MOD method, which is a predetermined action time standard method capable of accurately predicting action time of an assembly line, has been accepted by industrial enterprises and enterprise groups in terms of effectiveness and practicality, and has become a popular research. In the application field, the improved time prediction method based on the MOD method in the research is applied to the fields of electronic products, connector disassembly, mechanical equipment, assembly lines and the like. Possible deviations of this method are the degree of refinement of the operation process division and the deviation of the basic action time due to the narrow operating environment in the MOD method.
In combination with the development of virtual reality technology, a maintenance process time prediction method based on virtual simulation is studied to reduce the possible deviation. Since the deviation of the theoretical calculation method is caused by non-refinement of the operation process, the designer chooses to simulate the operation process in a relatively real virtual environment and uses this as a segmentation reference, thereby reducing the operation process segmentation. By setting the time compensation coefficients according to relevant ergonomic criteria and virtual environments, the basic action time bias caused by narrow operating environments is reduced. Many researchers have studied the process of operation division and the deviation compensation method in this method and have proposed various improvements. Therefore, virtual reality technology is introduced on the basis of a theoretical calculation method, so that the prediction accuracy can be improved, and the method can be effectively applied to industry. However, in this approach, there is still a bias due to subjective reasons of the serviceability designer, particularly in the following two aspects:
(1) The accuracy of the predicted time is highly dependent on the accuracy of the simulation process. Such dependencies may lead to time-bias caused by the repair simulation process in the virtual environment. When maintenance personnel have insufficient actual maintenance experience or simulation animation production experience, the fineness of the simulation process they produce in the virtual environment cannot reach the fineness of the actual maintenance process, and simulation animation of intermediate maintenance actions is often lacking, resulting in a reduction in the number of basic maintenance actions obtained by segmentation and a reduction in the total prediction time.
(2) Due to subjective differences in the prediction methods, the prediction time inevitably deviates. Because both the simulated animation and the compensation coefficients are empirically produced by the designer, subjective differences are created. Because of the different understanding of the operation, it is difficult for designers having different experiences in the virtual environment to make the same simulation process for the same operation process and to give the same compensation coefficient according to the qualitative criterion, thus resulting in differences and deviations in the predicted time.
Disclosure of Invention
The invention aims to provide a method, a system, electronic equipment and a medium for predicting equipment maintenance time, which are used for reducing deviation of maintenance time prediction.
in order to achieve the above object, the present invention provides a method for predicting equipment maintenance time, comprising the following steps.
And constructing an application body oriented to the maintenance process.
Extracting maintenance data based on the application ontology depending on different carriers and channels; the maintenance data includes text data and digital data; the text data comprises operation step structure information and action information; the digital data includes position coordinates of a virtual serviceman, position coordinates of an operation object, position coordinates of an operation point or plane, and position coordinates of a maintenance tool.
based on the maintenance data, a MODAPMS method is adopted to determine human body actions of each stage of maintenance operation, and maintenance operation time is obtained according to the human body actions; the maintenance operation includes a preparation phase, an operation phase, and a recovery phase.
And constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time.
and predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph.
in order to achieve the above purpose, the invention also provides a device maintenance time prediction system, which comprises the following modules.
And the application body construction module is used for constructing an application body facing the maintenance process.
The maintenance data extraction module is used for extracting maintenance data based on the application ontology and depending on different carriers and channels; the maintenance data includes text data and digital data; the text data comprises operation step structure information and action information; the digital data includes position coordinates of a virtual serviceman, position coordinates of an operation object, position coordinates of an operation point or plane, and position coordinates of a maintenance tool.
The human body action and maintenance operation time determining module is used for determining human body actions of each maintenance operation stage by adopting a MODAPMS method based on the maintenance data and acquiring maintenance operation time according to the human body actions; the maintenance operation includes a preparation phase, an operation phase, and a recovery phase.
And the maintenance process knowledge graph construction module is used for constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time.
and the maintenance time prediction module is used for predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph.
In order to achieve the above object, the present invention further provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the device maintenance time prediction method.
