Health management method and device for engineering mechanical equipment

Abstract

The invention relates to a health management method and device for engineering mechanical equipment. The engineering mechanical equipment comprises the system, and the system comprises parts needing to be monitored. The method comprises the following steps: acquiring a monitoring index associated with a part from a pre-constructed index library; acquiring monitoring data related to the monitoring indexes; obtaining maintenance knowledge which is associated with the parts and comprises the monitoring indexes from a pre-constructed maintenance knowledge base, wherein the maintenance knowledge comprises maintenance measures for the parts; determining the risk level of the part based on the monitoring data and a preset risk level threshold; in response to the fact that the risk level of the part is within the maintenance risk level range, a maintenance strategy for the part is determined based on the maintenance measures for the part; and pushing the maintenance strategy for the part so as to execute the maintenance measure for the part.

Description

Technical Field

[0001] This disclosure relates to the field of engineering machinery and equipment technology, and more specifically, to a health management method for engineering machinery and equipment, a health management device for engineering machinery and equipment, an electronic device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] Construction machinery and equipment often experience malfunctions during operation. Malfunctions are a direct manifestation of these malfunctions deviating from the functional baseline, reflecting whether the equipment can achieve its intended design functions. To ensure the safe operation and lifespan of construction machinery and equipment, it is necessary to monitor them to provide fault information when malfunctions occur. This fault information provides engineers with a basis for decision-making regarding the maintenance, repair, and replacement of parts. Summary of the Invention

[0003] A brief overview of this disclosure is given below to provide a basic understanding of some aspects of it. However, it should be understood that this overview is not an exhaustive summary of this disclosure. It is not intended to identify key or essential parts of this disclosure, nor is it intended to limit the scope of this disclosure. Its purpose is merely to present certain concepts of this disclosure in a simplified form as a prelude to the more detailed description that follows.

[0004] In related technologies, a reactive maintenance approach is commonly used for troubleshooting construction machinery. This involves engineers relying on experience to inspect and repair the equipment after a malfunction occurs. However, this approach lacks the capability for proactive monitoring and prevention. Faults are only detected and repaired after they happen, resulting in significant delays and hindering preventative measures against secondary damage caused by escalating problems. Furthermore, relying on manual judgment when performance indicators of construction machinery malfunction makes it difficult to quickly and accurately pinpoint the anomaly and its cause. This leads to prolonged downtime, impacting equipment utilization and operational continuity.

[0005] In order to overcome the above-mentioned problems in the related technologies, the present disclosure proposes the following solutions to achieve more reliable health management of engineering machinery and equipment.

[0006] According to a first aspect of this disclosure, a health management method for construction machinery equipment is provided, wherein the construction machinery equipment includes a system, and the system includes components that need to be monitored. The method includes: acquiring monitoring indicators associated with the components from a pre-built indicator library; acquiring monitoring data associated with the monitoring indicators; acquiring maintenance knowledge associated with the components and including the monitoring indicators from a pre-built maintenance knowledge base, the maintenance knowledge including maintenance measures for the components; determining a risk level of the components based on the monitoring data and a preset risk level threshold; in response to the risk level of the components being within the maintenance risk level range, determining a maintenance strategy for the components based on the maintenance measures for the components; and pushing the maintenance strategy for the components to execute the maintenance measures for the components.

[0007] According to a second aspect of this disclosure, a health management device for construction machinery equipment is provided, comprising: a module configured to perform the health management method for construction machinery equipment according to a first aspect of this disclosure.

[0008] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-executable instructions, which, when executed by the processor, cause the processor to perform the health management method for engineering machinery equipment according to a first aspect of this disclosure.

[0009] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having computer-executable instructions stored thereon, which, when executed by a computer, cause the computer to perform the health management method for engineering machinery equipment according to a first aspect of this disclosure.

[0010] According to a fifth aspect of this disclosure, a computer program product is provided, the computer program product including instructions that, when executed by a processor, implement the health management method for engineering machinery equipment according to a first aspect of this disclosure.

[0011] According to a sixth aspect of this disclosure, an engineering machinery device is provided, including a health management device for engineering machinery device according to a second aspect of this disclosure or an electronic device according to a third aspect of this disclosure.

[0012] The health management method for construction machinery provided by the embodiments of this disclosure acquires monitoring indicators that can reflect the performance of components of construction machinery that need to be monitored based on a pre-built indicator library, and acquires relevant maintenance knowledge based on a pre-built maintenance knowledge base. This allows for the determination of the risk level of components based on the monitoring indicator data, and, when the risk level indicates that a component needs maintenance, the determination of maintenance strategies for the component based on the relevant maintenance knowledge. This enables proactive monitoring of construction machinery to prevent failures, and, when a component needs maintenance, the determination of maintenance strategies quickly and accurately based on pre-known maintenance knowledge, thereby achieving more reliable health management of construction machinery. Attached Figure Description

[0013] The foregoing and other features and advantages of this disclosure will become clear from the following description of embodiments illustrated in conjunction with the accompanying drawings. The drawings, incorporated herein and forming a part of the specification, are further used to explain the principles of this disclosure and to enable those skilled in the art to make and use it. Wherein:

[0014] Figure 1 A flow chart of a health management method for engineering machinery equipment according to some embodiments of the present disclosure is shown;

[0015] Figure 2 A schematic structural diagram of a complete machine library, a system library, and a component library according to some embodiments of the present disclosure is shown;

[0016] Figure 3 Schematic structural diagrams of engineering machinery equipment according to some embodiments of the present disclosure are shown;

[0017] Figure 4 A schematic block diagram of an electronic device according to some embodiments of the present disclosure is shown;

[0018] Figure 5 A schematic block diagram of a computer system on which embodiments of the present disclosure may be implemented is shown;

[0019] Figure 6 A non-limiting illustrative example architecture of an engineering machinery device according to some embodiments of the present disclosure is shown.

[0020] Note that in the embodiments described below, the same reference numerals are sometimes used across different figures to denote the same parts or parts with the same function, and repeated descriptions are omitted. In some cases, similar reference numerals and letters are used to denote similar items, so once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0021] For ease of understanding, the positions, dimensions, and extents of the structures shown in the accompanying drawings and other materials may not represent actual positions, dimensions, and extents. Therefore, this disclosure is not limited to the positions, dimensions, and extents disclosed in the accompanying drawings and other materials. Detailed Implementation

[0022] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0023] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this disclosure or its application or use. That is, the structures and methods herein are shown in an exemplary manner to illustrate different embodiments of the structures and methods in this disclosure. However, those skilled in the art will understand that they merely illustrate exemplary ways that can be used to implement this disclosure, and not exhaustive ways. Furthermore, the drawings are not necessarily drawn to scale, and some features may be enlarged to show details of specific components.

