Maintenance service recommendation method, electronic device therefor, and recording medium

Abstract

The invention discloses a maintenance service recommendation method, an electronic device thereof, and a recording medium. And inputting related data of the damaged machine to the probability graph model through the interactive interface, and obtaining recommendation factors. Searching in the knowledge graph to select a plurality of selected categories related to the recommended factors from the plurality of variable categories; and based on the importance degree of each selected category, establishing a hierarchical architecture graph and presenting the hierarchical architecture graph to an interactive interface.

Description

Technical Field

[0001] The present invention relates to an artificial intelligence (AI) learning mechanism, and more particularly to a maintenance service recommendation method and an electronic device and a recording medium thereof. Background Art

[0002] Generally, after electronic products are sold, if the machine is damaged and needs to be repaired, it can be sent to a repair center. Repair engineers use manual inspection to find the cause of the damage, which takes a lot of time. Therefore, if the possible cause can be estimated through artificial intelligence algorithms, a lot of time can be saved. Summary of the invention

[0003] The present invention provides a maintenance service recommendation method and an electronic device and a recording medium thereof, which can provide reasoning and explainable prediction results.

[0004] The maintenance service recommendation method of the present invention is suitable for being executed by a processor, and the maintenance service recommendation method includes: providing a trained probability graph model and a knowledge graph; inputting relevant data of a damaged machine into the probability graph model, and obtaining a recommendation factor, wherein the knowledge graph is used to represent the correlation between multiple variable categories, each variable category includes multiple factors, and the recommended factor is one of the factors included in a recommended category in the variable category; searching in the knowledge graph to select multiple selected categories related to the recommended factor in the variable category, wherein the selected category includes the recommended category to which the recommended factor belongs and one or more related categories in other variable categories that are directly related to the recommended category; based on the importance of each selected category, establishing a hierarchical structure diagram and presenting it in an interactive interface, wherein the hierarchical structure diagram includes multiple hierarchies corresponding to the selected categories, and each hierarchy has multiple nodes corresponding to the factors included in each selected category.

[0005] In one embodiment of the present invention, the maintenance service recommendation method also includes: performing structural learning on the probability graph model based on the training data set to obtain a knowledge graph; performing parameter learning on the probability graph model based on the training data set and the knowledge graph; and performing cross-validation on the probability graph model based on the test data set.

[0006] In one embodiment of the present invention, the step of establishing a hierarchical structure diagram includes: determining the inheritance relationship of the hierarchy based on the importance of each selected category.

[0007] In one embodiment of the present invention, after establishing a hierarchical structure diagram and presenting it in an interactive interface, it also includes: in response to one of the nodes included in the hierarchy being selected, in the hierarchical structure diagram, a connection relationship between the selected node and its corresponding parent node in the previous hierarchy is visually presented.

[0008] In one embodiment of the present invention, in response to one of the nodes included in the hierarchy being selected, it also includes: expanding the node set corresponding to the selected node included in the next hierarchy of the selected node; and based on the historical statistics of each node in the node set, selecting multiple representative nodes in the node set and presenting them in the hierarchical structure diagram.

[0009] In an embodiment of the present invention, the maintenance service recommendation method further includes: displaying corresponding historical statistics for each node presented in the hierarchical structure diagram.

[0010] In one embodiment of the present invention, after inputting relevant data of a damaged machine into a probability graph model through an interactive interface, it also includes: calculating multiple probability values ​​of multiple factors included in a recommended category through a probability graph model; based on the probability values, extracting multiple representative factors from the factors in a high to low order; and in the interactive interface, sorting and displaying the representative factors, wherein the representative factor with the highest probability value is used as the final recommended factor.

[0011] In one embodiment of the present invention, the interactive interface includes a comparison page, and the maintenance service recommendation method also includes: selecting a first factor and a second factor in a first category in the variable category through the comparison page, and selecting a third factor in the second category in the variable category; inputting the first factor and the third factor into a probability graph model, and obtaining a plurality of first probability values ​​of a plurality of factors included in the third category in the variable category through a first knowledge graph; inputting the second factor and the third factor into a probability graph model, and obtaining a plurality of second probability values ​​of a plurality of factors included in the third category through a second knowledge graph; and displaying the first probability value and the second probability value in the comparison page.

[0012] In one embodiment of the present invention, the knowledge graph is a directed acyclic graph (DAG), and the probability graph model is established using a Bayesian network.

[0013] The electronic device of the present invention comprises: a memory including a trained probability graph model and an interactive interface; and a processor coupled to the memory and configured to execute the maintenance service recommendation method.

[0014] The non-transitory computer-readable recording medium of the present invention is used to store a program code. When the program code is executed by a processor, the processor executes the maintenance service recommendation method.

