Device maintenance state determination method and system, electronic device, and readable storage medium

Abstract

The present application relates to the technical field of equipment maintenance, and discloses an equipment maintenance state determination method and system, an electronic device and a readable storage medium, the method obtains equipment operation and maintenance records of a target device in a preset time period, determines a fault time period of a device fault event and a condition time period of a fault operation condition corresponding to the device fault event according to the equipment operation and maintenance records, calculates a fault occurrence probability of the target device when any fault operation condition is met based on the preset time period, the fault time period and the condition time period, so as to determine the probability of the target device occurring a device fault event when real-time operation parameters are collected, and determine the equipment maintenance state of the target device. Compared with determining whether the equipment needs to be maintained by maintenance experience of maintenance personnel, the correlation between the device fault event and the equipment operation and maintenance parameters is established, the determination standardization of the equipment maintenance state is realized, and the determination accuracy of the equipment maintenance state is improved.

Description

Technical Field

[0001] This invention relates to the field of equipment maintenance technology, and in particular to a method, system, electronic device, and readable storage medium for determining equipment maintenance status. Background Technology

[0002] The integration of Industrial Internet of Things (IIoT), big data, and artificial intelligence (AI) has become a crucial tool for the informatization and intelligentization of the industrial sector. Equipment condition monitoring, as one of the most critical application scenarios, effectively helps enterprises improve overall equipment operation and maintenance, management, and production efficiency, making it an indispensable intelligent management tool in the industrial field. Specifically, equipment maintenance status monitoring, through predictive maintenance solutions, can effectively address situations where equipment is missed or difficult to inspect due to high temperatures, high altitudes, insufficient light, or harsh environments. It helps enterprises achieve real-time control over the status of critical equipment, ensuring safe production; promptly identify potential equipment hazards, optimizing production decisions; accurately locate fault locations, providing reliable data for equipment maintenance and repair; and facilitate enterprises' intelligent manufacturing upgrades and transformation.

[0003] Currently, equipment status is usually monitored based on maintenance experience to determine whether maintenance is needed. However, due to the randomness and independence of equipment failure events, the asynchronous skill levels of maintenance personnel, the wide variety of production equipment, and the lack of correlation between equipment operation and maintenance parameters and equipment failure events, the quality of operation and maintenance cannot be standardized, and the accuracy of determining the equipment maintenance status is low. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] In view of the shortcomings of the prior art described above, the present invention discloses a method, system, electronic device and readable storage medium for determining equipment maintenance status, so as to improve the accuracy of determining equipment maintenance status.

[0006] This invention provides a method for determining the maintenance status of equipment, comprising: acquiring equipment operation and maintenance records of a target device within a preset time period, the equipment operation and maintenance records including equipment fault events and equipment operation and maintenance parameters; determining the time period during which the equipment fault event occurs on the target device as a fault time period; determining at least one fault operation condition corresponding to the equipment fault event based on the equipment operation and maintenance parameters within the fault time period; and determining the time period during which the target device satisfies each of the equipment fault conditions as a condition time period for each of the fault operation conditions; calculating the probability of occurrence of the equipment fault event when the target device satisfies any fault operation condition based on the preset time period, the fault time period, and the condition time period; collecting real-time operating parameters of the target device; determining a target probability of the real-time operating parameters from the fault occurrence probability based on the comparison relationship between the real-time operating parameters and the fault operation conditions; and determining the equipment maintenance status of the target device based on the target probability.

[0007] Optionally, the equipment operation and maintenance parameters include the overall equipment efficiency, which is obtained by the following formula: Obtain the equipment startup time, equipment shutdown time, equipment initialization period, equipment failure period, product production quantity, predicted production period for a single product, and product qualification quantity of the target equipment within a preset first time unit; determine the planned operating period based on the equipment startup time and equipment shutdown time; calculate the planned operating time of the equipment based on the equipment initialization period and equipment failure period to obtain the actual operating period; determine the time utilization rate of the target equipment based on the ratio between the actual operating period and the planned operating period; determine the theoretical production period based on the product production quantity and the predicted production period for a single product; determine the capacity utilization rate of the target equipment based on the ratio between the theoretical production period and the actual operating period; determine the yield rate of the target equipment based on the ratio between the product qualification quantity and the product production quantity; and determine the overall equipment efficiency of the target equipment based on the time utilization rate, the capacity utilization rate, and the yield rate.

