Knowledge graph-based first set of equipment technology maturity evaluation model construction method

Abstract

The application discloses a knowledge graph-based first set of equipment technical maturity evaluation model construction method, quantifies the connection degree of the first set of equipment to the upstream and downstream, obtains a synergy weight, and quantifies the independent operation capability of the first set of equipment, obtains an expansion weight, and measures the role of the first set of equipment in the production process through the two weights. Then, combined with the performance index of the first set of equipment, a preset equipment maintenance system is used to simulate possible fault conditions in the running process, so as to measure the technical maturity and realize the quantification of the technical maturity. In addition, the technical means of the knowledge graph is also used to realize the multi-dimensional representation of the characteristics of the first set of equipment, which is conducive to obtaining more accurate evaluation results. On the one hand, the method in the specification realizes the evaluation of the technical maturity of the first set of equipment through technical means specially suitable for digital calculation or data processing of specific applications.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing technology, and in particular to a method of digital computing or data processing specifically suitable for specific applications, specifically a method for constructing a first-of-its-kind equipment technology maturity assessment model based on a knowledge graph. Background Art

[0002] A set of equipment refers to the complete set of equipment required to produce a product or complete a specific task or function. For example, a CNC machine tool integrates CNC machining, automatic tool head replacement, and communication functions. Another example is a wind turbine, which can generate electricity while also detecting factors such as wind speed.

[0003] As the trend of domestic substitution spreads across various fields, equipment and sets that previously relied on imports are gradually being replaced by domestic competitors. On the one hand, the industry's dependence on traditional equipment has already taken shape. If the first set of equipment launched cannot meet users' demand for traditional equipment, it will be difficult to achieve the goal of replacement. On the other hand, if the first set of equipment is only used to replace traditional equipment and only uses price as an advantage to participate in market competition, it will be in a "catch-up" situation for a long time, making it difficult to demonstrate its own advantages, and gradually being squeezed out of the market. If the technical maturity of the first set of equipment can be fully and comprehensively evaluated before it comes out, and the maturity of the first set of equipment can be guaranteed to match the market in the research and development, trials, and tests, it will be beneficial to both the manufacturer of the first set of equipment and the user.

[0004] For example, the patent title is: A method for calculating the spare parts demand for multiple sets of equipment, the application number is: CN201810124190.6, and the main classification number is G06F17 / 18. On the one hand, it achieves the guarantee effect of maintenance work; on the other hand, it shows that the maintenance and evaluation operations for sets of equipment have a relatively broad prospect in this technical field. Summary of the Invention

[0005] The embodiments of the present application provide a method for constructing a first-set equipment technology maturity assessment model based on a knowledge graph to at least partially solve the above-mentioned technical problems.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for constructing a first-of-its-kind equipment technology maturity assessment model based on a knowledge graph, the method comprising:

[0008] Determine the synergy weight and expansion weight of the target set of equipment; the synergy weight represents the degree of connection between the target set of equipment and its upstream and downstream; the expansion weight represents the degree of functional realization of the target set of equipment when it is independent of its upstream and downstream;

[0009] Using preset quantification software, quantify various performance indicators of the target set of equipment to obtain quantitative values;

[0010] When the synergy weight is greater than a preset synergy weight threshold, a usable graph is constructed with the first indicator as a node; the first indicator is one of the performance indicators that has a connection relationship between upstream and downstream; the degree of connection between the first indicators is used as the weight; and the weight when the first indicator corresponds to the second indicator is greater than the weight when the first indicator does not correspond to the second indicator; the second indicator is a performance indicator other than the first indicator;

[0011] Using a preset equipment maintenance system, simulating a situation when the target equipment fails, and determining a comprehensive equipment risk based on the available map;

[0012] Determine a maturity score of the target set of equipment; the maturity score is negatively correlated with the comprehensive equipment risk, and when the comprehensive equipment risk is less than a preset risk threshold, the maturity score is also positively correlated with the orthogonality degree of the second indicator.

[0013] In an optional embodiment of this specification, the method further includes:

[0014] When the synergy weight is greater than the synergy weight threshold, if the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the synergy weight; if the comprehensive equipment risk is not less than the risk threshold, the maturity score is also negatively correlated with the synergy weight.

[0015] In an optional embodiment of this specification, the method further includes:

[0016] In the case where the synergy weight is greater than the synergy weight threshold, if the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the expansion weight.