[0015] Based on the above, the present invention uses the trained probability graph model to estimate a recommendation factor, and finds the selected categories that are strongly related to the recommendation factor based on the knowledge graph, and generates a hierarchical structure diagram. Based on this, it can be used for relevant personnel to explore the cause of machine damage. Based on this, it can not only be used to recommend or give prediction results, but also to perform causal reasoning to further assist technicians in the service and maintenance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a block diagram of an electronic device according to an embodiment of the present invention;

[0017] Figure 2 is a flow chart of a maintenance service recommendation method according to an embodiment of the present invention;

[0018] Figure 3 is a diagram of the architecture of a training probability graph model according to an embodiment of the present invention;

[0019] Figure 4 is a schematic diagram of variable categories according to an embodiment of the present invention;

[0020] Figure 5A and Figure 5B is a schematic diagram of the association between variable settings and structures according to an embodiment of the present invention;

[0021] Figure 6 is a schematic diagram of a DAG according to an embodiment of the present invention;

[0022] Figure 7 is a schematic diagram of a presentation screen of an interactive interface according to an embodiment of the present invention;

[0023] Figures 8A to 8D is a schematic diagram of how to unfold a hierarchical structure diagram according to an embodiment of the present invention;

[0024] Fig. 9 is a schematic diagram of an expanded result of a hierarchical structure diagram according to another embodiment of the present invention;

[0025] Fig.10 is a schematic diagram of a comparison page according to an embodiment of the present invention;

[0026] Fig.11 FIG. 4 is a schematic diagram of a visual presentation provided by a degree center algorithm according to an embodiment of the present invention.

[0027] Explanation of symbols

[0028] 100: Electronic devices

[0029] 110: Processor

[0030] 120: Memory

[0031] 130: Probabilistic graphical model

[0032] 135: Knowledge Graph

[0033] 140:Interactive interface

[0034] 150: Display

[0035] 510, 520, 600: DAG

[0036] 700: Presentation screen

[0037] 710~740: Area

[0038] 800: Hierarchical structure diagram

[0039] 810~860: Class

[0040] 811-813, 821-826, 831-836, 841-843, 851-856: nodes

[0041] 1000: Compare pages

[0042] 1100:Analysis page

[0043] D1, D2: variable setting

[0044] L1~L4、L21~L24: connecting line

[0045] R: root node

[0046] S205~S220: Steps of recommended maintenance service method DETAILED DESCRIPTION

[0047] Figure 1 is a block diagram of an electronic device according to an embodiment of the present invention. Figure 1 , the electronic device 100 includes a processor 110 and a memory 120. The memory 120 includes a probability graph model 130, a knowledge graph 135 and an interactive interface 140. In addition, the electronic device 100 also includes a display 150, so that the processor 110 can display the interactive interface 140 on the display 150.

[0048] The processor 110 is, for example, a central processing unit (CPU), a physical processing unit (PPU), a programmable microprocessor (Microprocessor), an embedded control chip, a digital signal processor (DSP), an application specific integrated circuit (ASIC) or other similar devices.

[0049] The memory 120 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk or other similar device or a combination of these devices. The memory 120 also includes one or more program code segments, which, after being installed, will be used by the processor 110 to execute the steps of the following maintenance service recommendation method.

[0050] The display 150 is, for example, a liquid crystal display (LCD) or a plasma display (Plasma Display), and is used to display the interactive interface 140 .

[0051] Figure 2 is a flowchart of a maintenance service recommendation method according to an embodiment of the present invention. Figure 1 and Figure 2 In step S205, a trained probability graph model 130 and a knowledge graph 135 are provided. In one embodiment, a service maintenance data set is provided, and the service maintenance data set is split into a training data set and a test data set. Then, the probability graph model 130 is subjected to structural learning based on the training data set to obtain the knowledge graph 135. Moreover, parameter learning is performed on the probability graph model 130 based on the training data set and the knowledge graph 135. Afterwards, cross-validation is performed on the probability graph model 130 based on the test data set.

[0052] In this embodiment, a Bayesian network is used to implement the probability graph model 130, which can provide reasons and explainable prediction results. The knowledge graph 135 is a directed acyclic graph (DAG). Here, bnlearn is used to learn the graph architecture of the Bayesian network and estimate the parameters.

[0053] Figure 3is a diagram of the architecture of a training probability graph model according to an embodiment of the present invention. Figure 3 The training of the probability graph model 130 includes three stages, namely, data preparation, model building, and verification processing.

[0054] In the data preparation stage, the service maintenance data set is first collected. The sources of the service maintenance data set are, for example, design data of the project, call log data when the machine is sent for repair, BIOS (Basic Input Output System) event log, etc. The design data includes, but is not limited to, call log data when the machine is sent for repair, field repair data, board return repair data, shipping data, research and development engineer report, design analysis tool (DAT) report, DC power supply, and product bill of materials (BOM).

