CN115034409A - Vehicle maintenance scheme determination method, device, equipment and storage medium - Google Patents
Vehicle maintenance scheme determination method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for determining a vehicle maintenance scheme. The method comprises the following steps: the method comprises the steps of obtaining original fault description data of a target vehicle, wherein the original fault description data comprise description data of a vehicle owner and/or description data of maintenance personnel, converting the original fault description data into standard knowledge representation information meeting preset requirements, wherein the preset requirements are associated with a preset maintenance knowledge map to obtain target fault description information, matching the target fault description information with the preset maintenance knowledge map, and outputting at least one recommended maintenance scheme according to a matching result. By adopting the technical scheme, the targeted recommended maintenance scheme can be quickly output by utilizing the fault description data and the knowledge map of the vehicle, so that a vehicle owner or maintenance personnel can be helped to quickly know a solution for solving the current vehicle fault problem, and the vehicle maintenance efficiency is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device, equipment and a storage medium for determining a vehicle maintenance scheme.
Background
With the continuous upgrade of consumption, automobiles gradually move into each household, the automobile output is more and more, the after-sale pressure is also more and more, and with the emergence of the concepts of automobile 'new and quartered' and 'software defined automobile' and the like, the automobile complexity is higher and higher, and the failure rate is higher.
At present, maintenance personnel generally read fault codes through a diagnostic instrument to perform fault diagnosis, the fault diagnosis based on the fault codes is a method based on knowledge reasoning, and effective diagnosis cannot be provided for vehicle components and systems without the fault codes. In addition, the existing fault diagnosis schemes only generally give fault types, detailed maintenance schemes cannot be provided, and as the complexity of automobiles is higher and higher, the maintenance schemes are also generally complex, maintenance personnel still need to further determine specific maintenance schemes by combining self experience, and the maintenance efficiency is low.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for determining a vehicle maintenance scheme, which can automatically determine the vehicle maintenance scheme and improve the vehicle maintenance efficiency.
According to an aspect of the present invention, there is provided a vehicle repair scenario determination method, including:
acquiring original fault description data of a target vehicle, wherein the original fault description data comprises description data of a vehicle owner and/or description data of maintenance personnel;
converting the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information, wherein the preset requirements are associated with a preset maintenance knowledge map;
and matching the target fault description information with the preset maintenance knowledge graph, and outputting at least one recommended maintenance scheme according to a matching result.
According to another aspect of the present invention, there is provided a vehicle repair scenario determination apparatus, characterized by comprising:
the system comprises a description data acquisition module, a fault analysis module and a fault analysis module, wherein the description data acquisition module is used for acquiring original fault description data of a target vehicle, and the original fault description data comprises description data of a vehicle owner and/or description data of maintenance personnel;
the fault description information conversion module is used for converting the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information, wherein the preset requirements are associated with a preset maintenance knowledge map;
and the maintenance scheme recommendation module is used for matching the target fault description information with the preset maintenance knowledge graph and outputting at least one recommended maintenance scheme according to a matching result.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle service plan determination method of any embodiment of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the vehicle service plan determination method according to any one of the embodiments of the present invention when executed.
According to the technical scheme of the embodiment of the invention, original fault description data of a target vehicle are obtained, the original fault description data are converted into standard knowledge representation information meeting preset requirements, target fault description information is obtained, the target fault description information is matched with a preset maintenance knowledge map, and at least one recommended maintenance scheme is output according to a matching result. By adopting the technical scheme, the targeted recommended maintenance scheme can be quickly output by utilizing the fault description data and the knowledge graph of the vehicle, so that a vehicle owner or maintenance personnel can be helped to quickly know the solution for solving the current vehicle fault problem, and the vehicle maintenance efficiency is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a vehicle service plan determination method provided in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a preset repair knowledge graph according to an embodiment of the present invention;
FIG. 3 is a flow chart of another vehicle service routine determination method provided in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of a vehicle repair scenario determination method provided in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a vehicle repair scenario determination apparatus provided in accordance with an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device implementing the vehicle repair scenario determination method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of a vehicle maintenance scheme determination method according to an embodiment of the present invention, where the embodiment is applicable to a case where a vehicle maintenance scheme is automatically recommended by an electronic device, and the method may be executed by a vehicle maintenance scheme determination device, which may be implemented in a form of hardware and/or software, and the vehicle maintenance scheme determination device may be configured in an electronic device such as a mobile phone, a laptop computer, a personal computer, or a server. As shown in fig. 1, the method includes:
By way of example, a target vehicle may be understood as a vehicle that is currently malfunctioning and needs to be serviced. The original fault description data may be obtained through a network, or may be actively input by a user (including a vehicle owner or/and a maintenance person), without limitation. The owner of the vehicle is typically the owner of the target vehicle. The description data of the vehicle owner may include data related to the current problem of the target vehicle described by the vehicle owner in a natural language, such as "unable to normally drive". The description data of the maintenance personnel can comprise related data of the current problem of the target vehicle described by the maintenance personnel in a natural language mode, such as 'white smoke at a relay' and the like. The original fault description data may include description data of a vehicle owner, description data of a maintenance person, or both description data of the vehicle owner and description data of the maintenance person.