[0024] In addition, techniques, methods and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods and equipment should be considered part of the specification.

[0025] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0026] Figure 1 A flowchart is shown of a health management method 100 (hereinafter referred to as "method 100") for construction machinery equipment according to some embodiments of the present disclosure. Figure 1 As shown, method 100 includes steps S102 to S112, wherein the engineering machinery equipment includes a system, and the system includes components that need to be monitored. Unless otherwise stated, the term "components" mentioned in method 100 refers to the components in the system that need to be monitored.

[0027] In step S102, monitoring indicators associated with the components are obtained from a pre-built indicator library.

[0028] In some examples, the indicator library can be obtained through testability analysis of components. The relevant monitoring indicators for the components that need to be monitored can be determined from the indicator library. For example, for a piston pump that needs to be monitored, the relevant monitoring indicators may include pressure, flow rate, vibration acceleration, running time, temperature, etc.

[0029] In step S104, monitoring data associated with the monitoring indicators are acquired.

[0030] In some examples, monitoring data can be obtained through sensors deployed on engineering machinery. For instance, sensors are used to monitor real-time data of relevant monitoring indicators.

[0031] In step S106, maintenance knowledge associated with the component and including the monitoring indicator is obtained from a pre-built maintenance knowledge base. This maintenance knowledge includes maintenance measures for the component.

[0032] In some examples, a maintenance knowledge base may include at least one piece of maintenance knowledge associated with a component. This maintenance knowledge may include information such as monitoring indicators and maintenance measures related to the component. For example, taking a plunger pump as an example, maintenance knowledge related to the plunger pump may include monitoring indicators such as "pressure," "flow rate," "vibration acceleration," "running time," and "temperature," as well as maintenance measures such as "reducing the load and checking if the relief valve is stuck." Through the maintenance knowledge base, appropriate maintenance measures for the components can be obtained.

[0033] In step S108, the risk level of the component is determined based on the monitoring data and the preset risk level threshold.

[0034] In some examples, a score reflecting the risk level of a component can be determined based on monitoring data. Preset risk level thresholds are set according to the specific risk knowledge of the component, and different risk levels can be quantified into multiple risk level thresholds. Therefore, after obtaining a score based on the monitoring data, the risk level of the component can be determined by combining it with the preset risk level thresholds.

[0035] In step S110, in response to the fact that the risk level of the component is within the maintenance risk level range, a maintenance strategy for the component is determined based on the maintenance measures for the component.

[0036] In step S112, a maintenance strategy for the component is pushed out in order to execute maintenance measures for the component.

[0037] In some examples, maintenance strategies can be used to guide engineers or automated equipment to perform maintenance measures to manage the health of components.

[0038] In some embodiments, method 100 may further include: constructing a complete machine library, a system library, and a component library. Here, the complete machine library may include multiple families of engineering machinery equipment, and each family of engineering machinery equipment may include at least one type of engineering machinery equipment; the system library may include multiple systems; the component library may include multiple types of components, each type of component may include at least one component family, and each component family may include at least one component.

[0039] refer to Figure 2 It shows a schematic structural diagram of a complete machine library 200, a system library 202, and a component library 204 according to some embodiments of the present disclosure. Figure 2 As shown, the component library, system library, and complete machine library can each have a classification tree structure. Specifically, in the complete machine library 200, P-Complete Machine (P is the complete machine level code) includes multiple engineering machinery equipment families, such as 1-Double-Axle Rigid Mining Car and 2-Articulated Dump Truck; further, 1-Double-Axle Rigid Mining Car can include various engineering machinery equipment, such as 1-90-ton Internal Combustion National III Mechanical Transmission Product Platform. In the system library 202, S-System (S is the system level code) includes multiple systems, such as 1-Braking System and 2-Centralized Lubrication System. In the component library 204, C-Component (C is the component level code) includes multiple types of components, such as 1-Hydraulic and 2-Electrical; further, 1-Hydraulic can include multiple component families, such as 1-Hydraulic Pump and 2-Hydraulic Valve; further, 1-Hydraulic Pump can include one type of component, namely 1-Piston Pump. At this time, the classification tree code of the piston pump can include the codes of all its parent level components, that is, the classification tree code of the piston pump is C111.

[0040] It's important to note that, taking plunger pumps as an example, even at the lowest sub-level in the parts library, "plunger pump" can refer to a collection of multiple plunger pumps of the same type but with different specific performance parameters. For instance, as shown in Table 1, the performance parameter types for plunger pumps can include pressure, operating mode, rotation direction, and control equipment, and some performance parameter types can include multiple specific performance parameters. For example, the rotation direction of a plunger pump can include clockwise and counterclockwise, and the control equipment can include constant pressure control and constant flow control.

[0041] Table 1

[0042]

[0043] As a non-restrictive example, when actually constructing engineering machinery equipment, the required components, systems, and equipment can be selected from pre-built component libraries, system libraries, and complete machine libraries, and the correspondence between each component and each system can be determined. Thus, engineering machinery equipment can be divided into three levels: complete machine, system, and component. The complete machine is the parent level of the system, and the system is the parent level of the component. The components, systems, and equipment selected from the component libraries, system libraries, and complete machine libraries all have classification tree codes. Furthermore, when actually constructing engineering machinery equipment, the selected components are actual components with defined specific performance parameters. For example, a piston pump coded C111-1-1-1-1 can be selected from component library 200; this means a piston pump with nominal pressure, open-loop operation, clockwise rotation, and constant pressure control.

[0044] refer to Figure 3 This illustrates a schematic structural diagram of an engineering machinery device 300 according to some embodiments of the present disclosure. For example... Figure 3 As shown, the engineering machinery equipment 300 may include: a whole machine level, a 90-ton internal combustion National III mechanical transmission product platform with a whole machine code; a system level, such as a braking system with a first system code and a centralized lubrication system with a second system code; and a component level, such as a plunger pump with a component code C111-1-1-1-1-1, wherein C111-1-1-1-1 also carries fault knowledge code, monitoring index code, maintenance knowledge code and repair knowledge code, so as to find the corresponding information from the various information databases (i.e., index database, maintenance knowledge database, fault knowledge database and repair knowledge database) described below.

[0045] In some embodiments, method 100 may further include: constructing an indicator library and a maintenance knowledge base based on a complete machine library, a system library, and a parts library. Here, the indicator library may include multiple sets of monitoring indicators, each set of monitoring indicators may include at least one monitoring indicator associated with a corresponding one of various engineering machinery equipment in the complete machine library, various systems in the system library, and various parts in the parts library. The maintenance knowledge base may include multiple pieces of maintenance knowledge, each piece of maintenance knowledge may include monitoring indicators, maintenance measures, and possible faults of a corresponding one of various engineering machinery equipment in the complete machine library, various systems in the system library, and various parts in the parts library, wherein the monitoring indicators in the maintenance knowledge are obtained from the indicator library. It is understood that "a corresponding one of various engineering machinery equipment, various systems, and various parts" refers to a single piece of engineering machinery equipment, a single system, or a single part.