[0055] Next, the data in the service maintenance data set is encoded, for example, using one-hot encoding to obtain corresponding numeric values. The encoded data is then split into a training data set and a test data set. For example, 90% of the data in the service maintenance data set is used as a training data set, and 10% of the data is used as a test data set.

[0056] In addition, variable categories are established to classify multiple data in the service maintenance data set. And, minority classes are further combined. For example, the categories with less than 10 data entries are combined into one category. For long-tail distribution data, there are many categories that need to be predicted, so the categories with less than 10 data entries are combined into one.

[0057] Figure 4 is a schematic diagram of variable categories according to an embodiment of the present invention. Figure 4, variable categories include project, subproject, hardware specification, component, function module (FM), symptoms, manufacturer, origin, import time, etc. Each variable category includes multiple types. Figure 4 As shown, the project includes 4 types, such as "Cyborg", "Mockingbird", "SP13", and "SP15". The hardware specifications include 9 hardware specifications, namely: Central Processing Unit (CPU), battery, hard disk 1 (such as a solid state drive), hard disk 2 (such as a traditional hard disk), liquid crystal display (Liquid-Crystal Display, LCD), ADT (Accelerated Display Technology) display card, motherboard, dual in-line memory module 1 (DIMM1; Dual In-line Memory Module (DIMM)), DIMM2.

[0058] In the model building stage, a probability graph model 130 is established to learn the service maintenance data set through the probability graph model 130, thereby establishing a knowledge graph 135 and a conditional probability distribution (CPD).

[0059] For example, the probability graph model 130 is structured based on the training data set to obtain the knowledge graph 135. Furthermore, parameter learning is performed on the probability graph model 130 based on the training data set and the knowledge graph 135. Afterwards, the probability graph model 130 after training is used to calculate multiple probability values ​​of multiple factors.

[0060] In one embodiment, a directed acyclic graph (DAG) of a Bayesian network is used as the knowledge graph 135. The nodes in the DAG represent variables, such as observable variables, potential variables, unknown variables, etc. The arrow connecting two nodes represents that the two variables have a causal relationship. If there is no arrow connecting the two nodes, the two variables are said to be conditionally independent of each other. If two nodes are connected by a single arrow, it means that one of the nodes is a "cause (parents)" and the other is a "result (descendants or children)", and the two nodes will generate a probability value.

[0061] In the model building stage, it includes: structural learning, which is used to define the relationship between the variable categories (composed of multiple sets of variables) of the service maintenance data set to establish a directed acyclic graph; parameter learning, which is used to quantify the strength of the relationship between variables, thereby establishing a conditional probability distribution (CPD). The role of structural learning is to learn the relationship between the variables in the service maintenance history data set of the damaged machine. For example, the service maintenance history data set includes the hardware specifications of the damaged machine, the symptoms of the error, which "component" was removed to solve the symptoms, the detailed information of the component (for example: functional module, manufacturer, etc.), and the origin of the damaged machine. The output of structural learning is a knowledge graph (such as DAG), which defines the relationship between all the variables used.

[0062] The role of parameter learning is to learn CPD. CPD describes the strength of each relationship defined in the DAG. The quantified CPD impact of the parent node on the corresponding child node can be known. Therefore, when performing estimation / forecasting, CPD can be used as the basis for the result estimation. This transparency can support and assist the relevant technicians in performing maintenance services, and the technicians will be able to consider alternatives based on the estimation results. In addition, other analyses corresponding to CPD (such as comparative analysis) can be used to improve decision-making.

[0063] During the verification process, the top K highest probability values ​​are taken from the multiple probability values ​​calculated by the probability graph model 130 based on the training data set, and the top K highest accuracy rates are taken from the multiple probability values ​​calculated by the probability graph model 130 based on the test data set to perform cross-validation.

[0064] In other embodiments, in order to improve accuracy, the type of variables and the variable content (factors) included therein may be further adjusted. Figure 4 The 17 variable categories shown are changed to 12 variable categories, for example, Figure 4 In the original 9 hardware specifications, it was adjusted to use 5, and the import time was removed. Figure 5A and Figure 5B It is a schematic diagram of variable settings and structural association according to an embodiment of the present invention. Figure 5A The example shows the increase of variable data. Set D1 is changed to variable set D2. Figure 5B The DAG 510 and DAG 520 generated based on the variable setting D1 and the variable setting D2 are illustrated respectively. Figure 5A and Figure 5BAs shown, both variable setting D1 and variable setting D2 include 12 variable categories, namely: project, sub-project, CPU, battery, hard disk 1, hard disk 2, LCD, symptom, origin, component, functional module, and manufacturer. The variable categories of variable setting D1 and variable setting D2 include different numbers of types, and the structure of the obtained DAG will also be different. Based on variable setting D1, DAG510 is obtained. Based on variable setting D2, DAG520 is obtained.