[0008] Optionally, the equipment operation and maintenance parameters may also include at least one of the following: equipment operating parameters, including one or more of equipment operating time, equipment current, and equipment voltage; equipment environmental parameters, including one or more of equipment ambient temperature, equipment ambient humidity, equipment ambient air quality, and equipment ambient light intensity; and equipment maintenance parameters, including one or more of equipment maintenance frequency, equipment maintenance type, and maintenance interval time period.

[0009] Optionally, determining at least one fault operating condition corresponding to the equipment fault event based on the equipment operation and maintenance parameters within the fault time period includes at least one of the following: determining multiple sampling time points within the fault time period, classifying the equipment operation and maintenance parameters corresponding to each sampling time point to obtain multiple fault operating conditions, wherein in any fault operating condition, the equipment operation and maintenance parameters corresponding to each sampling time point are the same; determining multiple sampling time periods within the fault time period, determining the operation and maintenance parameter intervals corresponding to each sampling time period based on the equipment operation and maintenance parameters within each sampling time period, classifying the operation and maintenance parameter intervals corresponding to each sampling time period to obtain multiple fault operating conditions, wherein in any fault operating condition, the operation and maintenance parameter intervals corresponding to each sampling time period are the same.

[0010] Optionally, the probability of a device malfunction occurring when any fault operating condition is met is calculated based on the preset time period, the fault time period, and the condition time period. This includes: determining the total probability of the device malfunction occurring based on the preset time period and the fault time period; calculating the total probability of condition satisfaction for each fault operating condition based on the preset time period; calculating the probability of condition superposition for each fault operating condition based on the fault time period; and calculating the probability of a device malfunction occurring when any fault operating condition is met based on the total probability of malfunction, the total probability of condition satisfaction, and the probability of condition superposition. The total probability of malfunction and the probability of condition superposition affect the probability of malfunction in a positively correlated manner, while the total probability of condition satisfaction affects the probability of malfunction in a negatively correlated manner.

[0011] Alternatively, the probability of a fault occurring can be determined using the following formula: Wherein, C represents a device malfunction event occurring in the target device, and F... n For the nth equipment operation and maintenance parameter under any fault operation condition, the P(C|F1,F2,...,F n P(C) represents the probability of a fault occurring when the target device meets the fault operating conditions, and P(F1,F2,...,F2) represents the total probability of the fault occurring. n |C) represents the conditional probability of fault superposition, P(F1,F2,...,F) n ) represents the total probability that the fault operation conditions are met.

[0012] Optionally, determining the equipment maintenance status of the target device based on the target probability includes at least one of the following: if the target probability is greater than or equal to a preset probability threshold, then the equipment maintenance status of the target device is determined to require maintenance; determining the growth trend of the target probability within a preset time period, and if the growth trend is greater than or equal to a preset trend threshold, then the equipment maintenance status of the target device is determined to require maintenance, wherein the growth trend includes growth value and / or growth rate.

[0013] This invention provides a system for determining the maintenance status of an equipment, comprising: an acquisition module, configured to acquire equipment operation and maintenance records of a target equipment within a preset time period, the equipment operation and maintenance records including equipment fault events and equipment operation and maintenance parameters; a determination module, configured to determine the time period during which the equipment fault event occurs on the target equipment as a fault time period, determine at least one fault operation condition corresponding to the equipment fault event based on the equipment operation and maintenance parameters within the fault time period, and determine the time period during which the target equipment satisfies each of the equipment fault conditions as a condition time period for each of the fault operation conditions; a calculation module, configured to calculate, based on the preset time period, the fault time period, and the condition time period, the probability of the equipment fault event occurring when the target equipment satisfies any fault operation condition; and a collection module, configured to collect real-time operating parameters of the target equipment, determine a target probability of the real-time operating parameters from the probability of the fault occurrence based on the comparison relationship between the real-time operating parameters and the fault operation conditions, and determine the equipment maintenance status of the target equipment based on the target probability.

[0014] The present invention provides an electronic device, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the above-described method.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described method.

[0016] The beneficial effects of this invention are:

[0017] By acquiring the equipment operation and maintenance records of the target equipment within a preset time period, and determining the fault time period and the corresponding fault operating conditions based on these records, the probability of a fault event occurring when any fault operating condition is met is calculated. This allows for the determination of the probability of a fault event occurring on the target equipment when real-time operating parameters are collected, thus defining the equipment's maintenance status. Compared to relying on maintenance personnel's experience to determine whether equipment requires maintenance, this method establishes a correlation between fault events and equipment operation and maintenance parameters, standardizing the determination of equipment maintenance status and improving its accuracy. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for determining the maintenance status of equipment according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of a system architecture for implementing a method for determining equipment maintenance status, as described in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of a device maintenance status determination system according to an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and sub-samples in the embodiments can be combined with each other.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0024] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0025] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0026] Unless otherwise stated, the term "multiple" means two or more.