[0017] In an optional embodiment of this specification, the method further includes:

[0018] In the case where the collaborative weight is greater than the collaborative weight threshold, the weight of the available graph is also negatively correlated with the redundancy level of the node pair to which it belongs;

[0019] If the median or mean of the weights contained in the available graph is greater than a preset graph weight threshold, the maturity score is also negatively correlated with the synergy weight.

[0020] In an optional embodiment of this specification, the method further includes:

[0021] When the collaborative weight is not greater than the collaborative weight threshold, the performance indicators are divided into a plurality of indicator sets, so that the indicator sets correspond one-to-one to the outputs of the target set of equipment; and there may be intersections between different indicator sets;

[0022] For each indicator set, an intermediate graph is constructed; the intermediate graph uses the performance indicators in the corresponding indicator set as nodes, and the completion degree of the output corresponding to the indicator set by the target set of equipment when two adjacent nodes are abnormal is used as the weight between the two adjacent nodes;

[0023] The contribution of the output to the function of the target set of equipment is weighted as the weight of the corresponding intermediate map to obtain an available map;

[0024] Using the equipment maintenance system, simulating a situation when the target equipment fails, and determining a comprehensive equipment risk based on the available map;

[0025] A maturity score of the target set of equipment is determined; the maturity score is negatively correlated with the comprehensive equipment risk.

[0026] In an optional embodiment of this specification, the method further includes:

[0027] Determining a first output from the outputs; the first output having the smallest degree of comprehensive orthogonality with the other outputs;

[0028] When the contribution degree of the first output is less than a preset contribution threshold, the maturity score obtained when the device risk obtained based on its corresponding available map is less than the preset output risk threshold is greater than the maturity score obtained when the device risk obtained based on its corresponding available map is not less than the output risk threshold.

[0029] In an optional embodiment of this specification, the method further includes:

[0030] The contribution threshold is negatively correlated with the market value of a device capable of achieving the first output in the market.

[0031] In an optional embodiment of this specification, the method further includes:

[0032] If the synergy weight is not greater than the synergy weight threshold, and the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the number of the indicator sets and the expansion weight.

[0033] In an optional embodiment of this specification, the method further includes at least one of the following:

[0034] The equipment maintenance system is KGPHMAgent;

[0035] The quantification software includes at least one of the following: Apifox software, kylinPET software, VERICUT software, Renishaw Ballbar Trace software, OpenFAST software, and WindPRO software;

[0036] The collaboration weight threshold when there is no competitor of the target set of equipment is smaller than the collaboration weight threshold when there is a competitor of the target set of equipment.

[0037] In an optional embodiment of this specification, the method further includes:

[0038] If the synergy weight is greater than the synergy weight threshold and the comprehensive equipment risk is less than the risk threshold, the maturity score is also negatively correlated with the ratio of the synergy weight to the expansion weight.

[0039] In the second aspect, an embodiment of the present application also provides a device for constructing a first-set equipment technology maturity assessment model based on a knowledge graph, which is used to implement the method steps in the first aspect.

[0040] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0041] processor; and

[0042] A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method steps of the first aspect.

[0043] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0044] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0045] The present application provides a method for constructing a technology maturity assessment model for first-of-its-kind equipment based on a knowledge graph, which quantifies the degree of the first-of-its-kind equipment's connection with upstream and downstream equipment to obtain a synergy weight; it also quantifies the independent operation capability of the first-of-its-kind equipment to obtain an expansion weight, and uses these two weights to measure the role of the first-of-its-kind equipment in the production process. Afterwards, combined with the performance indicators of the first-of-its-kind equipment, a preset equipment maintenance system is used to simulate possible failures that may occur during its operation to measure its technology maturity and achieve quantification of technology maturity. In addition, the present application also uses the technical means of the knowledge graph to achieve a multi-dimensional characterization of the characteristics of the first-of-its-kind equipment, which is conducive to obtaining more accurate assessment results. On the one hand, the method in this specification achieves the assessment of the technology maturity of the first-of-its-kind equipment through technical means of digital calculation or data processing specifically suitable for specific applications. On the other hand, the maturity score obtained combines the synergy and extensibility of the equipment, and can be comprehensively quantified from the two aspects of traditional equipment replacement and function expansion, thereby improving the guidance of the maturity score to equipment manufacturers. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 A schematic diagram of the process of constructing a first-of-its-kind equipment technology maturity assessment model based on a knowledge graph provided in an embodiment of this specification;

[0048] Figure 2 This is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0049] The present invention will be further described in detail below with reference to the accompanying drawings by way of specific embodiments. Similar elements in different embodiments are numbered with associated similar elements. In the following embodiments, many detailed descriptions are provided to enable the present application to be better understood. However, those skilled in the art will readily appreciate that some of the features may be omitted under different circumstances, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the present application are not shown or described in the specification. This is to avoid overwhelm the core of the present application with excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They will fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0050] In addition, the features, operations, or characteristics described in the specification may be combined in any appropriate manner to form various embodiments. Furthermore, the steps or actions in the method description may be reordered or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various sequences in the specification and drawings are provided solely for the purpose of clearly describing a particular embodiment and are not intended to be mandatory, unless otherwise specified.