[0065] return Figure 2 In step S210, the relevant data of the damaged machine is input into the probability graph model 130 through the interactive interface 140, and the recommended factor is obtained. In this embodiment, the knowledge graph 135 is, for example, a DAG, which is used to represent the association between multiple variable categories, each variable category includes multiple factors, and the recommended factor is one of the factors included in the recommended category in the variable category. Different input data will obtain different knowledge graphs 135.

[0066] In step S215, a search is performed in the knowledge graph 135 to select a plurality of selected categories related to the recommendation factor from the plurality of variable categories. Here, the selected category includes the recommendation category to which the recommendation factor belongs and one or more related categories in other variable categories that are associated with the recommendation category.

[0067] Figure 6 is a schematic diagram of a DAG according to an embodiment of the present invention. In this embodiment, please refer to Figure 6 , the probability graph model 130 is used to recommend components to be repaired (recommendation factor), so the recommended category is the "component" category. Figure 6 From the DAG 600 shown, it can be seen that the "component" category corresponds to node 601, and nodes 602 to 606 are associated with node 601. Based on this, the selected categories related to the recommended factors are the variable categories corresponding to nodes 601 to 606, including "component", "LCD", "functional module", "hard disk 2", "manufacturer", and "symptom". Figure 6 In the DAG 600 shown, the nodes 601-606 presented are respectively a set of variable categories. In addition, multiple nodes corresponding to all factors can also be presented in one decomposition diagram.

[0068] Next, in step S220, based on the importance of the selected categories, a hierarchical structure diagram is established and presented to the interactive interface 140. Here, the hierarchical structure diagram includes multiple hierarchies corresponding to the multiple selected categories, and each hierarchy has multiple nodes corresponding to the multiple factors included in the corresponding selected category. For example, in the data collection stage, relevant technical personnel can be asked about the importance of the strongly correlated variable categories corresponding to different recommendation factors. And in the model learning stage, variables of importance are added for learning.

[0069] In one embodiment, the hierarchical structure diagram is, for example, a causal tree. The root node of the causal tree represents the origin and does not correspond to any variable category. Variable categories with higher importance will be set at the level closer to the root node. For example, assuming that the importance is ranked from high to low as: "Functional module", "Symptom", "Manufacturer", "Component", "LCD", "Hard disk 2", then the first to sixth levels starting from the root node are set to "Functional module", "Symptom", "Manufacturer", "Component", "LCD", "Hard disk 2" respectively.

[0070] Afterwards, in response to one of the nodes included in the hierarchy being selected, a connection relationship between the selected node and its corresponding parent node in the upper hierarchy is visually presented in the hierarchy diagram.

[0071] Figure 7 is a schematic diagram of a display screen of an interactive interface according to an embodiment of the present invention. Figure 7 The display screen 700 of the interactive interface 140 includes a plurality of regions 710 to 740. Region 710 is used to provide the user with the opportunity to input relevant data of the damaged machine. Region 720 is used to present the recommended ranking of the representative factors. Region 730 is used to present the DAG (such as Figure 6 Region 740 is used to present a hierarchical diagram.

[0072] In one embodiment, area 710 is provided with a plurality of selection fields according to a plurality of variable categories. The user selects "Mockingbird" in the project field in area 710, selects "MKBV14TGL" in the sub-project field, and selects a plurality of factors in the symptom field, such as no boot, no POST (Power-On Self-Test), no power, no AC adapter power, and no battery power.

[0073] Area 720 is used to present the recommended ranking of representative factors. Multiple probability values ​​of multiple factors included in the recommendation category are calculated by the probability graph model 130. Based on the probability value, multiple representative factors are taken out from the factors in a high to low manner, for example, the top 10 factors with high probability values ​​are taken out. In area 720 of the presentation screen 700, the representative factors are sorted and displayed. Among these representative factors, the one with the highest probability value is the final recommended factor. In this embodiment, a bar graph is used to represent the number of probability values. In other embodiments, the percentage of historical statistics can be further displayed in area 720.

[0074] Figures 8A to 8D FIG. 1 is a schematic diagram of how to expand a hierarchical structure diagram according to an embodiment of the present invention. In this embodiment, it is assumed that the recommended component to be repaired (recommended factor) obtained by the probability graph model 130 is “CPU1”. Figure 6 For the DAG 600 shown, the selected categories strongly related to the component to be repaired "CPU1" are "component" (the variable category to which the component to be repaired "CPU1" belongs), "LCD", "function module", "hard disk 2", "manufacturer", and "symptom". The inheritance relationship of multiple hierarchies is determined according to the importance of the selected categories. For example, hierarchies 810 to 860 are respectively set to "function module", "symptom", "manufacturer", "component", "LCD", and "hard disk 2". In addition, the inheritance relationship between the factors included in each selected category is determined based on the DAG obtained by the probability graph model 130.