[0046] As a non-restrictive example, a set of monitoring indicators associated with P11 (i.e., the 90-ton internal combustion National III mechanical transmission product platform) is shown in Table 2 below.

[0047] Table 2

[0048]

[0049] The index codes for this group of indicators can be P11-111. Indicators for engineering machinery, equipment, systems, or components can be obtained through testability analysis. Furthermore, various indicators obtained through testability analysis can be selected as monitoring indicators in the indicator library based on the actual needs of health management.

[0050] It is understandable that each set of monitoring indicators in the indicator library can also include more indicator information, such as indicator units, indicator design target values ​​(including maximum, minimum, optimal, historical mean, standard deviation, etc.) and other baseline information.

[0051] As a non-restrictive example, the maintenance knowledge associated with C111 (i.e., the plunger pump) is shown in Table 3 below.

[0052] Table 3

[0053]

[0054] As shown in Table 3, maintenance knowledge can include maintenance objects, monitoring indicators, maintenance measures, and associated faults. The maintenance code for this maintenance knowledge entry can be C111-1&2-1. Maintenance knowledge can also include more maintenance information, such as the type of prediction model used to predict faults and instructions for operating maintenance measures.

[0055] After extracting monitoring indicators related to C111 from the indicator library, these indicators can be grouped based on relevant experience and knowledge. This allows indicators that can be used to predict the same fault to be included in the same maintenance knowledge entry. For example, pressure, vibration acceleration, flow rate, temperature, and operating time can be included in one maintenance knowledge entry. Furthermore, mutually influential monitoring indicators can be further subdivided into groups. For instance, since vibration acceleration, flow rate, and temperature all affect the plunger pump pressure, pressure, vibration acceleration, flow rate, and temperature can be grouped together, while operating time can be grouped separately. Additionally, this maintenance knowledge entry can include maintenance measures to provide appropriate maintenance when C111 requires maintenance.

[0056] In some embodiments, method 100 may further include: constructing a fault knowledge base and a maintenance knowledge base based on a complete machine library, a system library, and a component library. Here, the fault knowledge base includes multiple fault knowledge entries, each including a fault phenomenon associated with a corresponding one of various engineering machinery equipment in the complete machine library, various systems in the system library, and various components in the component library, and the fault corresponding to that fault phenomenon. The maintenance knowledge base includes multiple maintenance knowledge entries, each including a possible fault associated with a corresponding one of various engineering machinery equipment in the complete machine library, various systems in the system library, and various components in the component library, and maintenance measures for handling that fault.

[0057] As a non-restrictive example, the fault knowledge associated with C111 is shown in Table 4 below.

[0058] Table 4

[0059]

[0060] As shown in Table 4, fault knowledge can include the fault object, fault name, fault phenomenon, typical scenario, and fault cause. The code for this fault knowledge entry is C111-1-01-01. Therefore, fault knowledge associated with the fault object can be found from the fault knowledge base based on the fault object's classification tree code. Fault knowledge can also include information such as the fault's parent category and sub-category.

[0061] The maintenance information associated with C111 is shown in Table 5 below.

[0062] Table 5

[0063]

[0064] As shown in Table 5, maintenance knowledge can include information such as maintenance object, maintenance method, maintenance operation method, maintenance measures, and associated faults. The code for this maintenance knowledge is C111-1-1. Based on the classification tree code of the maintenance object, the maintenance measures and corresponding faults for that maintenance object can be found in the maintenance knowledge base.

[0065] Therefore, by pre-constructing complete machine libraries, system libraries, component libraries, as well as fault knowledge bases, indicator libraries, maintenance knowledge bases, and repair knowledge bases, relevant experience and knowledge related to various engineering machinery equipment can be standardized and structured, enabling the reuse of relevant experience and knowledge for engineering machinery equipment, and supporting platform-based integration and group management applications of engineering machinery equipment.

[0066] Furthermore, by pre-constructing complete machine libraries, system libraries, and component libraries, each component at each level of the actual constructed construction machinery equipment is coded. Moreover, by pre-constructing fault knowledge bases, indicator libraries, maintenance knowledge bases, and repair knowledge bases, information associated with each component can be quickly identified from these information bases. This allows for the rapid and accurate determination of monitoring indicators, corresponding maintenance measures, fault information, and repair measures for components, and even systems and complete machines used in construction machinery equipment. It eliminates the need for engineers to manually analyze monitoring data and determine maintenance or repair measures after a fault occurs or an anomaly appears, thereby improving the efficiency, comprehensiveness, and reliability of health management for construction machinery equipment and achieving more reliable health management for construction machinery equipment.

[0067] In some embodiments, the system may include multiple components (including components that need to be monitored and components that do not need to be monitored). In this embodiment, method 100 may further include: in response to an indicator library including monitoring indicators associated with at least one of the multiple components, and a maintenance knowledge base including maintenance knowledge associated with the at least one component, determining that the at least one component is a component that needs to be monitored.

[0068] It's understandable that construction machinery comprises numerous components. Monitoring and preventing faults in all components would place a significant burden on the equipment. Therefore, by using an indicator library and maintenance knowledge base, it's possible to select which components to monitor, enabling dynamic sensing of critical parts and reducing the burden and operating costs of the construction machinery. For example, when working with actual construction machinery, engineers can pre-determine which components require monitoring and which do not based on their experience. This allows information related to unnecessary components in the indicator library and maintenance knowledge base to be excluded or disabled. Consequently, during actual monitoring, only monitoring indicators and maintenance knowledge relevant to the components requiring monitoring can be retrieved from these libraries, ensuring that monitoring is focused solely on those critical parts.

[0069] In some embodiments, the monitoring indicators may include multiple sets of monitoring indicators, and the monitoring data may include multiple sets of monitoring data. Determining the risk level of a component based on the monitoring data and a preset risk level threshold may include: determining a score for a set of monitoring indicators based on a set of monitoring data related to each set of monitoring indicators in the multiple sets of monitoring data; determining the risk level indicated by each set of monitoring indicators based on the score of each set of monitoring indicators and the preset risk level threshold; and determining the risk level of the component based on the risk level indicated by each set of monitoring indicators.

[0070] In some examples, when the monitoring indicator has clearly defined upper and lower limits, the min-max normalization formula can be used, i.e. Where A represents the score of the monitoring indicator, x represents the real-time data of the monitoring indicator, Xmin represents the upper limit of the monitoring indicator, and Xmin represents the lower limit of the monitoring indicator.