[0051] The serial numbers assigned to components herein, such as "first," "second," etc., are used solely to distinguish the objects being described and do not convey any sequential or technical meaning. References to "connection" and "coupling" herein, unless otherwise specified, include both direct and indirect connections (couplings).

[0052] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0053] The methods in this manual are implemented by the first-of-its-kind equipment technology maturity assessment model, which can contain several functional modules, and different modules are used to perform the following steps. The assessment of the technology maturity of a first-of-its-kind equipment is not achieved overnight. For example, in the equipment R&D scenario, the methods in this manual can be used to conduct a phased assessment of the equipment, and then make targeted improvements to the equipment. In other words, in the application scenario, the first-of-its-kind equipment technology maturity assessment model in this manual can play a role throughout the entire stage of equipment R&D.

[0054] like Figure 1 As shown in the figure, the method for constructing the first equipment technology maturity assessment model based on the knowledge graph in this manual includes the following steps:

[0055] S100: Determine the coordination weight and expansion weight of the target set of equipment.

[0056] The target equipment in this specification is the first equipment to be evaluated for technical maturity. As the first equipment, it must not only replace existing equipment but also surpass existing equipment and exert broader advantages.

[0057] The synergy weight and expansion weight in this specification can be obtained based on manual experience. The synergy weight represents the degree of connection between the target set of equipment and its upstream and downstream; the expansion weight represents the degree of function realization of the target set of equipment when it is independent of its upstream and downstream.

[0058] The collaboration weight and the expansion weight are independent of each other. For example, if the first set of equipment is a CNC machine tool, in the production line, the CNC machine tool not only has to process parts, but may also have to perform operations such as changing tools, loading and unloading, and online inspection of parts. If a problem occurs in a certain link, such as the loss of the automatic loading function, it may cause its operation to be unable to be completed continuously, and downstream processes such as polishing and heat treatment may be interrupted. In other words, CNC machine tools need to collaborate with other equipment in the production line to complete operations. Their usage scenarios and the conditions of the equipment themselves will reduce the efficiency of this collaboration, which makes the collaboration weight of CNC machine tools relatively high. The value of the synergy weight is affected by many factors, such as: the process link in the production line (the upstream link often restricts the downstream link, and the synergy weight of the first set of equipment in the upstream is higher), the quality stability of its output (for example, the higher the precision and pass rate of the workpiece produced by the CNC machine tool, the smaller the error impact on the further downstream process, and the higher its synergy weight), and the degree of functional integration (for example, a CNC machine tool that has both workpiece processing and workpiece polishing functions, if one of the functions is lost, the downstream will be more affected, and its synergy weight will be higher).

[0059] Take a wind turbine, for example. Its function is to convert wind energy into electricity. Wind energy is highly volatile, and power generation may be unavailable for several consecutive days. However, even if a wind turbine fails to generate power for a period of time, this will not affect the downstream power grid. Furthermore, even if the downstream power grid fails and cannot accept the wind turbine's power, the wind turbine can idle during periods of strong wind to avoid damage. In other words, the wind turbine has a certain degree of independence from its upstream and downstream systems, resulting in a lower synergy weight.

[0060] As for expansion weights, let's take CNC machine tools as an example. CNC machine tools can at least be used to process parts. Suppose a CNC machine tool cannot process parts due to a broken bearing, but its chip and communication module are intact. It can still perform certain functions beyond processing parts, such as supervising and managing other equipment in the workshop. This supervisory and management function has little to do with upstream and downstream operations, nor with the parts being processed. Therefore, it can have a higher expansion weight. If a CNC machine tool lacks supervisory and management capabilities, its expansion weight will be lower.

[0061] In cross-domain scenarios, the expansion weight can be higher. For example, the aforementioned CNC machine tool's expansion is reflected in the supervision and management of other equipment, which is generally within the scope of parts processing. Equipment like wind turbines, on the other hand, can have cross-domain expansion. For example, a wind turbine can be equipped with an air defense radar module to realize its military value, which gives it a higher expansion weight.