And 102, converting the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information, wherein the preset requirements are associated with a preset maintenance knowledge graph.
In the embodiment of the invention, the maintenance scheme corresponding to the original fault description data is recommended by utilizing the preset maintenance knowledge map. A knowledge graph may also be understood as a knowledge base of a semantic network having a directed graph structure, which may include entities, relationships, and attributes. The preset maintenance knowledge graph can be understood as a knowledge graph containing the relation between the fault information and the maintenance scheme, and can be established in advance by using maintenance big data and other related data. In the step, original fault description data is converted into standard knowledge identification information which can be identified by a preset maintenance knowledge map, and the converted information is called target fault description information. The specific content and the conversion mode can be determined according to the construction mode of the preset maintenance knowledge map.
And 103, matching the target fault description information with the preset maintenance knowledge graph, and outputting at least one recommended maintenance scheme according to a matching result.
For example, the target fault description information may be matched with fault information in a preset maintenance knowledge graph, at least one piece of fault information successfully matched is found, and a maintenance scheme corresponding to the successfully matched fault information is output as a recommended maintenance scheme. Optionally, if a plurality of pieces of failure information successfully matched are found, or one piece of failure information successfully matched is found, and the failure information corresponds to a plurality of maintenance schemes, a plurality of candidate recommended maintenance schemes may be obtained, and one or more than one candidate recommended maintenance schemes are selected from the plurality of candidate recommended maintenance schemes to be output as the recommended maintenance scheme. The output mode is not limited, for example, the recommended maintenance scheme can be converted into a natural language text, and the natural language text is output in a voice or text display mode, so that a vehicle owner and/or a maintenance worker can know the specific content of the recommended maintenance scheme conveniently and can take corresponding operations, and the problem of the fault of the target vehicle can be solved.
According to the vehicle maintenance scheme determining method, original fault description data of a target vehicle are obtained, the original fault description data are converted into standard knowledge representation information meeting preset requirements, target fault description information is obtained, the target fault description information is matched with a preset maintenance knowledge map, at least one recommended maintenance scheme is output according to a matching result, the vehicle fault description data and the knowledge map can be used for rapidly outputting the targeted recommended maintenance scheme, a vehicle owner or maintenance personnel can be helped to rapidly know a solution for solving the current vehicle fault problem, and vehicle maintenance efficiency is improved.
In some embodiments, the preset repair knowledge map is obtained by: acquiring sample data, wherein unstructured historical fault maintenance case data in the sample data comprise sample fault description data and maintenance scheme data corresponding to the sample fault description data; and constructing the preset maintenance knowledge graph based on the sample data. The method has the advantages that the preset maintenance knowledge map can be constructed by using historical fault maintenance case data, and the accurate preset maintenance knowledge map can be obtained by fully using maintenance experience data.
Optionally, the sample fault description data may include description data of an owner of the vehicle and/or description data of a service person corresponding to the sample vehicle. The maintenance scheme data may include data related to a maintenance scheme adopted by a maintenance worker when the maintenance worker successfully solves the problem of the failure of the sample vehicle during the maintenance process of the sample vehicle, and may be described in a natural language manner, such as replacing a relay or upgrading a vehicle-mounted application program. Historical fault maintenance case data can be collected by the cloud, a database for maintaining the big data is constructed, and when a preset maintenance knowledge map needs to be constructed, required historical fault maintenance case data are obtained from the database.