[0071] In some examples, when the monitored indicators conform to a normal distribution and have historical mean and historical standard deviation, the Z-Score normalization formula can be used, i.e. μ represents the historical mean of the monitoring indicator, and σ represents the historical standard deviation of the monitoring indicator.

[0072] In some embodiments, at least one set of monitoring indicators in a plurality of monitoring indicators includes a basic monitoring indicator and a weighted monitoring indicator related to the basic monitoring indicator. For each set of monitoring indicators in the plurality of monitoring indicators, the following operations are performed: determining a score for the basic monitoring indicator based on the basic monitoring data corresponding to the basic monitoring indicator in a set of monitoring data related to the set of monitoring indicators in the plurality of monitoring data; determining a score for the weighted monitoring indicator based on the weighted monitoring data corresponding to the weighted monitoring indicator in the set of monitoring data; and determining a score for the set of monitoring indicators based on the scores of the basic monitoring indicator and the weighted monitoring indicator.

[0073] In some examples, maintenance knowledge can indicate the basic monitoring indicators and the weighted monitoring indicators associated with the basic monitoring indicators in one set of monitoring indicators.

[0074] In some examples, the scores for this set of monitoring indicators can also be referred to as the revised scores for the base monitoring indicators of that set. (Revised score) Where A0 is the score of the basic monitoring indicator, i is a positive integer, Ai is the score of the weighted monitoring indicator i, and αi is the weight of the weighted monitoring indicator i. The scores of the basic monitoring indicator and the enhanced monitoring indicator can be determined based on the normalization formula in the previous example.

[0075] In some examples, multiple sets of monitoring indicators include two sets of monitoring indicators. Determining the risk level of a component based on monitoring data and preset risk level thresholds may include: determining the risk level of a component based on the risk levels indicated by the two sets of monitoring indicators and a preset risk level matrix.

[0076] As a non-restrictive example, the preset risk level thresholds can be shown in Table 6 below.

[0077] Table 6

[0078]

[0079] The preset risk level matrix can be shown in Table 7 below.

[0080] Table 7

[0081]

[0082] As a non-restrictive example, C111 and related maintenance knowledge C111-1&2-1 are used as examples, which include a first set of monitoring indicators (pressure, vibration acceleration, flow rate and temperature) and a second set of monitoring indicators (running time). Among the first set of monitoring indicators, pressure is the basic monitoring indicator, and vibration acceleration, flow rate and temperature are weighted monitoring indicators of pressure.

[0083] Assume that in the first set of monitoring indicators, the weighted monitoring indicator 1 (vibration acceleration) has a weight of α1=0.1, the weighted monitoring indicator 2 (flow rate) has a weight of α2=0.2, and the weighted monitoring indicator 3 (temperature) has a weight of α3=0.1. Based on the acquired real-time data, A1=0.6, A2=0.5, and A3=0.5. The real-time data for the basic monitoring indicator 1 (pressure) is x=0.18, and the corresponding historical data mean μ=0.2. The standard deviation is set to σ=0.05, and the Z-Score normalization formula is used, i.e. So, what is the corrected stress score? If the preset risk level thresholds are P1=10, P2=30, P3=50, and P4=90, then the risk level indicated by the first set of monitoring indicators is III. It can be understood that the preset risk level thresholds can be set to the same or different values ​​based on different monitoring indicators.

[0084] The real-time data for the running time in the second set of monitoring indicators is 800 hours, with an upper limit of 1000 hours and a lower limit of 0 hours. Min-max normalization is used, which is also the scoring method for the running time data. If the preset risk level thresholds are P1=10, P2=40, P3=70, and P4=90, then the risk level indicated by the second set of monitoring indicators is IV.

[0085] Therefore, based on the risk level III indicated by the first set of monitoring indicators, the risk level IV indicated by the second set of monitoring indicators, and the preset risk level matrix shown in Table 7, the risk level of C111 can be determined to be medium risk.

[0086] It is understandable that for basic monitoring indicators, directly measuring the real-time data may not fully reflect the actual performance of the corresponding components, as this real-time data contains measurement errors and fails to show the trend of performance changes. Therefore, by incorporating weighted monitoring indicators that indirectly reflect the performance of the corresponding components and are related to the basic monitoring indicator, the effectiveness and accuracy of fault prediction can be effectively improved. For example, regarding pressure, the real-time pressure value of a plunger pump may not be very high at the current moment, but if the real-time data of vibration acceleration, flow rate, and temperature, which affect pressure, are high, it indicates that the plunger pump pressure is trending upwards. Therefore, the risk level indicated by the monitoring indicators of pressure, vibration acceleration, flow rate, and temperature will be higher than the risk level indicated by pressure alone. Furthermore, for plunger pump internal leakage faults, not only excessive pressure but also excessive operating time can cause internal leakage. Therefore, predicting plunger pump faults based on monitoring indicators including pressure and operating time is advantageous and comprehensive.

[0087] If the risk level of a component is determined to be within the maintenance risk level range, a maintenance strategy for the component can be determined based on the maintenance measures used for the component.

[0088] In some embodiments, determining a maintenance strategy for a component based on maintenance measures for the component may include: determining a maintenance strategy for the component based on maintenance measures for the component and the risk level of the component, wherein the maintenance strategy for the component includes maintenance measures for the component with a preset execution frequency, the preset execution frequency being determined based on the risk level of the component.

[0089] In some examples, the maintenance baseline of components can be dynamically adjusted based on the determined risk level of the components to push different maintenance strategies. As a non-limiting example, maintenance strategies can be shown in Table 8 below.

[0090] Table 8

[0091]

[0092] As shown in Table 8, the maintenance risk level range includes medium-high risk, medium risk, medium-low risk, low risk and very low risk, the repair risk level range includes extremely high risk and high risk, while extremely low risk and minimum risk do not require maintenance or repair.

[0093] When the risk level of the target object (e.g., component, system, or complete machine) is medium to high risk, the maintenance strategy is to increase the maintenance frequency based on the routine maintenance baseline to perform relevant maintenance measures at a higher frequency. When the risk level of the target object is medium risk, the maintenance strategy is to perform relevant maintenance measures at the routine maintenance frequency. When the risk level of the target object is medium to low risk, the maintenance strategy is to perform relevant maintenance measures at a short-cycle frequency (lower than the routine maintenance frequency). When the risk level of the target object is low risk, the maintenance strategy is to perform relevant maintenance measures at a long-cycle frequency (lower than the short-cycle frequency). When the risk level of the target object is very low risk, the maintenance strategy is to reduce the maintenance frequency based on the long-cycle frequency to postpone the execution of relevant maintenance measures.

[0094] As a non-limiting example, the risk level of C111 is determined to be medium risk in the aforementioned example, and the corresponding maintenance measure is "reduce the load and check whether the relief valve is stuck". Therefore, the maintenance strategy based on the risk level of C111 and the related maintenance measures includes: performing the maintenance measure "reduce the load and check whether the relief valve is stuck" according to the routine maintenance frequency and maintaining normal monitoring.