[0075] The hierarchical diagram 800 shows a root node R, which is used to represent the historical statistical sum calculated by the processor 110 in the training data set based on the selection in the area 710. For example, assuming that the user selects the project "Mockingbird", the sub-project "MKBV14TGL" and several symptoms through the fields in the area 710, the processor 110 finds data in the training data set that meets the above selections to calculate the historical statistical sum.

[0076] After the relevant data of the damaged machine is input into the probability diagram model 130 through the presentation screen 700 to obtain the DAG 600 and the component to be repaired "CPU1" (recommended factor), as shown in FIG. Fig. 8A As shown, the root node R, the layers 810-860 and the expansion result of the layer 810 as the first layer can be displayed in the layer structure diagram 800. Here, only three representative nodes 811-813 in the layer 810 are listed.

[0077] Specifically, for the layer 810 (first layer), the processor 110 calculates the historical statistics of the multiple factors included in the corresponding variable category "functional module". For example, for the functional module "FLASH / RTC", the data matching the project "Mockingbird", the sub-project "MKBV14TGL", several symptoms and the functional module "FLASH / RTC" are found in the training data set to calculate the historical statistics of 2248. After calculating the historical statistics of each factor in the variable category "functional module", the nodes 811-813 corresponding to the top three with the highest historical statistics are displayed in the corresponding columns of the layer 810.

[0078] in addition, Figures 8A to 8D The symbol "+" shown represents that the corresponding node has a corresponding node set that can be expanded in the next level. In response to one of the nodes being selected, the connection relationship between the selected node and its corresponding parent node in the previous level is presented in a visual manner, and the node set corresponding to the selected node included in the next level of the selected node is expanded.

[0079] like Figure 8B As shown, in response to the node 812 being selected, the connection relationship between the node 812 and the root node R is presented in a visual manner, for example, the root node R and the node 812 are connected by a connection line L1. At the same time, the next level of the node 812, that is, the node set corresponding to the selected node 812 included in the level 820 is expanded, and based on the historical statistics of each node in the node set, a plurality of (here three) representative nodes (i.e., nodes 821-823) are selected from the node set and presented in the hierarchical structure diagram 800. The sum of the historical statistics of all nodes in the node set corresponding to the node 812 in the level 820 is equal to the historical statistics of the node 812.

[0080] Then, if Figure 8C As shown, in response to node 821 being selected, node 812 and node 821 are connected by connecting line L2. At the same time, the next level of node 821, that is, the node set corresponding to the selected node 821 included in level 830 is expanded, and based on the historical statistics of each node in the node set, multiple (here three) representative nodes (that is, nodes 831 to 833) are selected from the node set and presented in the hierarchical structure diagram 800. Similarly, node 831 is selected in level 830, and node 841 is selected in level 840, and the following is obtained: Fig.8D shown.

[0081] The hierarchical structure diagram 800 is, for example, a causal tree, the purpose of which is to promote the interpretability of predictions by looking at causal relationships. In addition, the hierarchical structure diagram 800 can also be used as a basis for determining the root cause of a problem.

[0082] For example, if a technician wants to check what can be improved in the functional module "CPU", he can select node 812 corresponding to the functional module "CPU" and find that the historical statistics of the symptom "NO POWER" are the largest. Next, he selects node 821 corresponding to the symptom "NOPOWER" and finds that the historical statistics of the manufacturer "M1" are the largest. After that, he selects node 831 corresponding to the manufacturer "M1" and finds that it only includes one component "CPU1". Then he selects node 841 corresponding to the component "CPU1" and finds that the historical statistics of the LCD model "LCD 14"FHX INX N140HCA-EAC" are the highest.

[0083] In the case where component "CPU1" is the component to be repaired, based on the above-mentioned hierarchical structure diagram 800, the technician can analyze the detailed information of component "CPU1" to investigate the potential cause. For example, the technician can check the material quality of "M1", the only manufacturer of component "CPU1". Alternatively, the technician checks whether component "CPU1" is compatible with "LCD 14" FHX INX N140HCA-EAC, which has a higher usage frequency (casefrequency). The technician can also investigate whether there are potential assembly problems or other components / factors that are incompatible with component "CPU1" in the sub-project "MKBV14TGL" of the project "Mockingbird". The hierarchical structure diagram 800 helps the technician determine the root cause of the problem and possible solutions.