To achieve the above object, the present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described equipment repair time prediction method.
according to the specific embodiment provided by the invention, the invention discloses the following technical effects: the application ontology and the knowledge graph are two methods in the knowledge reuse technology, can be used for clarifying concepts in related fields and is beneficial to effective management and visualization of knowledge, so that the efficiency and accuracy of maintenance time prediction can be improved by combining the knowledge reuse technology such as the application ontology and the knowledge graph.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, 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 flowchart of an equipment maintenance time prediction method provided by the invention.
Fig. 2 is a schematic diagram of a method for predicting equipment maintenance time according to the present invention.
FIG. 3 is an application ontology diagram of a maintenance process constructed by prot g.
fig. 4 is a schematic diagram of a three-stage maintenance operation in a MODAPMS.
fig. 5 is a schematic view of the spatial position area of the upper limb adjustment motion.
Fig. 6 is a schematic diagram of specific elements involved in the time calculation process.
Fig. 7 is a schematic diagram of a constructed maintenance process knowledge graph.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method, a system, electronic equipment and a medium for predicting equipment maintenance time, which reduce deviation of maintenance time prediction by combining knowledge reuse technologies such as ontology and knowledge graph.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
embodiment one: as shown in fig. 1-2, the method for predicting equipment maintenance time provided by the invention comprises step S1-step S5.
S1: and constructing an application body oriented to the maintenance process.
S2: extracting maintenance data based on the application ontology depending on different carriers and channels; the maintenance data includes text data and digital data; the text data comprises operation step structure information and action information; the digital data includes position coordinates of a virtual serviceman, position coordinates of an operation object, position coordinates of an operation point or plane, and position coordinates of a maintenance tool.
S3: based on the maintenance data, a MODAPMS method is adopted to determine human body actions of each stage of maintenance operation, and maintenance operation time is obtained according to the human body actions; the maintenance operation includes a preparation phase, an operation phase, and a recovery phase.
S4: and constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time.
s5: and predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph.
Further, step S1 specifically includes steps S11 to S12.
Ensuring stable operation of the device or system throughout the life. Establishing an application ontology for a repair process may provide a structured and standardized representation of the repair process, identifying objects, concepts and semantic relationships that exist in the repair domain to determine the type of extracted data. This enables the knowledge of the repair process to be transmitted and shared between the person and the computer. A relatively standardized maintenance procedure application ontology is constructed to address ambiguity in the maintenance procedure in a number of areas to clarify the rationality of the computer build map and forecast times described below.
Step S11: the seven-step ontology development method proposed by Steady is adopted to construct an application ontology.
The seven-step application ontology development method provided by the Steady is combined to establish an application ontology of a maintenance process. The application ontology established by the method comprises classes and subclasses as shown in table 1.
Table 1 class and subclass of application ontology
The established explicit specification of the domain, scope and sign of the attributes of the application ontology-defined class of this repair process is shown in table 2. In addition, the application ontology is defined for slot value types, including but not limited to numbers, enumerations, boolean values, and instances, as also shown in Table 2 below.
TABLE 2 slots for application ontologies and attributes thereof
step S12: application ontology visualization.
step S121: and (5) application ontology visualization construction of the class.
The class and the corresponding subclasses are incorporated into the prot software tool for application ontology development. The determined categories include the repair process and its subordinate categories, including actions, step identifications, tools, objects, personnel, and relationships, respectively. The subclass of step identity includes level 1 step and its subclass step 2 and its subclass step 3. The subclass of objects includes action objects and association objects. Subclasses of relationships include location, goal (clause and objective), parallel, number, and subordinate.
Step S122: and (5) performing visual construction of application ontology of the class relationship.
When prot g is used, the direct relationship between steps can be modeled using a resource description framework. Such relationships include step actions, step nouns, step tools and step personnel, as well as step numbers, step sub-steps, step goal clauses and step goal objectives. Then, the indirect relationship needs to be defined by the above-described direct relationship. This relationship includes a quantitative verb corresponding to the step quantification, and a sub-noun and sub-verb corresponding to the step sub-step.
The application ontology of the above class and its relationships are constructed using prot g e, as shown in figure 3. By applying the ontology construction process, the classes and correspondences discussed above can also be determined as the type of data required for extraction.