[0095] Therefore, for the same maintenance measures, the frequency of implementation of the maintenance measures will also be different when the determined risk level is different. This allows for the development of flexible maintenance strategies based on different risk levels, thereby achieving more reliable health management of construction machinery and equipment.

[0096] In some embodiments, the monitoring indicators include a first monitoring indicator and a second monitoring indicator, and the monitoring data includes first monitoring data related to the first monitoring indicator and second monitoring data related to the second monitoring indicator. The method 100 may further include: obtaining first maintenance knowledge associated with a component and including the first monitoring indicator and second maintenance knowledge including the second monitoring indicator from a maintenance knowledge base, wherein the first maintenance knowledge includes a first maintenance measure for the component, and the second maintenance knowledge includes a second maintenance measure for the component; determining a first risk level of the component based on the first monitoring data and a preset risk level threshold, and determining a second risk level of the component based on the second monitoring data and the preset risk level threshold; in response to the first risk level of the component being within the maintenance risk level range, determining a first maintenance strategy for the component based on the first maintenance measure and the first risk level, and in response to the second risk level of the component being within the maintenance risk level range, determining a second maintenance strategy for the component based on the second maintenance measure and the second risk level; and pushing the first maintenance strategy and the second maintenance strategy to execute the first maintenance measure and the second maintenance measure.

[0097] In some examples, there can be multiple maintenance knowledge items related to the same component, with each item including a fault and a maintenance measure. When monitoring components, these monitoring data can be grouped according to the relevant maintenance knowledge. Then, based on the grouped monitoring data and the corresponding maintenance knowledge, the risk level and maintenance strategy for the component can be determined.

[0098] In addition to determining the risk level at the component level, the risk level at the system level or the risk level at the whole machine level can also be determined, and correspondingly, maintenance strategies for the system or for the whole machine can also be determined.

[0099] In some embodiments, the system may include multiple components that need to be monitored, and method 100 may further include: determining maintenance knowledge associated with the system, including maintenance measures for the system, from a maintenance knowledge base; determining the risk level of the system based on the risk levels of the multiple components; in response to the system's risk level being within the maintenance risk level range, determining a maintenance strategy for the system based on the maintenance measures for the system and the system's risk level; and pushing the maintenance strategy for the system to execute the maintenance measures for the system according to a preset execution frequency, wherein the preset execution frequency is determined based on the system's risk level.

[0100] In some examples, the system’s risk level is the highest among the risk levels of multiple components.

[0101] It's understandable that when multiple components of a system experience individual risks, the risk at the system-level can be more severe. This is because it means that even if other components within the system are functioning correctly, the system may still malfunction. Furthermore, the impact of the entire system failing is far greater than the impact of a single component's malfunction. In addition, system maintenance measures can include not only those for the at-risk components but also those for other, non-at-risk components, enabling system-level health management. Therefore, risk prediction at the system level is also beneficial.

[0102] In some embodiments, the construction machinery equipment includes multiple systems, and method 100 may further include: obtaining maintenance knowledge associated with the construction machinery equipment, including maintenance measures for the construction machinery equipment, from a maintenance knowledge base; determining the risk level of the construction machinery equipment based on the risk levels of the multiple systems; in response to the risk level of the construction machinery equipment being within the maintenance risk level range, determining a maintenance strategy for the construction machinery equipment based on the maintenance measures for the construction machinery equipment and the risk level of the construction machinery equipment; and pushing the maintenance strategy for the construction machinery equipment to execute the maintenance measures for the construction machinery equipment according to a preset execution frequency, wherein the preset execution frequency is determined based on the risk level of the construction machinery equipment.

[0103] In some examples, the risk level of the construction machinery equipment is the highest among the risk levels of multiple systems.

[0104] It's understandable that when multiple systems within a system experience risks individually, the risks at the system-wide level can be more severe. This is because it means that even if other systems within the system are functioning correctly, the entire system may still fail to operate properly. Furthermore, the impact of a system-wide failure is far greater than the impact of a single system malfunctioning. Therefore, risk prediction at the system-wide level is beneficial.

[0105] When the risk level of a component is determined to be outside the maintenance risk range based on monitoring data and preset risk level thresholds, other corresponding measures need to be taken.

[0106] In some embodiments, maintenance knowledge includes possible failures of components. For example, as shown in Table 3 above, associated failures of C111 (i.e., possible failures of C111) include internal leakage in the plunger pump.

[0107] In this embodiment, method 100 may further include: in response to a component's risk level exceeding the maintenance risk level range and falling within the repair risk level range, determining the actual fault that has occurred in the component based on possible faults in maintenance knowledge; obtaining repair knowledge associated with the component, including the actual fault, from a repair knowledge base, the repair knowledge including repair measures for the component to handle the actual fault; determining a repair strategy for the component based on the repair measures and the component's risk level; and pushing the repair strategy for the component to execute the repair measures for the component.

[0108] It is understandable that, similar to the implementation of determining the risk levels at the component level, system level, and complete machine level within the maintenance risk level range, the risk levels at the component level, system level, and complete machine level within the repair risk level range can also be determined.

[0109] For example, referring to Table 8, when the risk level of the target object is extremely high, the maintenance strategy is to immediately shut down the machine for inspection and to implement relevant maintenance measures; when the risk level of the target object is high, the maintenance strategy is to implement maintenance measures according to the planned maintenance frequency.

[0110] As a non-restrictive example, assuming the risk level of C111 is determined to be extremely high within the maintenance risk level range, then the actual fault of C111 can be determined to be internal leakage of the plunger pump based on the possible faults (internal leakage of the plunger pump) in the maintenance knowledge related to C111. Then, maintenance knowledge related to C111, including internal leakage of the plunger pump, can be obtained from the maintenance knowledge base. Based on this maintenance knowledge, maintenance measures for handling the internal leakage of the plunger pump in C111 (e.g., replacing the plunger pump) can be obtained. Finally, the maintenance strategy shown in Table 8 can be followed to immediately stop the machine for inspection and replace the plunger pump.

[0111] In some embodiments, in response to the risk level of the component not reaching the maintenance risk level range, method 100 may include: recording monitoring data without performing any maintenance or repair measures.

[0112] As shown in Table 8, when the risk level of the target object is extremely low, there is no need to repair or maintain the target object; it is only necessary to record or retain the target object's operation log.

[0113] In some embodiments, method 100 may further include: when a component malfunctions, obtaining fault knowledge related to the component from a fault knowledge base based on the fault phenomenon to determine the fault in the component; obtaining maintenance knowledge associated with the fault in the component from a maintenance knowledge base, the maintenance knowledge including maintenance measures for the component to handle the fault; determining a maintenance strategy for the component based on the maintenance measures for handling the fault; and pushing the maintenance strategy for the component to execute the maintenance measures for handling the fault.