[0084] Fig. 9 It is a schematic diagram of an expanded result of a hierarchical structure diagram according to another embodiment of the present invention. Fig. 9 The embodiment shown is Figures 8A to 8D Another expanded result of the embodiment shown. Please refer to Fig. 9 First, in level 810, select node 813, connect the root node R and node 813 with a connection line L21, and expand the next level of node 813, that is, nodes 824 to 826 corresponding to level 820. Next, select node 824, connect node 813 and node 824 with a connection line L22, and expand the next level of node 824, that is, nodes 834 to 836 corresponding to level 830. Then, select node 834, connect node 824 and node 834 with a connection line L23, and expand the next level of node 834, that is, nodes 842 to 843 corresponding to level 840. Then, select node 842, connect node 834 and node 842 with a connection line L24, and expand the next level of node 842, that is, nodes 854 to 856 corresponding to level 850. Expand each level in this way.

[0085] exist Fig. 9In the expanded results shown, the technician wants to check what can be improved in the functional module "POWER". He can select node 813 corresponding to the functional module "POWER" and find that the historical statistics of the symptom "NO POWER" are the largest. It can be seen at level 830 that the manufacturer "M4" has the largest historical statistics, and it includes two components "PU4401" and "PU4601" at the next level. Therefore, the technician can determine that the two types of components are prone to internal damage. If improvements are to be made, it is necessary to further analyze whether the corresponding manufacturer "M4" has problems in the procurement process, assembly of components, or inappropriate component warehousing. The technician can also use level 850 to check whether the type of LCD is a problem that affects the damage to the machine. For example, analyze whether the type of LCD is compatible with the component to be repaired.

[0086] The hierarchical diagram 800 derived from nodes 601-606 in DAG 600 can help technicians understand the cause-effect relationship and find the root cause of the problem. By analyzing the variable categories such as "functional module", "manufacturer", "LCD", etc., the technician can determine whether the problem is caused by procurement, assembly, warehousing or component compatibility. This presentation screen 700 is conducive to suggestions and improvement analysis for the maintenance process.

[0087] In one embodiment, the interactive interface 140 further provides a comparison page to compare the results between different variable categories. Specifically, through the comparison page, the first factor and the second factor are selected in the first category of the variable category, and the third factor is selected in the second category of the variable category. The first factor and the third factor are input into the probability graph model 130, and a plurality of first probability values ​​of a plurality of factors included in a third category of the variable category are obtained. The second factor and the third factor are input into the probability graph model 130, and a plurality of second probability values ​​of a plurality of factors included in the third category are obtained. Afterwards, the first probability value and the probability value are displayed in the comparison page.

[0088] Fig.10 is a schematic diagram of a comparison page according to an embodiment of the present invention. Fig.10 , the technician can analyze the comparison page 1000 to find the amount that can reduce the symptom "NO POWER". Therefore, in the comparison page 1000, the technician selects the project "Monckingbird" and the project "Cyborg" (first factor, second factor) in the variable category "Project" (first category), and selects the symptom "NOPOWER" (third factor) in the variable category "Symptom" (second category).

[0089] Based on the selection, the probability value of each variable (factor) corresponding to the variable category "functional module" (third category) of both the project "Monckingbird" and the project "Cyborg" and the ratio of each variable to the two categories are displayed in the comparison page 1000. For example, for the functional module "INT IO" (factor), the probability value of the project "Monckingbird" is 0.029764, and the probability value of the project "Cyborg" is 0.025276, and the ratio of the two is 0.029764 / 0.025276.

[0090] The higher the ratio, the higher the probability that the repair component is damaged in the project "Monckingbird" than in the project "Cyborg". The closer the ratio is to 0, the higher the probability that the repair component is damaged in the project "Cyborg" than in the project "Monckingbird".

[0091] like Fig.10 As shown, the function module "Flash / RTC" has the highest ratio, which means that the probability of the component to be repaired being damaged in the project "Monckingbird" is much higher than that in the project "Cyborg". It can be inferred that the defect problem of the component to be repaired may be highly related to the machine type. If the assembly process of the project "Monckingbird" and the project "Cyborg" are different, it can be inferred that the defect problem of the component to be repaired may be related to the assembly process. Alternatively, assuming that component A exists in the project "Monckingbird" and component A does not exist in the project "Cyborg", it can be inferred that the component to be repaired and component A may be incompatible.

[0092] In addition, when the ratio is close to 1 or equal to 1, it means that the component to be repaired is not related to the variable category "project" and may break down in any project. After that, reselect another variable category, such as "manufacturer" for comparison to further analyze whether there is a problem with the material quality. In addition, if the damage problem is not related to the component, the similarity of the assembly process, features, or components between different projects can be analyzed to determine whether there is a problem with the assembly process or an incompatibility problem.