In some embodiments, structured vehicle data is also included in the sample data. The vehicle data includes attribute data of the vehicle and attribute data of parts in the vehicle. The advantage of setting up like this lies in, can construct more accurate preset maintenance knowledge map. For example, the vehicle data may be derived from after-market data, including but not limited to vehicle model, date of manufacture, and date of sale, including but not limited to brand, type, and model of parts such as engines, transmissions, and controllers.
In some embodiments, said building said preset maintenance knowledge map based on said sample data comprises: determining first initial knowledge representation data from the vehicle data; performing knowledge extraction on the historical fault maintenance case data to obtain second initial knowledge representation data, wherein the knowledge extraction comprises entity extraction, attribute extraction, event extraction and relationship extraction, the entity extraction comprises detection of entities and classification of the entities, the entities comprise parts of vehicles, the categories of the entities are determined based on vehicle assemblies and vehicle systems, the event extraction comprises fault event extraction and maintenance event extraction, the relationship extraction comprises determination of the relationship between a fault event and a maintenance scheme, and the maintenance scheme comprises at least one maintenance event; and performing knowledge fusion on the first initial knowledge representation data and the second initial knowledge representation data to generate standard knowledge representation information meeting the preset requirement, and constructing the preset maintenance knowledge map according to a knowledge fusion result. The method has the advantages that structured vehicle data and unstructured historical fault maintenance case data (hereinafter referred to as historical cases) are fused, and a reasonable and accurate preset maintenance knowledge map is constructed for vehicle maintenance.
For example, determining the first initial knowledge representation data from the vehicle data may comprise: and integrating the vehicle data to obtain knowledge unit data, and taking the knowledge unit data as first initial knowledge representation data. The knowledge unit can comprise entities, attributes and relations, a series of high-quality fact expressions can be formed on the basis of knowledge unit data, and a foundation is laid for the construction of an upper-layer preset maintenance knowledge map.
The main knowledge extraction content can include word segmentation, part-of-speech recognition, synonym recognition, keyword extraction, named entity recognition, entity relationship recognition, semantic understanding, semantic similarity calculation and the like of historical fault maintenance case data. The final purpose of knowledge extraction is to implement entity extraction, relationship extraction, attribute extraction and event extraction. The main methods may include, but are not limited to, completing the segmentation of the corpus by using a Natural Language Processing (NLP) segmentation algorithm based on Hidden Markov (HMM) models, Directed Acyclic Graph (DAG) prefix dictionary trees, maximum forward matching, and Bi-directional long-short Term memory network (Bi-LSTM) deep learning segmentation, and extracting key information based on Term Frequency-Inverse Document Frequency (Term-Inverse Document Frequency, TF-IDF), text ranking (TextRank) keyword extraction algorithm, and the like. And after knowledge extraction is carried out on the historical fault maintenance case data, the obtained data is recorded as second initial knowledge representation data.
For example, the purpose of entity extraction is to identify named entities (entities for short) in historical cases, where the named entities mainly include parts of the vehicle, and the parts may include various types of components in the vehicle, including hardware and/or software, including relays, controllers, fuel tanks, and the like, and may also include applications such as vehicle navigation. Entities are identified by naming the entities and classifying the entities onto corresponding entity types. The specific classification manner may be determined based on a vehicle assembly and a vehicle system, the vehicle assembly may include, for example, an engine assembly, a steering gear assembly, a transmission assembly, a front axle, a rear axle, and a frame, the vehicle system may include, for example, a power system, a transmission system, a driving system, a braking system, a steering system, a lighting system, an electrical system, and a fuel supply system, and the classification manner may be subdivided or combined based on this, which is not limited specifically.
Illustratively, the task of attribute extraction is to construct an attribute list for each ontology semantic class, while attribute value extraction is to add attribute values to the entities of one semantic class, and the extraction of attributes and attribute values can form a knowledge graph dimension of the complete entity concept. Taking an engine as an example, the attribute values may include, for example, manufacturer, type, model, and the like.