[0062] In the equipment development scenario with the purpose of product replacement, the synergy weight and expansion weight of the target set of equipment can be determined by referring to the situation of existing mature equipment.

[0063] In an optional embodiment of this specification, the synergy weight threshold may be an empirical value. The synergy weight threshold in the absence of competing products for the target device set is lower than the synergy weight threshold in the presence of competing products for the target device set. In the absence of competing products, consolidating the market should be the primary goal, with expanding the device's application areas being secondary.

[0064] S102: quantifying various performance indicators of the target set of equipment using preset quantification software to obtain quantified values.

[0065] The performance indicators of different target equipment sets vary and can be determined based on relevant national standards or user needs. For example, the performance indicators of CNC machine tools may include: machining accuracy (such as reset accuracy, geometric accuracy, contour accuracy, etc.), machining capability (such as spindle speed range, feed rate range, etc.), machining efficiency (such as machining speed, tool change speed, etc.), and other indicators, which are not detailed here.

[0066] Quantitative values ​​can be obtained by combining software and test values. In an optional embodiment of this specification, the quantification software includes at least one of the following: Apifox software (mostly used for CNC machine tools), KylinPET software, VERICUT software, Renishaw Ballbar Trace software, OpenFAST software (mostly used for wind turbines), and WindPRO software. The selection can be based on the target equipment. In addition, software in related technologies that can achieve the same or similar functions is also applicable to this specification, where conditions permit.

[0067] S104: When the collaborative weight is greater than a preset collaborative weight threshold, construct an available graph with the first indicator as a node.

[0068] The technical solutions in this specification examine the maturity evaluation method based on different situations of the size of the collaborative weight.

[0069] The graph used in this manual is the knowledge graph, which contains nodes and the weights between them. A knowledge graph is a structured semantic network used to represent and organize knowledge. It describes real-world concepts and objects through entities, relationships, and attributes. Depending on the application scenario and construction purpose.

[0070] As a knowledge representation and organization tool, knowledge graphs draw on and utilize some natural laws in their design and implementation to better simulate and understand real-world knowledge systems. The following are some natural law characteristics that may be involved in the construction and application of knowledge graphs:

[0071] 1. Hierarchy and classification (similar to biological taxonomy)

[0072] Natural laws: Organisms, substances, and phenomena in nature often have hierarchical structures and taxonomic relationships. For example, the taxonomic hierarchy of kingdom, phylum, class, order, family, genus, and species is based on the natural laws of morphology, genetics, and evolutionary relationships.

[0073] Applications in knowledge graphs: Knowledge graphs can better organize and manage knowledge by defining hierarchical and categorical relationships between entities. For example, in a product knowledge graph, products can be divided into different categories (such as electronics, clothing, and food), and each category can be further divided into more specific subcategories. This hierarchical structure facilitates rapid information location and retrieval and aligns with human cognitive habits.

[0074] 2. Causation (similar to the law of causality in physics and chemistry)

[0075] Natural Laws: Cause and effect is one of the most fundamental laws in nature. For example, in physics, the action of forces causes an object to change its state of motion, and in chemical reactions, the interaction between reactants produces new substances.

[0076] Applications in knowledge graphs: Knowledge graphs can explicitly represent causal relationships between entities. For example, in a medical knowledge graph, causal relationships such as "smoking causes lung cancer" and "high blood pressure increases the risk of heart disease" can be represented. This representation of causal relationships facilitates reasoning and prediction. For example, in intelligent question-answering systems, user questions can be answered based on causal relationships.

[0077] Connectedness (similar to interactions in an ecosystem)

[0078] Natural laws: There are extensive interactions and connections between things in nature. For example, in an ecosystem, plants, animals, and microorganisms interact through food chains and symbiotic relationships to maintain the balance of the ecosystem.

[0079] Applications in knowledge graphs: Knowledge graphs represent complex relationships between entities through graph structures, revealing the connections between different entities. For example, in social network knowledge graphs, relationships between people such as friendships, colleagues, and relatives can be represented; in enterprise knowledge graphs, relationships between companies such as partnerships and competition can be represented. This connection facilitates knowledge reasoning and recommendations. For example, in recommendation systems, relevant products can be recommended to users based on the relationships between users and products.