[0114] As a non-limiting example, construction machinery has fault detection capabilities. Therefore, when a component of the construction machinery malfunctions, or when the construction machinery detects a fault in a component, it can retrieve fault knowledge associated with that component and including the fault phenomenon from a maintenance knowledge base based on the component's classification tree code. This fault knowledge allows it to determine the fault that occurred in the component. Then, based on the fault, it retrieves maintenance knowledge associated with the component and the fault from the maintenance knowledge base, thereby obtaining maintenance measures.

[0115] Therefore, because the component has a classification tree code, the actual fault of the component and the maintenance measures to handle the fault can be determined based on a pre-built fault knowledge base and maintenance knowledge base. This allows for rapid location of the faulty component and quick determination of maintenance measures, enabling timely and effective repair of the component, reducing downtime and maintenance costs of construction machinery, and improving the utilization rate and operational continuity of construction machinery. It is understood that this embodiment can also be used for systems or complete machines.

[0116] This disclosure also provides a health management device for construction machinery equipment, including a module configured to perform the health management method for construction machinery equipment described in any of the foregoing embodiments.

[0117] In some embodiments, the health management device may include a monitoring indicator acquisition module, a monitoring data acquisition module, a maintenance knowledge acquisition module, a risk level determination module, a maintenance strategy determination module, and a push module.

[0118] Here, the monitoring indicator acquisition module is configured to acquire monitoring indicators associated with the component from a pre-built indicator library. The monitoring data acquisition module is configured to acquire monitoring data associated with the monitoring indicators. The maintenance knowledge acquisition module is configured to acquire maintenance knowledge associated with the component and including the monitoring indicators from a pre-built maintenance knowledge base, which includes maintenance measures for the component. The risk level determination module is configured to determine the risk level of the component based on the monitoring data and a preset risk level threshold. The maintenance strategy determination module is configured to determine a maintenance strategy for the component based on the maintenance measures for the component in response to the component's risk level falling within the maintenance risk level range. The push module is configured to push the maintenance strategy for the component to execute the maintenance measures for the component.

[0119] Various embodiments of the health management device for engineering machinery equipment can be found in the preceding description of various embodiments of method 100, and will not be repeated here.

[0120] This disclosure also provides an electronic device. (See reference...) Figure 4 This illustrates a schematic block diagram of an electronic device 400 according to some embodiments of the present disclosure. Figure 4As shown, the electronic device includes a processor 402 and a memory 404 storing computer-executable instructions that, when executed by the processor 402, cause the processor 402 to perform the method 100 according to any of the foregoing embodiments. The processor 402 may be, for example, a central processing unit (CPU) of the electronic device 400. The processor 402 may be any type of general-purpose processor, or it may be a processor specifically designed for health management of engineering machinery, such as an application-specific integrated circuit (“ASIC”). The memory 404 may be coupled to the processor 402 and may include various computer-readable media accessible by the processor 402. In various embodiments, the memory 404 described herein may include volatile and non-volatile media, removable and non-removable media. For example, the memory 404 may include any combination of: random access memory (“RAM”), dynamic RAM (“DRAM”), static RAM (“SRAM”), read-only memory (“ROM”), flash memory, cache memory, and / or any other type of non-transitory computer-readable media. The memory 404 may store instructions that, when executed by the processor 402, cause the processor 402 to execute the method 100 according to any of the foregoing embodiments of the present disclosure.

[0121] The electronic device 400 is configured to perform the method 100 described in any of the foregoing embodiments, and therefore reference can be made to the description of the various embodiments of method 100 above, which will not be repeated here.

[0122] This disclosure also provides a computer-readable storage medium having computer-executable instructions stored thereon, which, when executed by a processor, cause the processor to perform a health management method for engineering machinery equipment according to any of the foregoing embodiments of this disclosure.

[0123] This disclosure also provides a computer program product that may include instructions that, when executed by a processor, can implement the health management method for engineering machinery equipment according to any of the foregoing embodiments of this disclosure. The instructions may be any set of instructions that can be executed directly by one or more processors, such as machine code, or any set of instructions that can be executed indirectly, such as a script. The instructions may be stored in an object code format for direct processing by one or more processors, or stored in any other computer language, including scripts or sets of independent source code modules that are interpreted on demand or compiled in advance.

[0124] Figure 5A schematic block diagram of a computer system 500 on which embodiments of the present disclosure may be implemented is shown. The computer system 500 includes a bus 502 or other communication mechanism for transmitting information, and a processing means 504 coupled to the bus 502 for processing information. The computer system 500 also includes a memory 506 coupled to the bus 502 for storing instructions to be executed by the processing means 504; the memory 506 may be random access memory (RAM) or other dynamic storage device. The memory 506 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processing means 504. The computer system 500 also includes a read-only memory (ROM) 508 or other static storage device coupled to the bus 502 for storing static information and instructions for the processing means 504. A storage device 510, such as a magnetic disk or optical disk, is provided and coupled to the bus 502 for storing information and instructions. Computer system 500 may be coupled via bus 502 to output device 512 for providing output to a user, such as, but not limited to, a display (such as a cathode ray tube (CRT) or liquid crystal display (LCD)), speakers, etc. Input device 514, such as a keyboard, mouse, microphone, etc., is coupled to bus 502 for transmitting information and command selections to processing device 504. Computer system 500 may execute embodiments of the present disclosure. Consistent with certain implementations of the present disclosure, results are provided by computer system 500 in response to processing device 504 executing one or more sequences of one or more instructions contained in memory 506. Such instructions may be read into memory 506 from another computer-readable medium, such as storage device 510. Execution of the sequence of instructions contained in memory 506 causes processing device 504 to perform the methods described herein. Alternatively, the teachings may be implemented using hard-wired circuitry in place of or in combination with software instructions. Therefore, implementations of the present disclosure are not limited to any particular combination of hardware circuitry and software. In various embodiments, computer system 500 can be connected across a network to one or more other computer systems, such as computer system 500, to form a networked system via network interface 516. This network may include a private network or a public network such as the Internet. In a networked system, one or more computer systems can store data and supply data to other computer systems. As used herein, the term "computer-readable medium" refers to any medium that participates in providing instructions to processing device 504 for execution. Such media can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical discs or magnetic disks such as storage device 510. Volatile media include dynamic memory such as memory 506. Transmission media include coaxial cables, copper wires, and optical fibers, including wiring containing bus 502.Common forms of computer-readable media or computer program products include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, or any other magnetic media, CD-ROMs, digital video discs (DVDs), Blu-ray discs, any other optical media, thumb drives, memory cards, RAM, PROMs and EPROMs, fast EPROMs, any other memory chips or cartridges, or any other tangible media from which a computer can read. Various forms of computer-readable media may be involved when carrying one or more sequences of one or more instructions to processing device 504 for execution. For example, instructions may initially be carried on a disk of a remote computer. The remote computer may load the instructions into its dynamic memory and transmit the instructions over a telephone line using a modem. A modem local to computer system 500 may receive data over a telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector coupled to bus 502 may receive the data carried in the infrared signal and place the data on bus 502. Bus 502 carries the data to memory 506, from which processing device 504 retrieves and executes the instructions. Optionally, the instructions received by the memory 506 may be stored on the storage device 510 before or after execution by the processing device 504.