[0027] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0028] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0029] First, it should be noted that the embodiments disclosed herein determine the probability of equipment failure events using Bayes' theorem. The full name of Bayes' theorem is the "Bayes-Laplace formula," and its expression is: That is, the probability of event A occurring given that event B has occurred = the probability of event B occurring given that event A has occurred × the probability of event A occurring ÷ the probability of event B occurring, where P(A) is the prior probability and P(B|A) is the posterior probability.

[0030] Combination Figure 1 As shown in the embodiments of this disclosure, a method for determining the maintenance status of equipment is provided, including:

[0031] Step S101: Obtain the equipment operation and maintenance records of the target device within a preset time period;

[0032] The equipment operation and maintenance records include equipment failure events and equipment operation and maintenance parameters;

[0033] Step S102: Determine the time period during which the target device experiences a device failure event as the failure time period; determine at least one failure operation condition corresponding to the device failure event based on the device operation and maintenance parameters within the failure time period; and determine the time period during which the target device meets each device failure condition as the condition time period of each failure operation condition.

[0034] Step S103: Calculate the probability of a fault event occurring when the target device meets any fault operating condition by using a preset time period, a fault time period, and a condition time period.

[0035] Step S104: Collect the real-time operating parameters of the target device, and determine the target probability of the real-time operating parameters from the probability of fault occurrence based on the comparison between the real-time operating parameters and the fault operating conditions.

[0036] Step S105: Determine the equipment maintenance status of the target equipment based on the target probability.

[0037] The equipment maintenance status determination method provided in this disclosure involves acquiring the equipment operation and maintenance records of the target equipment within a preset time period. Based on these records, the method determines the fault time period of the equipment failure event and the condition time period of the corresponding fault operating conditions. Then, based on the preset time period, the fault time period, and the condition time period, it calculates the probability of a fault event occurring when any fault operating condition is met. This allows for the determination of the probability of a fault event occurring on the target equipment when real-time operating parameters are collected, thus determining the equipment maintenance status. Compared to relying on maintenance personnel's experience to determine whether equipment needs maintenance, this method establishes a correlation between equipment failure events and equipment operation and maintenance parameters by calculating the probability of a fault event occurring when any fault operating condition is met, thereby standardizing the determination of equipment maintenance status and improving its accuracy.

[0038] Combination Figure 2 As shown, this disclosure provides a system architecture for implementing a method for determining equipment maintenance status, including a data acquisition layer 201, a data storage layer 202, a system service layer 203, and a maintenance display layer 204. The data acquisition layer collects equipment operation and maintenance records and real-time operating parameters of the target equipment within a preset time period. The data storage layer stores the collected equipment operation and maintenance records and real-time operating parameters. The system service layer calculates the probability of a fault event occurring when the target equipment meets any fault operating condition based on a preset time period, a fault time period, and a conditional time period. The system service layer also determines the target probability of the real-time operating parameters from the fault occurrence probability based on the comparison between the real-time operating parameters and the fault operating conditions, and determines the equipment maintenance status of the target equipment based on the target probability. The maintenance display layer displays the equipment maintenance status of the target equipment to remind the user to perform equipment maintenance.

[0039] The system architecture for determining equipment maintenance status provided in this disclosure involves acquiring equipment operation and maintenance records of the target equipment within a preset time period. Based on these records, the system determines the fault time period of an equipment failure event and the condition time period of the corresponding fault operating conditions. Then, based on the preset time period, fault time period, and condition time period, it calculates the probability of an equipment failure event occurring when any fault operating condition is met. This allows for the determination of the probability of an equipment failure event occurring on the target equipment when real-time operating parameters are collected, thus determining the equipment maintenance status. Compared to relying on maintenance personnel's experience to determine whether equipment needs maintenance, this method establishes a correlation between equipment failure events and equipment operation and maintenance parameters by calculating the probability of an equipment failure event occurring when any fault operating condition is met, thereby standardizing the determination of equipment maintenance status and improving its accuracy.

[0040] In some embodiments, the data acquisition layer is used to connect to various different devices using a data edge acquisition gateway to collect device data. At the same time, it connects to data from various systems, such as business data from various platforms like IIoT (Industrial Internet of Things), SCADA (Supervisory Control and Data Acquisition), and WMS (Warehouse Management System).

[0041] In some embodiments, the data storage layer is used to store various types of data for the system, including various business data of system operation, file data, log data and other related persistent data. The data storage layer includes PostgresDB (a process-based database), Minio (a distributed storage database), Redis (RemoteDictionary Server), etc.