[0080] 4. Patterns and regularities (similar to the laws of physics and the periodic law of chemistry)

[0081] Natural laws: There are many recurring patterns and regularities in nature. For example, the laws of celestial motion (such as Kepler's laws) and the periodic patterns of chemical elements.

[0082] Applications in knowledge graphs: Knowledge graphs can use graph analysis techniques (such as graph neural networks) to uncover underlying patterns and regularities in data. For example, in financial knowledge graphs, cyclical patterns in financial markets can be discovered by analyzing transaction relationships between companies and market dynamics. In biomedical knowledge graphs, underlying mechanisms of disease can be discovered by analyzing interaction patterns between genes.

[0083] 5. Dynamics and evolution (similar to biological evolution and climate change)

[0084] Natural laws: Things in nature are changing dynamically. Biological evolution, climate change, etc. are all the results of dynamic evolution.

[0085] Applications in knowledge graphs: Knowledge graphs need to be continuously updated to reflect the dynamic changes in knowledge. For example, in a news knowledge graph, the development of events and changes in related entities need to be updated in real time; in a science and technology knowledge graph, new research results and technological developments need to be added promptly. This dynamic update mechanism, similar to the evolutionary process in nature, ensures that the knowledge graph reflects the latest state of knowledge.

[0086] 6. Similarity and Analogy (similar to analogical reasoning in physics)

[0087] Natural laws: Many phenomena in nature can be understood through analogical reasoning. For example, the nature of electric current can be better understood by analogizing the similarities between electric current and water flow.

[0088] Applications in knowledge graphs: Knowledge graphs can expand knowledge through similarity analysis and analogical reasoning. For example, in language knowledge graphs, new lexical relationships can be generated by analyzing the similarity of word meanings; in product knowledge graphs, new product designs can be designed by analogizing existing product features. This analogical reasoning facilitates knowledge innovation and expansion.

[0089] 7. Feedback mechanism (similar to the self-regulation of ecosystems)

[0090] Natural laws: Feedback mechanisms exist in ecosystems and many natural phenomena. For example, negative feedback mechanisms in ecosystems can maintain ecological balance.

[0091] Applications in knowledge graphs: Knowledge graphs can optimize their structure and content through user feedback and system self-learning mechanisms. For example, in intelligent question-answering systems, based on user satisfaction feedback, the system can adjust the knowledge representation and reasoning logic in the knowledge graph, thereby improving system performance.

[0092] Knowledge graphs draw on the characteristics of natural laws during their construction and application, such as hierarchy, causality, correlation, patterning, dynamics, similarity, and feedback mechanisms. These characteristics enable knowledge graphs to better simulate real-world knowledge systems, helping us organize, manage, and apply knowledge more efficiently. By simulating the characteristics of natural laws, they provide powerful support for knowledge representation and reasoning.

[0093] The first indicator in this specification is the performance indicator that has a connection relationship between the upstream and downstream (for example, the processing speed of the CNC machine tool will affect the connection relationship with the downstream, while the communication efficiency of its communication module will not affect the downstream); the connection degree between the first indicators is used as the weight (for example, the connection degree between the spindle speed range and the processing speed is low, while the connection degree between the tool change speed and the processing speed is high. The specific quantification method can be based on manual experience); and the weight when the first indicator corresponds to the second indicator is greater than the weight when it does not correspond to the second indicator (for example, the communication efficiency of the communication module is the second indicator, which has nothing to do with the downstream, but the tool change or switching of the processing mode of the CNC machine tool needs to be controlled by the signal of the master control or administrator, then the first indicator of the tool change speed corresponds to the second indicator of the communication efficiency of the communication module); the second indicator is a performance indicator other than the first indicator.

[0094] Because the primary focus is on synergy, the usable graph in this specification uses the first indicator as a node to highlight the efficiency of the usable graph's information transmission and enhance the clarity of the first graph's information expression. Furthermore, the weighting also considers the correspondence between the first indicator and the second indicator. Specifically, the degree of connectivity between operations that require the functional implementation of different indicators on a device is also reflected in the usable graph, enabling the graph to convey the device's stability indicators and quantify its robustness.

[0095] S106: Using a preset equipment maintenance system, simulating a situation when the target equipment fails, and determining a comprehensive equipment risk based on the available map.