[0093] When the ratio is close to 0, for the functional module “GPU1”, the ratio is close to 0, which means that the probability of the component to be repaired being damaged in the project “Cyborg” is much higher than that in the project “Mockingbird”.

[0094] Fig.11is a schematic diagram of a visual representation provided by a degree centrality algorithm according to an embodiment of the present invention. The degree centrality algorithm is a tool that can be used to identify popular nodes in a given graph by measuring the number of incoming or outgoing relationships from a node, depending on the relationship projection direction.

[0095] Please refer to Fig.11 , the analysis page 1100 utilizes the degree centrality algorithm to combine big data and graph algorithms to identify key nodes in the data that need to be improved. The analysis page 1100 displays the most influential nodes (left table), indicating the nodes that should be improved first.

[0096] The top N influential nodes can be found by analyzing page 1100. For example, choose to find the top 11 and calculate the corresponding weight scores. In this embodiment, the component "CPU1" has the highest connectivity. The technician can further analyze the graph on the right side of the analysis page 1100 to view the correlation between components, manufacturers, and symptoms. For example, the corresponding properties and neighbor information can be displayed by clicking on a node. The property bar is used to indicate the algorithm value, node name, probability value, etc. of the graph algorithm. The neighbor bar is used to indicate the connection with the selected node (symptom and manufacturer) and its score.

[0097] based on Fig.8D , in the case of the analyzed component "CPU1", which has only one manufacturer "M1". In other cases, e.g. Fig. 9 The component "PU4401" shown involves 2 manufacturers, so analyzing the connectivity and shared characteristics of both with component "PU4401" (using Louvain community detection) can help identify possible causes of defects. This approach allows technicians to prioritize improvements and gain insight into potential problems in the network.

[0098] In summary, the present invention uses a trained probability graph model to construct a knowledge graph and infer a recommendation factor, and finds selected categories that are strongly related to the recommendation factor based on the knowledge graph, and generates a hierarchical structure diagram. The hierarchical structure diagram can promote the interpretability of predictions by viewing causal relationships, and can also serve as a basis for determining the root cause of the problem. Based on this, it can be used by relevant personnel to explore the cause of the machine damage. Based on this, it can not only be used to recommend or give prediction results, but also perform causal reasoning to further assist technicians in the service and maintenance process.

Claims

1. A maintenance service recommendation method, suitable for being executed by a processor, the maintenance service recommendation method comprising: Provide trained probability graph models and knowledge graphs; Inputting relevant data of the damaged machine into the probability graph model and obtaining a recommended factor, wherein the knowledge graph represents the association between a plurality of variable categories, each of the variable categories includes a plurality of factors, and the recommended factor is one of the factors included in the recommended category in the variable categories; Searching the knowledge graph to select a plurality of selected categories associated with the recommendation factor from among the variable categories; and Based on the importance of each of the selected categories, a hierarchical structure diagram is established and presented in the interactive interface, wherein the hierarchical structure diagram includes a plurality of hierarchies corresponding to the selected categories, and each of the hierarchies has a plurality of nodes corresponding to the factors included in each of the selected categories.

2. The maintenance service recommendation method according to claim 1, further comprising: Performing structural learning on the probability graph model based on the training data set to obtain the knowledge graph; Based on the training data set and the knowledge graph, performing parameter learning on the probability graph model; as well as Cross validation is performed on the probability graphical model based on the test dataset.

3. The maintenance service recommendation method as claimed in claim 1, wherein the step of establishing the hierarchical structure diagram comprises: Based on the importance of each of the selected categories, the inheritance relationship of the hierarchies is determined.

4. The maintenance service recommendation method according to claim 1, wherein after the hierarchical structure diagram is established and presented in the interactive interface, the method further comprises: In response to one of the nodes included in the layers being selected, a connection relationship between the selected node and its corresponding parent node in the upper layer is visually presented in the layer structure diagram.

5. The maintenance service recommendation method as claimed in claim 4, wherein in response to one of the nodes included in the layers being selected, the method further comprises: Expand the node set included in the next level of the selected node; as well as Based on the historical statistics of each node in the node set, a plurality of representative nodes are selected from the node set and presented in the hierarchical structure diagram.

6. The maintenance service recommendation method according to claim 5, further comprising: For each of the nodes presented in the hierarchical structure diagram, the corresponding historical statistics are displayed respectively.

7. The repair service recommendation method as claimed in claim 1, wherein after inputting the relevant data of the damaged machine into the probability graph model through the interactive interface, the method further comprises: Calculating a plurality of probability values ​​of the factors included in the recommendation category by using the probability graph model; Based on the probability values, a plurality of representative factors are selected from the factors in a descending order of values; as well as In the interactive interface, the representative factors are sorted and displayed, wherein the representative factors with the highest probability value are used as the final recommendation factor.