[0042] In some embodiments, the system service layer includes services such as equipment services, spare parts services, planning and scheduling services, equipment models, diagnostic analysis, and statistical reports. Among them, equipment services are used to manage the ledger of asset equipment and equipment lifecycle maintenance information; spare parts services are used to manage the equipment spare parts ledger, spare parts usage records, and other related information; planning and scheduling services are used to manage the processing of information related to equipment inspection, patrol inspection, maintenance, problems, and operation and maintenance triggers; equipment models mainly utilize big data, AI, and other technologies to model equipment data, ultimately forming equipment operation and maintenance models, equipment maps, and other equipment-related analyses; diagnostic analysis mainly utilizes equipment model data to perform equipment model analysis to provide intelligent early warning and optimize equipment operating efficiency; and statistical reports are used to collect statistics on equipment, spare parts, planning and scheduling, diagnostic analysis, and other related data.

[0043] In some embodiments, the maintenance display layer is used to display the device maintenance status of the target device on a PC (Personal Computer), APP (application), or mini-program to remind the user to perform device maintenance.

[0044] Optionally, the equipment operation and maintenance parameters include the overall equipment efficiency, which is obtained through the following formulas: Obtain the equipment startup time, shutdown time, initialization time period, failure time period, product production quantity, predicted production time period for a single product, and qualified product quantity within a preset first time unit; determine the planned operating time period based on the equipment startup and shutdown times; calculate the planned operating time based on the initialization and failure time periods to obtain the actual operating time period; determine the time utilization rate of the target equipment based on the ratio between the actual operating time period and the planned operating time period; determine the theoretical production time period based on the product production quantity and the predicted production time period for a single product; determine the capacity utilization rate of the target equipment based on the ratio between the theoretical production time period and the actual operating time period; determine the yield rate of the target equipment based on the ratio between the qualified product quantity and the production quantity; and determine the overall equipment efficiency of the target equipment based on the time utilization rate, capacity utilization rate, and yield rate.

[0045] In some embodiments, Overall Equipment Effectiveness (OEE) is the ratio of the actual output to the theoretical output of the target equipment during its load period. Since the target equipment often encounters downtime due to factory shutdowns such as water, power, and gas outages, resulting in waiting for orders, production schedules, inspections, or waiting for the previous process, this embodiment of the disclosure, in order to reflect the concept of downtime caused by non-equipment factors, references the equipment initialization period and equipment failure period in the OEE. This makes the OEE of the target equipment more closely reflect the current production situation, thereby improving the accuracy of determining the OEE.

[0046] Alternatively, time utilization can be determined using the following formula:

[0047]

[0048] In the formula, θ1 represents the time utilization rate, and TPoint represents the time efficiency. Start Device power-on time point, TPoint End For the equipment shutdown time, TPeriod Init Tperiod is the device initialization time period. pause This refers to the period during which the equipment was out of service.

[0049] Alternatively, capacity utilization can be determined using the following formula:

[0050]

[0051] In the formula, θ2 is the capacity utilization rate, and TPoint is the capacity utilization rate. Start Device power-on time point, TPoint End N is the device shutdown time. produce For the quantity of products produced, a produce Predict the production timeframe for a single product.

[0052] Alternatively, the yield rate can be determined using the following formula:

[0053]

[0054] In the formula, θ2 is the yield rate, and N produce N represents the quantity of products produced. pass This refers to the number of qualified products.

[0055] Optionally, the equipment operation and maintenance parameters may also include at least one of the following: equipment operating parameters, including one or more of equipment operating time, equipment current, and equipment voltage; equipment environmental parameters, including one or more of equipment ambient temperature, equipment ambient humidity, equipment ambient air quality, and equipment ambient light intensity; and equipment maintenance parameters, including one or more of equipment maintenance frequency, equipment maintenance type, and maintenance interval time period.

[0056] In some embodiments, the system collects all attributes of the equipment, such as equipment properties, manufacturer information, operating parameters (running time, current, voltage, OEE, etc.), operating environment (high temperature, high altitude, insufficient light, harsh environment, etc.), maintenance information, and repair information, and performs data modeling and analysis to obtain relevant parameters, maintenance standards, operating environment, and other attributes for efficient equipment operation, forming a standardized equipment model. This allows equipment operation to no longer rely on human experience to determine equipment operating efficiency, thereby improving the overall efficiency of equipment operation and reducing costs.

[0057] In some embodiments, some equipment operation and maintenance parameters are shown in Table 1.