Further, step S2 specifically includes steps S21 to S22.
The data related to step S2 belongs to maintenance data of products in the digital design process, and depends on different carriers and channels. Text data is extracted from the service manual through semantic analysis and digital data is extracted from the man-machine virtual environment through a data interface respectively, wherein the man-machine virtual environment refers to digital CAD software adopted by a designer in digital prototype design.
Step S21: text data is extracted from a service manual in the serviceability design.
The extracted text data mainly corresponds to the name slot of the class outlined in the application ontology constructed in step S1, wherein the attributes are represented as character strings. A useful input to extract text data is a service manual in a serviceable design.
For text data, the extraction method is typically semantic analysis. Semantic analysis is a sub-field of natural language processing, with emphasis on enabling machines to understand the meaning of human language. It involves the development of algorithms that can analyze the relationships between words, phrases and sentences to derive their intended meanings. The data extracted by semantic analysis includes operation step structure information and action information from the service manual.
step S211: and extracting structural information of the operation steps.
Because the service manual has a structured format, a list with numbers or bullets and a consistent program layout, operational step structure information, such as the order of the service process and the details of each step, etc., is readily identifiable. The sequence of repair procedures corresponds to the class step identification in the application ontology described above.
In particular, the extracted data includes step identifications of different levels of numbers or letters and descriptions of the corresponding repair operations. The correspondence is shown in table 3.
Table 3 Structure of maintenance steps in maintenance Manual
step S212: the motion information is extracted using a pattern matching method and a dependency matching method.
for the action information, data represented by the main-predicate object can be extracted using a pattern matching method and a dependency matching method in semantic analysis.
the pattern matching method recognizes inherent features of words in sentences, such as general POS tags and shapes, and extracts information based on a preset pattern. These classes for extracting name slots include actions, objects, parallelism, targets, and quantity, while the class for extracting identification slots includes only objects. The preset word patterns of the pattern matching method are shown in table 4.
TABLE 4 preset word patterns for pattern matching methods
The dependency matching method mainly identifies dependency relations among different parts of text, such as subjects and objects of sentences and other preset dependency modes. By means of a tool-dependent matcher in natural language processing, the method may extract data corresponding to class membership and location from membership and location relationships. The preset word patterns of the pattern matching method are shown in table 5.
TABLE 5 preset dependency pattern of dependency matching method
step S22: digital data is extracted from an already designed digital prototype.
Currently, the equipment design phase has been converted from a drawing design mode to a digital model design mode, which uses computer aided design software (CAD) and computer aided manufacturing software (CAM) to create a 3D digital model, forming a digital prototype. Allowing the designer to make quick modifications and analyses so that the designer can build and test prototypes more quickly. Digital prototypes can also help designers coordinate designs better and provide more accurate results.
Digital prototypes are widely used in many industries including industrial design, aerospace industry, medical devices, automotive manufacturing, architectural design, and the like. By using the digital prototype, enterprises can more quickly become realistic with creative and innovation, and the product quality and the production efficiency are improved, so that the competitiveness of the digital prototype in the market is enhanced. Currently, in most industries, digital prototypes have replaced traditional drawings as the essential materials for the design process.
The extraction data of the digital data is derived from digital prototypes widely used in various industries currently, and the object of the maintenance data extraction is derived from the definition of the specification in the application ontology of the maintenance process. The digital data is invoked using a method that invokes an attribute of a homonymous object that is open to the industrial software.
Specifically, the extracted data can be classified into two types according to its interface: data related to people and data related to objects.
the data related to the person corresponds to the coordinate slots and the body data slots in each person. These data include the position (three dimensions) of the virtual serviceman, height, stride, shoulder width, palm size, upper arm size, and lower arm size.
the data related to the object corresponds to the coordinate slots in the object and tool class. The data includes the position of the object of operation (three-dimensional), the position of the operating point or plane (multi-dimensional), and the position of the service tool. The definition and description of the extracted digital data is shown in table 6.
Table 6 definition and description of extracted digital data
further, step S3 specifically includes steps S31 to S33.