[0096] In related technologies, systems capable of risk prediction based on knowledge graphs are applicable to the technical solutions described in this specification, where conditions permit. In an optional embodiment of this specification, the equipment maintenance system is KGPHMAgent. KGPHMAgent is an intelligent equipment maintenance system that integrates deep learning, knowledge graph construction, and natural language processing technologies. It utilizes knowledge graphs to implement functions such as equipment fault diagnosis, maintenance decision-making and monitoring, and risk prediction. Based on the equipment's knowledge graph, it can assess the robustness of the equipment through functions such as fault diagnosis, failure attribution analysis, and intelligent question-and-answering. You can choose the appropriate tool based on your specific needs.

[0097] The comprehensive equipment risk in this specification is a comprehensive assessment of the risks that the target equipment may face when it is put into online use. This risk can be the highest or average value of the risks when implementing different functions, and can be obtained through system debugging.

[0098] S108: Determine the maturity score of the target set of equipment.

[0099] The maturity score in this specification quantifies the technical maturity of the first set of equipment. The higher the score, the more mature the target equipment.

[0100] The maturity score is negatively correlated with the overall equipment risk. When the overall equipment risk is less than a preset risk threshold, the maturity score is also positively correlated with the degree of orthogonality of the second indicator (the degree of orthogonality between the second indicator and the first indicator, independent of other indicators). A higher degree of orthogonality indicates that the functionality achieved based on the second indicator is less susceptible to other factors. This means that the relationship between the first and second indicators is unidirectional: the first indicator corresponds to the second indicator, but the second indicator does not necessarily correspond to the first. Continuing with the CNC machine tool example, the target equipment's monitoring of other equipment will not be significantly affected by the first indicator, so the network communication efficiency indicator has a higher degree of orthogonality.

[0101] In an optional embodiment of the present specification, if the synergy weight is greater than the synergy weight threshold, and the comprehensive equipment risk is less than the risk threshold, the maturity score is also negatively correlated with the ratio of the synergy weight to the expansion weight, so as to pursue the beneficial effect of the expression of expansion while fully reflecting the synergy. If the ratio of the synergy weight to the expansion weight is small, it indicates that the target set of equipment has achieved a cross-domain technological breakthrough while serving its own production line. Even under more extreme conditions, the target set of equipment can play a role in other fields it has expanded to after being separated from the production line, and has high development potential.

[0102] The present application provides a method for constructing a technology maturity assessment model for first-of-its-kind equipment based on a knowledge graph, which quantifies the degree of the first-of-its-kind equipment's connection with upstream and downstream equipment to obtain a synergy weight; it also quantifies the independent operation capability of the first-of-its-kind equipment to obtain an expansion weight, and uses these two weights to measure the role of the first-of-its-kind equipment in the production process. Afterwards, combined with the performance indicators of the first-of-its-kind equipment, a preset equipment maintenance system is used to simulate possible failures that may occur during its operation to measure its technology maturity and achieve quantification of technology maturity. In addition, the present application also uses the technical means of the knowledge graph to achieve a multi-dimensional characterization of the characteristics of the first-of-its-kind equipment, which is conducive to obtaining more accurate assessment results. On the one hand, the method in this specification achieves the assessment of the technology maturity of the first-of-its-kind equipment through technical means of digital calculation or data processing specifically suitable for specific applications. On the other hand, the maturity score obtained combines the synergy and extensibility of the equipment, and can be comprehensively quantified from the two aspects of traditional equipment replacement and function expansion, thereby improving the guidance of the maturity score to equipment manufacturers.

[0103] In an optional embodiment of the present specification, when the synergy weight is greater than a preset synergy weight threshold, if the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the synergy weight (that is, at a macro level, the target set of equipment has a higher degree of correlation with the entire production line and a higher degree of synergy); if the comprehensive equipment risk is not less than the risk threshold, the maturity score is also negatively correlated with the synergy weight (the higher the degree of synergy with the production line of the target set of equipment, the lower the maturity score in the case of higher risk, so as to highlight its inadaptability on the production line and achieve the purpose of highlighting its characteristics).

[0104] If the synergy weight is greater than the preset synergy weight threshold and the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the expansion weight. A synergy weight greater than the synergy weight threshold indicates that the target equipment is closely related to the production line. This usage scenario often restricts its cross-domain expansion capabilities. If it can still demonstrate relatively outstanding cross-domain capabilities, it should also be characterized.

[0105] In addition, when the collaborative weight is greater than the preset collaborative weight threshold, the weight of the available graph is also negatively correlated with the redundancy of the node pair to which it belongs; (that is, if there is redundancy, the weight is low) If the median or mean of the weights contained in the available graph is greater than the preset graph weight threshold, then the maturity score is also negatively correlated with the collaborative weight (indicating insufficient redundancy backup and high risk). The graph weight threshold can be an empirical value. When determining the maturity, after completing the judgment on the collaborative weight, the judgment based on the graph weight threshold can be performed first. If it is not greater than the graph weight threshold, and the collaborative weight is greater than the preset collaborative weight threshold, the comprehensive equipment risk is less than the risk threshold, then the maturity score is positively correlated with the collaborative weight.