Illustratively, relational extraction is generally triple extraction, with one predicate having two arguments. The relationship between the entities can be obtained through the relationship extraction, and in the embodiment of the present invention, the relationship is specifically used for obtaining the relationship between the fault event and the maintenance scheme.
For example, the event extraction may be understood as extraction of a multivariate relationship, a trigger word and a representative event type and a representative event subtype are identified according to a context, and the fault diagnosis needs to define event categories, wherein each event category and/or the representative event subtype corresponds to a unique event template. The unstructured text of the event information is presented in a structured form, and the method is mainly used for identifying and classifying the historical cases serving as events to corresponding types for subsequent construction of a diagnosis knowledge graph.
Illustratively, knowledge fusion is to fuse the structured vehicle data and the unstructured historical case data, and the main contents include but are not limited to ontology reasoning, rule reasoning, path computation, similarity computation, inconsistency detection, ontology construction and management, data mapping, entity matching, ontology fusion and the like. The core of knowledge fusion is to realize the construction of a fault knowledge graph by carrying out entity alignment, ontology construction and quality evaluation. Finally, a knowledge graph of the fault symptoms and the solutions is constructed through Word2Vec by text vectorization, Apriori relation exploration and text classification convolutional neural network (TextCNN).
Illustratively, entity alignment primary content is entity disambiguation and co-reference disambiguation. The main purpose of entity alignment is to determine whether two or more entities from different information sources are pointing to the same object in the real world. In addition, if multiple entities are found to represent the same object, an alignment relationship needs to be constructed between the entities, and information contained in the entities is fused and aggregated at the same time. The purpose of ontology building is to share common understanding of information structure among people or software, enable reuse of domain knowledge, make domain assumptions more definite, separate domain knowledge from operational knowledge, and analyze domain knowledge. The quality evaluation is mainly verification and comprises completion, error correction, external chaining and updating of all parts, and the data quality is evaluated according to the missing rate and the accuracy rate to ensure the consistency and the accuracy of the knowledge graph.
Optionally, the construction process of the preset maintenance knowledge graph can be completed by the electronic device in the embodiment of the present invention, and the constructed preset maintenance knowledge graph can be stored locally in the electronic device; the preset maintenance knowledge graph can be constructed by the server, and the constructed preset maintenance knowledge graph can be stored in a database corresponding to the server, can be issued to the electronic equipment, and can also be accessed by the electronic equipment through the server.
In some embodiments, the failure events include failure symptom events, failed part events, and part failure alarm events, the preset maintenance knowledge map includes association relationships among various failure events, part categories, and maintenance solutions, and the target failure description information includes at least one of the failure symptom events, the failed part events, and the part failure alarm events. The advantage of setting up like this lies in, can more rationally set for predetermine the data structure in the maintenance knowledge map, convenient quick location recommends the maintenance scheme.
Fig. 2 is a schematic structural diagram of a preset maintenance knowledge graph according to an embodiment of the present invention, as shown in fig. 2, the preset maintenance knowledge graph includes an association relationship between a fault symptom (corresponding to a fault symptom event), a failed part (corresponding to a failed part event), a fault alarm (corresponding to a part fault alarm event), a system classification (corresponding to a part category), and a maintenance plan.
For example, each fault symptom may correspond to one or more failed parts, each fault symptom may correspond to one or more fault alarms, one failed part may correspond to one or more different system classifications, and each fault symptom, failed part, system classification, and fault alarm may have a corresponding fault diagnosis scheme (i.e., maintenance scheme). The target fault description information comprises at least one item of fault symptom event, failure part event and part fault alarm event, and the corresponding maintenance scheme can be found out through the event contained in the target fault description information in a preset maintenance knowledge map.
In some embodiments, the outputting at least one recommended repair scenario according to the matching result includes: and outputting a plurality of recommended maintenance schemes according to the matching result, wherein each recommended maintenance scheme corresponds to one recommended probability value. The advantage of this arrangement is that the user is provided with a plurality of selectable maintenance schemes, and the recommended priority is identified according to the probability value, so that the user is helped to quickly select the appropriate maintenance scheme. For example, a diagnosis of a fault may correspond to a plurality of recommendations, such as fault 1-scenario 1: 90%, fault 1-scenario 2: 80%, fault 1-scenario 3: 70%, etc.