[0125] According to various embodiments, instructions configured to be executed by a processing device to perform a method are stored on a computer-readable medium. The computer-readable medium may be a device for storing digital information. For example, a computer-readable medium includes a compact disc read-only memory (CD-ROM) as known in the art for storing software. The computer-readable medium is accessed by a processor adapted to execute the instructions configured to be executed.

[0126] This disclosure also provides an engineering machinery equipment, including the health management device for engineering machinery equipment described in any of the foregoing embodiments or the electronic device described in any of the foregoing embodiments.

[0127] In some embodiments, the construction machinery equipment is communicatively coupled to a cloud server, and the construction machinery equipment is configured to upload acquired monitoring data related to monitoring indicators to the cloud server. The cloud server then determines the risk level of components based on the monitoring data and a preset risk level threshold. In response to a component's risk level falling within the maintenance risk level range, the cloud server determines a maintenance strategy for the component based on maintenance measures for the component, and pushes the maintenance strategy to the user's app and customer operation and maintenance system. The cloud server may be configured with pre-built libraries for complete machines, systems, components, indicators, fault knowledge bases, maintenance knowledge bases, and repair knowledge bases.

[0128] refer to Figure 6This illustrates a non-limiting illustrative example architecture of an engineering machinery device 600 according to some embodiments of the present disclosure. For example... Figure 6 As shown, the construction machinery equipment 600 may include a module layer, a data acquisition layer, an analysis and decision-making layer, and a maintenance and repair push layer. The module layer may include a common virtual basic library and physical construction machinery equipment modules. The common virtual basic library may include pre-built complete machine libraries, system libraries, component libraries, fault knowledge bases, indicator libraries, maintenance knowledge bases, and repair knowledge bases. The data acquisition layer may include onboard sensors and remote transmission equipment. The analysis and decision-making layer may include onboard processors and cloud servers. The maintenance and repair push layer may include physical construction machinery equipment, a (user) APP, and a customer operation and maintenance system. The physical construction machinery equipment modules are built based on the complete machine library, system library, component library, fault knowledge base, indicator library, maintenance knowledge base, and repair knowledge base, including coded complete machines, systems, components, and related fault knowledge, maintenance knowledge, monitoring indicators, and repair knowledge of the physical construction machinery equipment.

[0129] The foregoing has described one or more exemplary embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a server system. Of course, this disclosure does not exclude the possibility that, with the future development of computer technology, the computer implementing the functions of the above embodiments can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0131] While one or more embodiments of this disclosure provide the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or terminal product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0132] The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded. For example, the use of terms such as "first" or "second" to denote names does not indicate any particular order.

[0133] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, when implementing one or more embodiments of this disclosure, the functions of each module can be implemented in one or more software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0134] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0137] Those skilled in the art will understand that one or more embodiments of this disclosure may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] One or more embodiments of this disclosure can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0139] The same or similar parts between the various embodiments of this disclosure can be referred to mutually, and each embodiment focuses on describing the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. In the description of this disclosure, the descriptions of terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., mean that the specific feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of this disclosure. In this disclosure, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this disclosure and the features of the different embodiments or examples.

[0140] Additionally, when used in this disclosure, the terms “here,” “above,” “below,” “in the following,” “overall,” and similar terms should refer to the entirety of this disclosure and not any particular part thereof. Furthermore, unless expressly stated otherwise or otherwise understood in the context in which they are used, conditional language used herein, such as “may,” “possibly,” “for example,” “like,” etc., is generally intended to express that certain embodiments include, while other embodiments do not, certain features, elements, and / or states. Therefore, such conditional language is not generally intended to imply that one or more embodiments require features, elements, and / or states in any way, or whether such features, elements, and / or states are included or performed in any particular embodiment.

[0141] The above description is merely an embodiment of one or more embodiments of this disclosure and is not intended to limit the scope of the one or more embodiments of this disclosure. Various modifications and variations can be made to the one or more embodiments of this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims.

Claims

1. A health management method for engineering machinery and equipment, wherein, The engineering machinery equipment includes a system, the system includes components that need to be monitored, and the method includes: Obtain monitoring indicators associated with the components from a pre-built indicator library; Obtain monitoring data related to the monitoring indicators; Retrieve maintenance knowledge associated with the component and including the monitoring indicators from a pre-built maintenance knowledge base, the maintenance knowledge including maintenance measures for the component; The risk level of the component is determined based on the monitoring data and the preset risk level threshold. In response to the component's risk level falling within the maintenance risk level range, a maintenance strategy for the component is determined based on the maintenance measures for the component; and Push maintenance strategies for the components to enable the execution of maintenance measures for the components.

2. The method according to claim 1, wherein, Determining a maintenance strategy for the components based on the maintenance measures used for the components includes: A maintenance strategy for the component is determined based on the maintenance measures for the component and the risk level of the component. The maintenance strategy for the component includes maintenance measures for the component with a preset execution frequency, which is determined based on the risk level of the component.

3. The method according to claim 1, wherein, The monitoring indicators include a first monitoring indicator and a second monitoring indicator, the monitoring data includes first monitoring data related to the first monitoring indicator and second monitoring data related to the second monitoring indicator, and the method includes: Obtain first maintenance knowledge associated with the component and including the first monitoring indicator and second maintenance knowledge including the second monitoring indicator from the maintenance knowledge base, wherein the first maintenance knowledge includes first maintenance measures for the component and the second maintenance knowledge includes second maintenance measures for the component; The first risk level of the component is determined based on the first monitoring data and the preset risk level threshold, and the second risk level of the component is determined based on the second monitoring data and the preset risk level threshold. In response to the first risk level of the component being within the maintenance risk level range, a first maintenance strategy for the component is determined based on the first maintenance measure and the first risk level; and in response to the second risk level of the component being within the maintenance risk level range, a second maintenance strategy for the component is determined based on the second maintenance measure and the second risk level. The first maintenance strategy and the second maintenance strategy are pushed to execute the first maintenance measure and the second maintenance measure.