[0106] Furthermore, when the collaborative weight is not greater than the collaborative weight threshold, the performance indicators are divided into several indicator sets, so that the indicator sets correspond one-to-one to the outputs of the target set of equipment (the output is the result of the realization of the function of the target set of equipment, such as a wind turbine, electric energy is an output, the detected radar data is an output, and the detected meteorological data is an output); there may be intersections between different indicator sets (for example, the indicator of power supply stability, the above three outputs may be involved).

[0107] For each of the indicator sets, an intermediate graph is constructed respectively; the intermediate graph uses the performance indicators in the corresponding indicator set as nodes, and the completion degree of the output corresponding to the indicator set of the target set of equipment when two adjacent nodes are abnormal (the completion degree can be used to characterize robustness. For example, the two adjacent indicators are the anemometer and the maximum yaw angle of the blade. When these two indicators are abnormal, the wind turbine can still have 80% of the power output capacity, and the completion degree is 0.8) is used as the weight between the two adjacent nodes.

[0108] The contribution of the output to the function of the target set of equipment is weighted by adding the weight of the corresponding intermediate map according to the contribution of the output to the function of the target set of equipment (the contribution can be obtained based on experience. The contribution of the basic function of the equipment is more mature than that of the extended function. The contribution of the underlying function is higher than that of other functions. For example, a wind turbine can at least generate electricity, and the power source of the radar is mainly wind power, so the contribution of the wind power output is 100%. The contribution of the radar output is lower. Different contributions are independent of each other) to obtain a usable map (if there are multiple outputs, there will be multiple usable maps).

[0109] The equipment maintenance system is used to simulate a situation in which a failure occurs in the target equipment set, and based on the available graph, a comprehensive equipment risk is determined. A maturity score of the target equipment set is determined; the maturity score is negatively correlated with the comprehensive equipment risk.

[0110] In an optional embodiment of the present specification, when the synergy weight is not greater than the synergy weight threshold, a first output is determined from the outputs; the first output has the lowest degree of comprehensive orthogonality with all other outputs. The first output is the least independent and depends on other outputs, such as radar power relying on wind turbine power generation. The nodes of the intermediate graph of the first output have the highest degree of overlap with the intermediate graphs of the other outputs. The comprehensive degree of orthogonality can be the sum of the individual orthogonality degrees.

[0111] If the contribution of the first output is less than a preset contribution threshold (the contribution threshold can be obtained based on experience and, optionally, is negatively correlated with the market value of devices capable of achieving the first output), and the maturity score is greater than the maturity score obtained when the device risk obtained from the corresponding available map (device risk can be obtained based on relevant technologies and is not a comprehensive device risk) is less than a preset output risk threshold (the output risk threshold can be an empirical value and is positively correlated with the output risk of competing products in the market or, in the absence of competing products, is positively correlated with the maximum risk a user can bear), then the maturity score is greater than the maturity score obtained when the device risk obtained from the corresponding available map is not less than the output risk threshold. For example, radars may have redundant backup power supplies and guaranteed performance, indicating that this first-of-its-kind device has broader market prospects.

[0112] In a further optional embodiment, if the synergy weight is not greater than the synergy weight threshold, and when the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the number of indicator sets (indicating strong market adaptability to different fields) and the expansion weight.

[0113] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for its services.

[0114] The processor, network interface, and memory can be interconnected via an internal bus, such as an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be categorized as an address bus, a data bus, a control bus, and the like. For ease of presentation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0115] The memory is used to store programs. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0116] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming, at a logical level, a device for constructing a first-of-its-kind equipment technology maturity assessment model based on a knowledge graph. The processor executes the program stored in the memory and is specifically configured to execute any of the aforementioned methods for constructing a first-of-its-kind equipment technology maturity assessment model based on a knowledge graph.