In some embodiments, after the outputting at least one recommended maintenance plan according to the matching result, the method further includes: and receiving implementation result information input by a maintenance worker aiming at a target recommended maintenance scheme, and adjusting a recommended probability value corresponding to the target recommended maintenance scheme in the preset maintenance knowledge map according to the implementation result information. The advantage of setting up like this is, can predetermine the practical application of maintenance knowledge map in-process, constantly perfect and optimize it.
Illustratively, the adjustment may include an up and/or down adjustment. For example, after the maintenance personnel adopt the target recommended maintenance scheme to maintain the target vehicle, the fault is eliminated, the target recommended maintenance scheme can be considered to be more accurate, and the preset maintenance knowledge map can be updated, so that the recommended probability value corresponding to the target recommended maintenance scheme is increased; for another example, after the maintenance personnel adopt the target recommended maintenance scheme to maintain the target vehicle, the fault is not eliminated, the target recommended maintenance scheme can be considered as incapable of solving the problem, and the preset maintenance knowledge map can be updated, so that the recommendation probability value corresponding to the target recommended maintenance scheme is reduced by a first value; for another example, after the maintenance personnel perform maintenance processing on the target vehicle by using the target recommended maintenance scheme, the fault is not eliminated, and a new fault is introduced, the target recommended maintenance scheme may be considered to be not advisable, and the preset maintenance knowledge graph may be updated, so that the recommended probability value corresponding to the target recommended maintenance scheme is reduced by a second value, where the second value is greater than the first value. The implementation result information may include relevant information such as whether to resolve the fault and whether to introduce a new fault.
In some embodiments, after the outputting at least one recommended maintenance plan according to the matching result, the method further includes: receiving newly-added maintenance scheme data input by maintenance personnel aiming at the original fault description data, and converting the newly-added maintenance scheme data into standard knowledge representation information meeting preset requirements to obtain a newly-added maintenance scheme; and updating the preset maintenance knowledge graph according to the newly added maintenance scheme. The method has the advantages that after the maintenance personnel maintain the vehicle by adopting all recommended maintenance schemes, the faults are not eliminated, the recommended schemes are not appropriate, the maintenance personnel can grope out a new maintenance scheme capable of solving the faults according to own experience and input relevant information of the new maintenance scheme into the electronic equipment, the electronic equipment determines newly added maintenance scheme data according to the input information of the maintenance personnel, and the newly added maintenance scheme data are added into the preset maintenance knowledge graph after being converted into standard knowledge representation information meeting preset requirements so as to update the preset maintenance knowledge graph.
Fig. 3 is a flowchart of another vehicle repair scenario determination method provided according to an embodiment of the present invention, which is optimized based on the above-mentioned alternative embodiments, and fig. 4 is a schematic diagram of the vehicle repair scenario determination method provided according to an embodiment of the present invention, which can be understood by referring to fig. 3 and 4. As shown in fig. 3, the method includes:
Specifically, the vehicle data includes attribute data of the vehicle and attribute data of parts in the vehicle, and the historical fault maintenance case data includes sample fault description data and maintenance scheme data corresponding to the sample fault description data.
Illustratively, steps 301 to 304 correspond to a building process of the preset maintenance knowledge graph, that is, a development process, and steps 305 to 310 correspond to an application process of the preset maintenance knowledge graph. As shown in fig. 4, the whole process includes: s0 original data (namely sample data) preparation, S1 data integration (namely obtaining first initial knowledge representation data), S2 knowledge extraction (converting historical fault maintenance cases into second initial knowledge representation data), S3 knowledge fusion, S4 maintenance knowledge atlas database construction, S5 data preprocessing (namely processing original fault description data into initial knowledge representation data, wherein the process can be realized by calling a related algorithm corresponding to S2), S6 maintenance scheme recommendation, S7 weight adjustment (namely adjusting recommendation probability value), and S8 expert diagnosis. The construction process of the preset maintenance knowledge graph can comprise S0-S1-S2-S3-S4, and the application process of the preset maintenance knowledge graph can comprise S5-S2-S3-S4-S6-S7/S8.