The MODAPMS method proposed in step S3 is a modified MOD method, defined as a modular arrangement of predetermined motion criteria, and is a mathematical model capable of performing a temporal prediction method based on the data extracted in step S2. The method defines the basic actions of the MOD method as element vectors, rather than 1MOD. The definition and sign of 21 basic actions in MODAPTS are shown in Table 7. By using these element vectors, the repair operation can be decomposed according to the characteristics of the different repair phases and expressed as a combination of basic MOD method operations.
TABLE 7MODAPMS basic action definition
The resolution of the repair actions should adhere to achieving standardized and standardized descriptions of real world repair activities. Thus, in a standard sense, the MODAPMS method divides maintenance operations into three phases: the human body movement action in the preparation phase, the maintenance operation action in the operation phase and the body recovery action in the recovery phase. A representation of a three-phase maintenance operation in a MODAPMS is shown in fig. 4. The specific steps are as follows.
Step S31: human body motion in the preparation phase.
Human body action at the preparation stage) Defined as the lower limb locomotor action (/ >) required by a virtual serviceman to move towards an object) (including forward, backward, lateral, upward and downward movements) and lower limb adjustment movements to contact maintenance objects) By gesture adjustment, both are based on specific constraints, such as maintenance environment, object location, personal location, etc. From the positions of the serviceman and the object, and walking in the MOD method assist motion (W5), the lower limb movement motion (/ >) can be calculated using the following formula)。
（1）
Wherein P isx、Pyrespectively represent the horizontal plane coordinates, OO of the central position of the foot step of the maintenance personnelx、OOyrepresenting the horizontal plane coordinates of the operation target, lw5 represents the step length of the walking W5 activity of the person in the MOD assist operation.
Lower limb adjusting action) Can be calculated from the heights of the maintenance object and the maintenance person and the auxiliary actions of the MOD method, including bending and lifting (B17), standing and sitting (S30). Such adjustment actions can be further divided into standing, bending, sitting and squatting postures according to the height difference and the definition of MOD method. The corresponding height of each gesture may be calculated using the following formula. /(I)
（2）
Wherein P isz、PLrepresenting the height coordinate of the center position of the foot of the maintenance person and the lower arm size, respectively, and OOz representing the height coordinate of the operation object. Hstand、Hsitrepresenting the height position of the elbow from the foot bottom in standard standing and sitting positions, respectively, the data is obtained by the data of operators, and is known from the human body size of Chinese adults in GB/T10000-1988, HstandThe data corresponding to the 50 percentile of the middle is 1024mm, HsitThe data corresponding to the 50 percentile is 676mm. B17 and S30 are defined in table 7, and represent the bending and lifting, standing and sitting motions, respectively.
thus, human motion in MODAPMS) The specific calculation can be obtained by the following equations (1) and (2) through three basic actions of walking (W5), bending and raising (B17), standing and sitting (S30) in the MOD method as variable maps.
（3）
Step S32: human body motion at the operational stage.
Maintenance operation action at operation stage) There are two categories based on the presence of tools: manual upper limb operation and use of tools for upper limb operation. Both types of maintenance actions include upper limb adjustment actions (/ >) required by a maintenance person to touch an object with the palm) (movements of finger, hand, forearm, whole arm and straightened arm) and upper limb manipulation movements (/ >) (screw, crank, turn, pull and push). The main difference is that for the upper limb operation using the tool, there is also a tool-based adjustment action (/ >) based on the maintenance object as action) And tool-based operation actions (/ >))。
The time premise of calculating the operation action is the MOD method principle, namely that the movement action should be accompanied by the terminal action. Therefore, the upper limb adjusting action) Can be divided into exercise actions (/ >) in upper limb adjustment) And terminal action in upper limb adjustment). Motion action (/ >) in upper limb adjustment actions) Can be measured according to fig. 5, where the plane is perpendicular to the plane of the human body. The data to be received includes human body data, position data, and operation object position data. Defining terminal actions (/ >) in corresponding upper limb adjustment according to MOD method) And selects according to the action type (grabbing or placing), thereby selecting P3 and G5.
therefore, maintenance operation action) It can be calculated in the following formula, or it can be mapped as a variable by an action in the action category (finger movement (M1), …, boom movement (M5)) and termination category (touch (G0), … …, tape component placement (P5)) in the MOD method.