4. The method according to claim 1, wherein, The monitoring indicators include multiple sets of monitoring indicators, and the monitoring data includes multiple sets of monitoring data. Determining the risk level of the component based on the monitoring data and a preset risk level threshold includes: The score of a monitoring indicator is determined based on a set of monitoring data related to each of the multiple sets of monitoring indicators. The risk level indicated by each set of monitoring indicators is determined based on the score of each set of monitoring indicators and the preset risk level threshold. The risk level of the component is determined based on the risk level indicated by each set of monitoring indicators.

5. The method according to claim 4, wherein, At least one set of monitoring indicators includes basic monitoring indicators and weighted monitoring indicators related to the basic monitoring indicators, wherein the following operations are performed for each set of monitoring indicators: The score of the basic monitoring indicator is determined based on the basic monitoring data corresponding to the basic monitoring indicator in one of the multiple sets of monitoring data related to the monitoring indicator in that set of monitoring data, and the score of the weighted monitoring indicator is determined based on the weighted monitoring data corresponding to the weighted monitoring indicator in that set of monitoring data. The scores for this group of monitoring indicators are determined based on the scores of the basic monitoring indicators and the scores of the weighted monitoring indicators.

6. The method according to claim 4, wherein, The multiple sets of monitoring indicators include two sets of monitoring indicators. Determining the risk level of the component based on the monitoring data and a preset risk level threshold includes: The risk level of the component is determined based on the risk level indicated by the two sets of monitoring indicators and the preset risk level matrix.

7. The method according to claim 1, wherein, The system includes multiple components, and the method further includes: In response to the indicator library including monitoring indicators associated with at least one of the plurality of components and the maintenance knowledge base including maintenance knowledge associated with the at least one component, the at least one component is determined to be a component that needs to be monitored.

8. The method according to claim 1, wherein, The system includes multiple components that need to be monitored, and the method further includes: From the maintenance knowledge base, determine the maintenance knowledge associated with the system, including maintenance measures for the system; The risk level of the system is determined based on the risk levels of multiple components; In response to the system's risk level falling within the maintenance risk level range, a maintenance strategy for the system is determined based on the maintenance measures for the system and the system's risk level; and A maintenance strategy for the system is pushed out so that maintenance measures for the system can be executed at a preset execution frequency, wherein the preset execution frequency is determined based on the risk level of the system.

9. The method according to claim 8, wherein, The risk level of the system is the highest risk level among the risk levels of the multiple components.

10. The method according to claim 8, wherein, The engineering machinery equipment includes multiple of the aforementioned systems, and the method further includes: Retrieve maintenance knowledge associated with the construction machinery equipment, including maintenance measures for the construction machinery equipment, from the maintenance knowledge base; The risk level of the engineering machinery and equipment is determined based on the risk levels of multiple systems. In response to the fact that the risk level of the construction machinery equipment falls within the maintenance risk level range, a maintenance strategy for the construction machinery equipment is determined based on the maintenance measures for the construction machinery equipment and the risk level of the construction machinery equipment; and A maintenance strategy for the construction machinery equipment is pushed out so that maintenance measures for the construction machinery equipment can be executed according to a preset execution frequency, wherein the preset execution frequency is determined based on the risk level of the construction machinery equipment.

11. The method according to claim 10, wherein, The risk level of the engineering machinery equipment is the highest among the risk levels of the multiple systems.

12. The method according to claim 1, wherein, The maintenance knowledge includes possible failures of the components, and the method further includes: In response to the fact that the risk level of the component exceeds the maintenance risk level range and falls within the repair risk level range, the actual fault of the component is determined based on the possible faults in the maintenance knowledge; Retrieve maintenance knowledge associated with the component, including the actual fault that occurred, from the maintenance knowledge base; the maintenance knowledge includes maintenance measures for the component to handle the actual fault that occurred. A maintenance strategy for the component is determined based on the maintenance measures and the risk level of the component; and A maintenance strategy for the component is pushed out in order to execute maintenance measures for the component.

13. The method according to claim 1, further comprising: When the component malfunctions, fault knowledge related to the component is retrieved from the fault knowledge base based on the fault phenomenon to determine the fault that occurred in the component. Retrieve maintenance knowledge associated with the component and the fault that occurred in the component from a maintenance knowledge base, the maintenance knowledge including maintenance measures for the component to handle the fault; A maintenance strategy for the component is determined based on the maintenance measures used to handle the fault; as well as A maintenance strategy for the component is pushed out in order to implement maintenance measures to address the fault.

14. The method according to claim 1, further comprising: Construct a complete machine library, a system library, and a component library. The complete machine library includes multiple families of engineering machinery equipment, and each family of engineering machinery equipment includes at least one type of engineering machinery equipment. The system library includes multiple systems. The component library includes multiple types of components, each type of component includes at least one component family, and each component family includes at least one component. Based on the complete machine library, the system library, and the component library, an indicator library and a maintenance knowledge base are constructed, wherein... The indicator library includes multiple sets of monitoring indicators. Each set of monitoring indicators includes at least one monitoring indicator associated with one of the various engineering machinery equipment in the complete machine library, the various systems in the system library, and the various components in the component library. The maintenance knowledge base includes multiple maintenance knowledge entries. Each maintenance knowledge entry includes monitoring indicators, maintenance measures, and possible failures of a corresponding one of the various engineering machinery equipment in the complete machine library, various systems in the system library, and various components in the component library. The monitoring indicators in the maintenance knowledge are obtained from the indicator library.

15. The method of claim 14, further comprising: A fault knowledge base and a maintenance knowledge base are constructed based on the aforementioned complete machine library, the aforementioned system library, and the aforementioned component library, wherein... The fault knowledge base includes multiple fault knowledge entries. Each fault knowledge entry includes a fault phenomenon associated with one of the various engineering machinery equipment in the complete machine library, various systems in the system library, and various components in the component library, as well as the fault corresponding to that fault phenomenon. The maintenance knowledge base includes multiple maintenance knowledge entries. Each maintenance knowledge entry includes a possible fault associated with one of the various engineering machinery equipment in the complete machine library, various systems in the system library, and various components in the component library, as well as maintenance measures for handling the fault.

16. A health management device for engineering machinery and equipment, comprising: A module configured to perform a health management method for engineering machinery equipment according to any one of claims 1 to 15.

17. An electronic device comprising: processor; as well as A memory storing computer-executable instructions, which, when executed by the processor, cause the processor to perform a health management method for engineering machinery equipment according to any one of claims 1 to 15.

18. A computer-readable storage medium having stored thereon computer-executable instructions, which, when executed by a computer, cause the computer to perform a health management method for engineering machinery equipment according to any one of claims 1 to 15.

19. A computer program product comprising instructions that, when executed by a processor, implement the health management method for engineering machinery equipment according to any one of claims 1 to 15.

20. An engineering machinery equipment, comprising: The health management device for engineering machinery equipment according to claim 16 or the electronic device according to claim 17.

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