And 303, extracting knowledge from the historical fault maintenance case data to obtain second initial knowledge representation data.
Wherein the knowledge extraction includes an entity extraction, an attribute extraction, an event extraction, and a relationship extraction, the entity extraction includes detection of entities and classification of entities, the entities include parts of vehicles, the classification of entities is determined based on vehicle assemblies and vehicle systems, the event extraction includes a failure event extraction and a maintenance event extraction, the relationship extraction includes determining a relationship of a failure event and a maintenance plan, the maintenance plan includes at least one maintenance event.
And 304, performing knowledge fusion on the first initial knowledge representation data and the second initial knowledge representation data to generate standard knowledge representation information meeting preset requirements, and constructing a preset maintenance knowledge graph according to knowledge fusion results.
Illustratively, the failure events include failure symptom events, failed part events, and part failure alarm events, the preset maintenance knowledge map includes association relationships among various failure events, part categories, and maintenance schemes, and the target failure description information includes at least one of the failure symptom events, the failed part events, and the part failure alarm events.
And 305, acquiring original fault description data of the target vehicle, wherein the original fault description data comprises description data of a vehicle owner and description data of a maintenance person.
Optionally, target vehicle data of the target vehicle may also be obtained, including attribute data of the target vehicle and attribute data of parts in the target vehicle.
And step 306, converting the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information.
For example, the original fault description data may be converted into standard knowledge representation information meeting preset requirements with reference to processing of sample data. For example, entity extraction, attribute extraction, and event extraction may be included, and since the original fault description data does not include a repair solution, the relationship extraction may not be performed. And then, carrying out knowledge fusion to obtain standard knowledge identification information.
Optionally, if the target vehicle data is further acquired, the first initial knowledge representation information may also be determined according to the target vehicle data, information obtained by extracting knowledge from the original fault description data is recorded as the second initial knowledge representation information, and the first initial knowledge representation information and the second initial knowledge representation information are fused to obtain the target fault description information.
And 307, matching the target fault description information with a preset maintenance knowledge graph, and outputting a plurality of recommended maintenance schemes according to matching results, wherein each recommended maintenance scheme corresponds to a recommended probability value.
Illustratively, this may be determined from input from maintenance personnel.
And 309, promoting a recommended probability value corresponding to the target recommended maintenance scheme in the preset maintenance knowledge map according to implementation result information input by the maintenance personnel aiming at the target recommended maintenance scheme.
And 310, converting the newly added maintenance scheme data input by the maintenance personnel into standard knowledge representation information meeting preset requirements to obtain a newly added maintenance scheme, and adding the newly added maintenance scheme into the preset maintenance knowledge map.
The vehicle maintenance scheme determining method provided by the embodiment of the invention can obtain unstructured experience data for constructing the fault knowledge map through data cleaning based on historical fault maintenance cases for maintaining big data, perform entity extraction, attribute extraction, event extraction and relation extraction through knowledge extraction, infer knowledge based on experience knowledge to realize entity alignment, body construction and fault knowledge map construction, store the fault knowledge map in a database, automatically provide fault diagnosis and maintenance scheme recommendation according to fault description data when a new fault case occurs, verify the recommendation result, perform weight adjustment on the case with the correct recommendation result if the recommendation result is correct, ensure the system to be more certain, diagnose through a fault diagnosis expert (namely, maintenance personnel) if the recommendation result is not correct, update the diagnosis result as a new case into the empirical fault diagnosis system, the method has the advantages that the maintenance big data can be continuously increased through learning, the knowledge map is continuously optimized, and compared with a fault code diagnosis and car networking method based on priori knowledge, the method is wider in application range coverage.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a vehicle repair scenario determination apparatus according to an embodiment of the present invention. As shown in fig. 5, the apparatus includes:
a description data obtaining module 501, configured to obtain original fault description data of a target vehicle, where the original fault description data includes description data of a vehicle owner and/or description data of a maintenance worker;
a fault description information conversion module 502, configured to convert the original fault description data into standard knowledge representation information meeting preset requirements, so as to obtain target fault description information, where the preset requirements are associated with a preset maintenance knowledge graph;
and a maintenance scheme recommending module 503, configured to match the target fault description information with the preset maintenance knowledge graph, and output at least one recommended maintenance scheme according to a matching result.