（4）
The maintenance operations in question can be classified according to the type of operation into disassembly, manual operations and visual inspection. Specifically, the disassembly class may be further divided into a manual plug sub-class, a crank sub-class, and a screw sub-class according to objects and tools. The manual insertion sub-category refers to the actions such as insertion switch or screw. The screwdrivers are assembled by a serviceman using a T-shaped maintenance tool such as a screwdriver. On the other hand, the crank subclass refers to the use of L-shaped service tools, such as open-ended wrenches and ratchet wrenches. Verbs associated with disassemblable classes include install, remove, disconnect, disengage, unclamp, etc. The manual operation class can be further divided into subclasses such as rotation, pressing, pulling, and lifting. These subcategories correspond to various actions such as turning a switch, manually pressing a switch, pushing an object with a certain force, and lifting the object up and down. Visual inspection is a type of action in which a person views an object with his eyes, which may depend on tools such as a flashlight. Table 8 illustrates a representation of the repair operation actions mentioned in the MODAPMS.
Table 8 representation of maintenance operation actions in MODAPMS
Step S33: human body motion at the recovery stage.
action of recovering original state in recovery stage) The operation of returning the end state of the operation to the standby state is referred to as a neutral posture of the person. In MODAPMS, the maintenance operation action may be performed by a motion action (/ >) in upper limb adjustment) And lower limb adjustment actions (/ >)) Mapping into variables including finger movement (M1), …, arm movement (M5) and flexion (B17), standing and sitting (S30) in the MOD method, as shown in the following formula.
（5）
Maintenance action) Human motion actions (/ >) that can be performed through the preparatory phase) Maintenance operation action of operation phase (/ >)) And body recovery actions of recovery phase (/ >) Is calculated as shown in the following formula. The specific elements involved in the time calculation process are shown in fig. 6.
（6）/>
the calculation of the time is based on the obtained timethe time corresponding to the standard action 1MOD defined by the MOD method is 0.129, and the unit is seconds(s) as shown in the following formula.
（7）
Further, the step S4 specifically includes: the knowledge graph is constructed by integrating the application ontology constructed in the step S1, the triplet data and the operation action paradigm extracted in the step S2 in the MODAPMS proposed in the step S3, and Neo4j is used as a data storage medium for time prediction. Neo4j is a graph-oriented database dedicated to storing and managing large and complex data sets, particularly when building knowledge graphs. Entities in the graph are stored as nodes, and relationships between entities are represented as edges. By utilizing the Neo4j functionality, complex data can be converted into a visual graph showing the relationship between nodes and edges, thereby facilitating a more comprehensive understanding and analysis of the data. Fig. 7 shows a knowledge graph of a maintenance process, as established by way of example. This example is a step in the 737 starter generator disassembly process, outlined in the service manual as "(a) disconnecting the electrical connector (P5) [2] from the starter generator [1 ].
further, step S5 specifically includes steps S51 to S510.
Based on the above steps S1-S4, each operation procedure is read in turn and its maintenance time is predicted. The prediction process includes ten steps, which are described in detail below.
step S51: and acquiring an operation instruction corresponding to the highest-level maintenance step.
considering that the low-level maintenance steps can be regarded as summary descriptions of the high-level maintenance steps, the maintenance step application ontology and the corresponding attribute descriptions thereof of the highest level are sequentially obtained based on the maintenance process knowledge graph。
Step S52: and acquiring the operation actions and the association relation thereof.
obtaining corresponding actions based on a repair process knowledge graphQuantitative relationship/>Parallel relationshipand constructs the action set as/>。
（8）
Step S53: an operation object and its coordinate position are acquired.
Obtaining corresponding objects based on maintenance process knowledge graphLocation of operation object/>position of operating point or plane/>And constructs the object set as/>。
（9）
Step S54: and acquiring maintenance personnel and corresponding data thereof.
Obtaining a current personnel position based on a repair process knowledge graphAnd body data/>and constructs the dataset as/>。
（10）
step S55: the actions are converted to a representation of MODAPMS.