[0117] The above application Figure 1The knowledge graph-based method for constructing a first-of-its-kind equipment technology maturity assessment model disclosed in the illustrated embodiments can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be performed by hardware integrated logic circuits or software instructions within the processor. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0118] The electronic device may also perform Figure 1 A method for constructing a technology maturity assessment model for the first set of equipment based on knowledge graph is proposed, and it is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0119] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, they execute any of the aforementioned knowledge graph-based methods for constructing a first-of-its-kind equipment technology maturity assessment model.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0125] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0126] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0127] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for constructing a first-of-its-kind equipment technology maturity assessment model based on knowledge graph, characterized in that: The method comprises: Determine the synergy weight and expansion weight of the target set of equipment; the synergy weight represents the degree of connection between the target set of equipment and its upstream and downstream; the expansion weight represents the degree of functional realization of the target set of equipment when it is independent of its upstream and downstream; Using preset quantification software, quantify various performance indicators of the target set of equipment to obtain quantitative values; When the synergy weight is greater than a preset synergy weight threshold, a usable graph is constructed with the first indicator as a node; the first indicator is one of the performance indicators that has a connection relationship between upstream and downstream; the degree of connection between the first indicators is used as the weight; and the weight when the first indicator corresponds to the second indicator is greater than the weight when the first indicator does not correspond to the second indicator; the second indicator is a performance indicator other than the first indicator; Using a preset equipment maintenance system, simulating a situation when the target equipment fails, and determining a comprehensive equipment risk based on the available map; Determine a maturity score of the target set of equipment; the maturity score is negatively correlated with the comprehensive equipment risk, and when the comprehensive equipment risk is less than a preset risk threshold, the maturity score is also positively correlated with the orthogonality degree of the second indicator.

2. The method according to claim 1, wherein: The method further comprises: When the synergy weight is greater than the synergy weight threshold, if the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the synergy weight; if the comprehensive equipment risk is not less than the risk threshold, the maturity score is also negatively correlated with the synergy weight.

3. The method according to claim 1, wherein: The method further comprises: In the case where the synergy weight is greater than the synergy weight threshold, if the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the expansion weight.

4. The method according to claim 1, wherein: The method further comprises: In the case where the collaborative weight is greater than the collaborative weight threshold, the weight of the available graph is also negatively correlated with the redundancy level of the node pair to which it belongs; If the median or mean of the weights contained in the available graph is greater than a preset graph weight threshold, the maturity score is also negatively correlated with the synergy weight.

5. The method according to claim 1, wherein: The method further comprises: When the collaborative weight is not greater than the collaborative weight threshold, the performance indicators are divided into a plurality of indicator sets, so that the indicator sets correspond to the outputs of the target set of equipment one by one; and there are intersections between different indicator sets; For each indicator set, an intermediate graph is constructed; the intermediate graph uses the performance indicators in the corresponding indicator set as nodes, and the completion degree of the output corresponding to the indicator set by the target set of equipment when two adjacent nodes are abnormal is used as the weight between the two adjacent nodes; The contribution of the output to the function of the target set of equipment is weighted as the weight of the corresponding intermediate map to obtain an available map; Using the equipment maintenance system, simulating a situation when the target equipment fails, and determining a comprehensive equipment risk based on the available map; A maturity score of the target set of equipment is determined; the maturity score is negatively correlated with the comprehensive equipment risk.

6. The method according to claim 5, wherein: The method further comprises: Determining a first output from the outputs; the first output having the smallest degree of comprehensive orthogonality with the other outputs; When the contribution degree of the first output is less than a preset contribution threshold, the maturity score obtained when the device risk obtained based on its corresponding available map is less than the preset output risk threshold is greater than the maturity score obtained when the device risk obtained based on its corresponding available map is not less than the output risk threshold.

7. The method according to claim 6, wherein: The method further comprises: The contribution threshold is negatively correlated with the market value of a device capable of achieving the first output in the market.

8. The method according to claim 6, wherein: The method further comprises: If the synergy weight is not greater than the synergy weight threshold, and the comprehensive equipment risk is less than the risk threshold, the maturity score is also positively correlated with the number of the indicator sets and the expansion weight.

9. The method according to claim 1, wherein: The method further comprises at least one of the following: The equipment maintenance system is KGPHMAgent; The quantification software includes at least one of the following: Apifox software, kylinPET software, VERICUT software, Renishaw Ballbar Trace software, OpenFAST software, and WindPRO software; The collaboration weight threshold when there is no competitor of the target set of equipment is smaller than the collaboration weight threshold when there is a competitor of the target set of equipment.

10. The method according to claim 1, wherein: The method further comprises: If the synergy weight is greater than the synergy weight threshold and the comprehensive equipment risk is less than the risk threshold, the maturity score is also negatively correlated with the ratio of the synergy weight to the expansion weight.

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