The vehicle maintenance scheme determining device provided by the embodiment of the invention obtains original fault description data of a target vehicle, converts the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information, matches the target fault description information with a preset maintenance knowledge map, outputs at least one recommended maintenance scheme according to a matching result, and can quickly output a targeted recommended maintenance scheme by using the fault description data and the knowledge map of the vehicle, help a vehicle owner or maintenance personnel to quickly know a solution for solving the current vehicle fault problem, and improve the vehicle maintenance efficiency.
Optionally, the preset maintenance knowledge graph is obtained by the following method:
acquiring sample data, wherein the sample data comprises structured vehicle data and unstructured historical fault maintenance case data, the vehicle data comprises attribute data of a vehicle and attribute data of parts in the vehicle, and the historical fault maintenance case data comprises sample fault description data and maintenance scheme data corresponding to the sample fault description data;
and constructing the preset maintenance knowledge graph based on the sample data.
Optionally, the constructing the preset maintenance knowledge graph based on the sample data includes:
determining first initial knowledge representation data from the vehicle data;
performing knowledge extraction on the historical fault maintenance case data to obtain second initial knowledge representation data, wherein the knowledge extraction comprises entity extraction, attribute extraction, event extraction and relationship extraction, the entity extraction comprises detection of entities and classification of the entities, the entities comprise parts of vehicles, the categories of the entities are determined based on vehicle assemblies and vehicle systems, the event extraction comprises fault event extraction and maintenance event extraction, the relationship extraction comprises determination of the relationship between a fault event and a maintenance scheme, and the maintenance scheme comprises at least one maintenance event;
and performing knowledge fusion on the first initial knowledge representation data and the second initial knowledge representation data to generate standard knowledge representation information meeting the preset requirement, and constructing the preset maintenance knowledge map according to a knowledge fusion result.
Optionally, the fault event includes a fault symptom event, a failure component event, and a component fault alarm event, the preset maintenance knowledge map includes an association relationship among various fault events, component categories, and maintenance schemes, and the target fault description information includes at least one of the fault symptom event, the failure component event, and the component fault alarm event.
Optionally, the outputting at least one recommended maintenance scheme according to the matching result includes:
and outputting a plurality of recommended maintenance schemes according to the matching result, wherein each recommended maintenance scheme corresponds to one recommended probability value.
Optionally, the apparatus further comprises:
and the adjusting module is used for receiving implementation result information input by a maintenance worker aiming at a target recommended maintenance scheme after at least one recommended maintenance scheme is output according to the matching result, and adjusting the recommended probability value corresponding to the target recommended maintenance scheme in the preset maintenance knowledge graph according to the implementation result information.
Optionally, the apparatus further comprises:
a newly-added maintenance scheme obtaining module, configured to receive newly-added maintenance scheme data input by a maintenance worker for the original fault description data after outputting at least one recommended maintenance scheme according to the matching result, and convert the newly-added maintenance scheme data into standard knowledge representation information meeting a preset requirement to obtain a newly-added maintenance scheme;
and the scheme adding module is used for adding the newly added maintenance scheme into the preset maintenance knowledge graph.
The vehicle maintenance scheme determining device provided by the embodiment of the invention can execute the vehicle maintenance scheme determining method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method.
Fig. 6 is a schematic structural diagram of the electronic device 10 that implements the vehicle repair scenario determination method according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the vehicle service plan determination method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle service plan determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the vehicle service plan determination method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A vehicle repair scenario determination method, comprising:
acquiring original fault description data of a target vehicle, wherein the original fault description data comprises description data of a vehicle owner and/or description data of maintenance personnel;
converting the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information, wherein the preset requirements are associated with a preset maintenance knowledge map;
and matching the target fault description information with the preset maintenance knowledge graph, and outputting at least one recommended maintenance scheme according to a matching result.
2. The method of claim 1, wherein the predetermined repair knowledge-graph is derived by:
acquiring sample data, wherein the sample data comprises structured vehicle data and unstructured historical fault maintenance case data, the vehicle data comprises attribute data of a vehicle and attribute data of parts in the vehicle, and the historical fault maintenance case data comprises sample fault description data and maintenance scheme data corresponding to the sample fault description data;
and constructing the preset maintenance knowledge graph based on the sample data.