Based onAnd/>according to the MODAPMS method in the step S3, a three-stage action pattern is obtained from the maintenance process knowledge graph and added to/>。
（11）
step S56: human body movement is predicted in the preparation phase.
Based on、/>、/>According to formula (3) in step S3, the maintenance operation action is predicted and stored as/>。
Step S57: predicting maintenance operation actions in the operation stage.
As in step S56, the operation action is predicted and stored as according to the formula (4) in step S3。
Step S58: the recovery phase body recovery actions are predicted.
As in step S56, the recovery action is predicted and stored as according to equation (5) in step S3。
step S59: predicting time of maintenance step。
（11）
Step S510: updating corresponding data of maintenance personnel.
According to the MODAPMS method in step 3, in combination with step S54, step S55 is performed during the calculation，/>，updating the current location set/>, of the maintenance personnel，/>。
The equipment maintenance time prediction method provided by the invention has the following advantages.
(1) Independent of the high cost simulation process.
the method builds a maintenance process application ontology and a knowledge graph based on a maintenance manual, thereby building a digital information body. This method allows the computer to automatically predict the repair time without creating a simulated animation.
(2) And the expression of the maintenance process is standardized, and the risk of subjective deviation is reduced.
The method comprises a semantic analysis method for directly extracting actions from a service manual and a MODAPMS method for decomposing the actions into basic actions, and can enable a computer to replace human beings in time prediction so as to reduce differences and deviations.
Embodiment two: in order to perform a corresponding method of the above embodiment to achieve the corresponding functions and technical effects, an equipment maintenance time prediction system is provided below.
The system includes the following modules.
And the application body construction module is used for constructing an application body facing the maintenance process.
The maintenance data extraction module is used for extracting maintenance data based on the application ontology and depending on different carriers and channels; the maintenance data includes text data and digital data; the text data comprises operation step structure information and action information; the digital data includes position coordinates of a virtual serviceman, position coordinates of an operation object, position coordinates of an operation point or plane, and position coordinates of a maintenance tool.
The human body action and maintenance operation time determining module is used for determining human body actions of each maintenance operation stage by adopting a MODAPMS method based on the maintenance data and acquiring maintenance operation time according to the human body actions; the maintenance operation includes a preparation phase, an operation phase, and a recovery phase.
And the maintenance process knowledge graph construction module is used for constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time.
and the maintenance time prediction module is used for predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph.
embodiment III: an electronic device according to a third embodiment of the present invention includes a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the device maintenance time prediction method provided in the first embodiment.
In practical applications, the electronic device may be a server.
In practical applications, the electronic device includes: at least one processor (processor), memory (memory), bus, and communication interface (communication interface).
wherein: the processor, communication interface, and memory communicate with each other via a communication bus.
And the communication interface is used for communicating with other devices.
and a processor, configured to execute a program, and specifically may execute the method described in the foregoing embodiment.
in particular, the program may include program code including computer-operating instructions.
The processor may be a central processing unit, CPU, or specific integrated circuit ASIC (ApplicationSpecificIntegratedCircuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the electronic device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
and the memory is used for storing programs. The memory may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory.
Embodiment four: based on the description of the third embodiment, a storage medium is provided in which a computer program is stored, and the computer program is executable by a processor to implement the equipment maintenance time prediction method of the first embodiment.
The equipment maintenance time prediction system provided in the second embodiment of the present invention exists in various forms, including but not limited to: (1) a mobile communication device: such devices are characterized by mobile communication capabilities and are primarily aimed at providing voice, data communications. Such terminals include: smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones, etc. (2) ultra mobile personal computer device: such devices are in the category of personal computers, having computing and processing functions, and generally having mobile internet access capabilities. Such terminals include: PDA, MID, and UMPC devices, etc., such as iPad. (3) portable entertainment device: such devices may display and play multimedia content. The device comprises: audio, video players (e.g., iPod), palm game consoles, electronic books, and smart toys and portable car navigation devices. (4) other electronic devices with data interaction function.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present invention. It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
these computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
in one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flashRAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer readable media, as defined in the present invention, does not include transitory computer readable media (transshipment) such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
the invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer.
Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The invention may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A method for predicting equipment maintenance time, comprising:
Constructing an application body facing to a maintenance process;
Extracting maintenance data based on the application ontology depending on different carriers and channels; the maintenance data includes text data and digital data; the text data comprises operation step structure information and action information; the digital data comprise position coordinates of a virtual maintenance person, position coordinates of an operation object, position coordinates of an operation point or a plane and position coordinates of a maintenance tool;
based on the maintenance data, a MODAPMS method is adopted to determine human body actions of each stage of maintenance operation, and maintenance operation time is obtained according to the human body actions; the maintenance operation comprises a preparation phase, an operation phase and a recovery phase;
constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time;
and predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph.
2. The equipment maintenance time prediction method according to claim 1, wherein the construction of the application ontology for the maintenance process specifically comprises:
and constructing an application body facing to a maintenance process by adopting a seven-step application body development method provided by Stanford, and visualizing the application body.
3. The equipment maintenance time prediction method according to claim 1, wherein the maintenance data is extracted depending on different carriers and channels based on the application ontology, specifically comprising:
based on the application ontology, text data is extracted from the service manual by semantic analysis and digital data is extracted from the human-machine virtual environment by a data interface.
4. The equipment maintenance time prediction method according to claim 1, wherein the human body motion in the preparation stage is defined as a lower limb movement motion required for a virtual serviceman to move toward the operation object and a lower limb adjustment motion contacting the operation object; the human body action at the operation stage is defined as an upper limb adjustment action and an upper limb operation action required by a maintenance person to touch an operation object by using a palm; the human body motion in the recovery stage is defined as an upper limb adjustment motion and a lower limb adjustment motion required for a maintenance person to adjust to a neutral posture.
5. the equipment maintenance time prediction method according to claim 1, wherein the method for determining human body actions at each stage of maintenance operation by using a MODAPMS method, and obtaining maintenance operation time according to the human body actions, specifically comprises:
according to the formuladetermining the human body action/>, of the preparation stageThe method comprises the steps of carrying out a first treatment on the surface of the Wherein/>Representing lower limb movement/motion-Representing lower limb adjustment actions;
according to the formuladetermining human body action/>, of the operational phaseThe method comprises the steps of carrying out a first treatment on the surface of the Wherein/>representing the upper limb adjustment actions,/>Representing the operation actions of the upper limbs,/>Representing tool-based adjustment actions,/>representing tool-based operation actions,/>Represents the movement in the adjustment of the upper limb,Representing the terminal action in upper limb adjustment,/>Representing locomotor action in upper limb manipulation,/>representing terminal actions in upper limb operations,/>Representing the movement in the adjustment of the upper limb when the tool is picked up,/>representing the terminal action in upper limb adjustment when the tool is picked up,/>represents the movement in the upper limb operation when the tool is picked up,Representing a terminal action in an upper limb operation when the tool is picked up;
according to the formulaDetermining the human body action/>, of the recovery phase；
according to the formula，/>Calculate maintenance operation time/>the method comprises the steps of carrying out a first treatment on the surface of the Wherein/>Indicating the action of the maintenance operation.
6. a system for predicting equipment repair time, comprising:
the application body construction module is used for constructing an application body facing the maintenance process;
The maintenance data extraction module is used for extracting maintenance data based on the application ontology and depending on different carriers and channels; the maintenance data includes text data and digital data; the text data comprises operation step structure information and action information; the digital data comprise position coordinates of a virtual maintenance person, position coordinates of an operation object, position coordinates of an operation point or a plane and position coordinates of a maintenance tool;
the human body action and maintenance operation time determining module is used for determining human body actions of each maintenance operation stage by adopting a MODAPMS method based on the maintenance data and acquiring maintenance operation time according to the human body actions; the maintenance operation comprises a preparation phase, an operation phase and a recovery phase;
The maintenance process knowledge graph construction module is used for constructing a maintenance process knowledge graph based on the application body, the maintenance data, the human body actions and the maintenance operation time;
and the maintenance time prediction module is used for predicting the maintenance time of the maintenance process based on the maintenance process knowledge graph.
7. an electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the device repair time prediction method of any one of claims 1-5.
8. a computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the equipment servicing time prediction method according to any of claims 1-5.
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