8. The repair service recommendation method as claimed in claim 1, wherein the interactive interface comprises a comparison page, and the repair service recommendation method further comprises: Through the comparison page, selecting a first factor and a second factor in a first category among the variable categories, and selecting a third factor in a second category among the variable categories; Inputting the first factor and the third factor into the probability graph model, and obtaining a plurality of first probability values ​​of a plurality of factors included in the third category among the variable categories through the first knowledge graph; Inputting the second factor and the third factor into the probability graph model, and obtaining a plurality of second probability values ​​of a plurality of factors included in the third category among the variable categories through a second knowledge graph; and The first probability values ​​and the second probability values ​​are displayed on the comparison page.

9. The maintenance service recommendation method as claimed in claim 1, wherein the knowledge graph is a directed acyclic graph, and the probability graph model is established using a Bayesian network.

10. An electronic device comprising: Storage, including trained probabilistic graph models, knowledge graphs, and interactive interfaces; as well as A processor is coupled to the memory and is configured to: Inputting relevant data of the damaged machine into the probability graph model through the interactive interface, and obtaining a recommended factor, wherein the knowledge graph is used to represent the correlation between a plurality of variable categories, each of the variable categories includes a plurality of factors, and the recommended factor is one of the factors included in the recommended category in the variable categories; Searching the knowledge graph to select a plurality of selected categories associated with the recommendation factor from among the variable categories; Based on the importance of each of the selected categories, a hierarchical structure diagram is established and presented in the interactive interface, wherein the hierarchical structure diagram includes a plurality of hierarchies corresponding to the selected categories, and each of the hierarchies has a plurality of nodes corresponding to the factors included in each of the selected categories.

11. The electronic device of claim 10, wherein the processor is configured to: Performing structural learning on the probability graph model based on the training data set to obtain the knowledge graph; Based on the training data set and the knowledge graph, performing parameter learning on the probability graph model; and Cross validation is performed on the probability graphical model based on the test dataset.

12. The electronic device of claim 10, wherein the processor is configured to: Based on the importance of each of the selected categories, the inheritance relationship of the hierarchies is determined.

13. The electronic device of claim 10, wherein after the hierarchical structure diagram is created and presented in the interactive interface, the processor is configured to: In response to one of the nodes included in the layers being selected, a connection relationship between the selected node and its corresponding parent node in the upper layer is visually presented in the layer structure diagram.

14. The electronic device of claim 13, wherein in response to one of the nodes included in the layers being selected, the processor is configured to: Expand the node set included in the next level of the selected node; and Based on the historical statistics of each node in the node set, a plurality of representative nodes are selected from the node set and presented in the hierarchical structure diagram.

15. The electronic device of claim 14, wherein the processor is configured to: For each of the nodes presented in the hierarchical structure diagram, the corresponding historical statistics are displayed respectively.

16. The electronic device of claim 10, wherein the processor is configured to: Calculating a plurality of probability values ​​of the factors included in the recommendation category by using the probability graph model; Based on the probability values, extract a plurality of representative factors from the factors in a descending order; and In the interactive interface, the representative factors are sorted and displayed, wherein the representative factors with the highest probability value are used as the final recommendation factor.

17. The electronic device of claim 10, wherein the interactive interface comprises a comparison page, and the processor is configured to: Through the comparison page, selecting a first factor and a second factor in a first category among the variable categories, and selecting a third factor in a second category among the variable categories; Inputting the first factor and the third factor into the probability graph model, and obtaining a plurality of first probability values ​​of a plurality of factors included in the third category among the variable categories through the first knowledge graph; Inputting the second factor and the third factor into the probability graph model, and obtaining a plurality of second probability values ​​of a plurality of factors included in the third category among the variable categories through a second knowledge graph; and The first probability values ​​and the second probability values ​​are displayed on the comparison page.

18. The electronic device as described in claim 10, wherein the knowledge graph is a directed acyclic graph, and the probability graph model is established using a Bayesian network.

19. A non-transitory computer-readable recording medium for storing program code, a trained probability graph model, a knowledge graph, and an interactive interface, wherein when the program code is executed by a processor, the processor performs the following steps: Inputting relevant data of the damaged machine into the probability graph model through the interactive interface, and obtaining a recommended factor, wherein the knowledge graph is used to represent the correlation between a plurality of variable categories, each of the variable categories includes a plurality of factors, and the recommended factor is one of the factors included in the recommended category in the variable categories; Searching the knowledge graph to select a plurality of selected categories associated with the recommendation factor from among the variable categories; Based on the selected categories and the importance of each of the selected categories, a hierarchical structure diagram is established and presented in the interactive interface, wherein the hierarchical structure diagram includes multiple hierarchies corresponding to the selected categories, and each of the hierarchies has multiple nodes corresponding to the factors included in each of the selected categories.

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