3. The method of claim 2, wherein said building said preset maintenance knowledge graph based on said sample data comprises:
determining first initial knowledge representation data from the vehicle data;
performing knowledge extraction on the historical fault maintenance case data to obtain second initial knowledge representation data, wherein the knowledge extraction comprises entity extraction, attribute extraction, event extraction and relationship extraction, the entity extraction comprises detection of entities and classification of the entities, the entities comprise parts of vehicles, the categories of the entities are determined based on vehicle assemblies and vehicle systems, the event extraction comprises fault event extraction and maintenance event extraction, the relationship extraction comprises determination of the relationship between a fault event and a maintenance scheme, and the maintenance scheme comprises at least one maintenance event;
and performing knowledge fusion on the first initial knowledge representation data and the second initial knowledge representation data to generate standard knowledge representation information meeting the preset requirement, and constructing the preset maintenance knowledge map according to a knowledge fusion result.
4. The method of claim 3, wherein the failure events include failure symptom events, failed part events, and part failure alarm events, the preset maintenance knowledge map includes associations between various failure events, part categories, and maintenance recipes, and the target failure description information includes at least one of the failure symptom events, the failed part events, and the part failure alarm events.
5. The method of claim 1, wherein outputting at least one recommended repair scenario based on the matching results comprises:
and outputting a plurality of recommended maintenance schemes according to the matching result, wherein each recommended maintenance scheme corresponds to one recommended probability value.
6. The method of claim 5, further comprising, after said outputting at least one recommended repair scenario based on the matching result:
and receiving implementation result information input by a maintenance worker aiming at a target recommended maintenance scheme, and adjusting a recommended probability value corresponding to the target recommended maintenance scheme in the preset maintenance knowledge map according to the implementation result information.
7. The method of claim 5, further comprising, after said outputting at least one recommended repair scenario based on the matching result:
receiving newly-added maintenance scheme data input by maintenance personnel aiming at the original fault description data, and converting the newly-added maintenance scheme data into standard knowledge representation information meeting preset requirements to obtain a newly-added maintenance scheme;
and adding the newly added maintenance scheme into the preset maintenance knowledge graph.
8. A vehicle repair scenario determination device, comprising:
the system comprises a description data acquisition module, a fault analysis module and a fault analysis module, wherein the description data acquisition module is used for acquiring original fault description data of a target vehicle, and the original fault description data comprises description data of a vehicle owner and/or description data of maintenance personnel;
the fault description information conversion module is used for converting the original fault description data into standard knowledge representation information meeting preset requirements to obtain target fault description information, wherein the preset requirements are associated with a preset maintenance knowledge map;
and the maintenance scheme recommendation module is used for matching the target fault description information with the preset maintenance knowledge graph and outputting at least one recommended maintenance scheme according to a matching result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle service scenario determination method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the vehicle service plan determination method of any one of claims 1-7 when executed.
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| CN115328689A (en) * | 2022-10-12 | 2022-11-11 | 集度科技有限公司 | Fault diagnosis method, device, equipment and program product |
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| CN116107286A (en) * | 2022-12-07 | 2023-05-12 | 中国第一汽车股份有限公司 | Vehicle fault diagnosis method and device, vehicle and storage medium |
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| CN115639801A (en) * | 2022-10-10 | 2023-01-24 | 合肥合锻智能制造股份有限公司 | Fault diagnosis and analysis decision platform based on multiple intelligent agents |
| CN115328689A (en) * | 2022-10-12 | 2022-11-11 | 集度科技有限公司 | Fault diagnosis method, device, equipment and program product |
| CN115328689B (en) * | 2022-10-12 | 2023-06-27 | 集度科技有限公司 | Fault diagnosis method, device, equipment and program product |
| CN116107286A (en) * | 2022-12-07 | 2023-05-12 | 中国第一汽车股份有限公司 | Vehicle fault diagnosis method and device, vehicle and storage medium |
| CN116129551A (en) * | 2022-12-09 | 2023-05-16 | 浙江凌骁能源科技有限公司 | Automobile fault root cause analysis method, device, computer equipment and storage medium |
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