[0058] Table 1

[0059]

[0060]

[0061] Optionally, determining at least one fault operating condition corresponding to the equipment fault event based on the equipment operation and maintenance parameters within the fault time period includes at least one of the following: determining multiple sampling time points within the fault time period, classifying the equipment operation and maintenance parameters corresponding to each sampling time point to obtain multiple fault operating conditions, wherein the equipment operation and maintenance parameters corresponding to each sampling time point are the same in any fault operating condition; determining multiple sampling time periods within the fault time period, determining the operation and maintenance parameter intervals corresponding to each sampling time period based on the equipment operation and maintenance parameters within each sampling time period, classifying the operation and maintenance parameter intervals corresponding to each sampling time period to obtain multiple fault operating conditions, wherein the operation and maintenance parameter intervals corresponding to each sampling time period are the same in any fault operating condition.

[0062] Optionally, the probability of a fault event occurring when the target equipment meets any fault operating condition is calculated based on a preset time period, a fault time period, and a condition time period. This includes: determining the total probability of a fault event occurring based on the preset time period and the fault time period; calculating the total probability of condition satisfaction for each fault operating condition based on the preset time period; calculating the probability of condition superposition for each fault operating condition based on the fault time period; and calculating the probability of a fault event occurring when the target equipment meets any fault operating condition based on the total probability of fault occurrence, the total probability of condition satisfaction, and the probability of condition superposition. The total probability of fault occurrence and the probability of condition superposition affect the probability of fault occurrence with a positive correlation, while the total probability of condition satisfaction affects the probability of fault occurrence with a negative correlation.

[0063] Alternatively, the probability of a fault occurring can be determined using the following formula:

[0064]

[0065] Where C represents a device malfunction event in the target device, and F n Let P(C|F1,F2,...,F) be the nth equipment operation and maintenance parameter under any fault operation condition. n Let P(C) be the probability of a fault occurring when the target equipment meets the fault operation conditions, and let P(C) be the total probability of the fault occurring. n |C) represents the conditional probability of fault superposition, P(F1,F2,...,F) n ) represents the total probability that the fault operation conditions are met.

[0066] In some embodiments, based on the equipment model, intelligent early warning is used as a trigger condition to execute an intelligent diagnostic algorithm model, thereby achieving equipment fault cause identification and fault location. Simultaneously, based on equipment fault diagnosis theory, mathematical modeling thinking is used to achieve data featureization and logical knowledge representation, realizing the automation and intelligence of the diagnostic process. Relying on the equipment's digital model, an equipment prediction algorithm library is formed based on industry mechanisms, extracting relevant features from the collected data and digitally representing diagnostic knowledge and mechanisms. The system provides a diagnostic model function, with a one-to-one correspondence between the diagnostic model and the equipment model, enabling automatic fault diagnosis of the equipment based on the diagnostic model. The diagnostic model is generated based on diagnostic mechanisms and optimized according to fault case data.

[0067] In some embodiments, the equipment maintenance status includes a category maintenance status corresponding to several maintenance categories, wherein the maintenance categories include one or more of the following: timed feeding (NB), tank resistance and / or voltage adjustment (RC), anode replacement (AC), effect extinction (AEB), aluminum tapping (TAP), bus lifting (RR), and side feeding operation (iRRFEED).

[0068] Optionally, determining the equipment maintenance status of the target device based on the target probability includes at least one of the following: if the target probability is greater than or equal to a preset probability threshold, then the equipment maintenance status of the target device is determined to require maintenance; determining the growth trend of the target probability within a preset time period, and if the growth trend is greater than or equal to a preset trend threshold, then the equipment maintenance status of the target device is determined to require maintenance, wherein the growth trend includes the growth value and / or the growth rate.

[0069] Optionally, determining the equipment maintenance status of the target device based on the target probability includes: if the target probability is greater than or equal to a preset probability threshold, then determining the equipment maintenance status of the target device as requiring maintenance; if the target probability is less than the preset probability threshold, then determining the equipment maintenance status of the target device as not requiring maintenance.

[0070] In some embodiments, the trigger state is determined by comparing current real-time data with a threshold. Depending on how the threshold is configured, it can be categorized into manually set thresholds and algorithmically adaptive thresholds.

[0071] Optionally, determining the equipment maintenance status of the target device based on the target probability includes: determining the growth trend of the target probability within a preset time period, wherein the growth trend includes the growth value and / or growth rate; if the growth trend is greater than or equal to a preset trend threshold, the equipment maintenance status of the target device is determined to require maintenance; if the growth trend is less than the preset trend threshold, the equipment maintenance status of the target device is determined to require no maintenance.

[0072] In some embodiments, by combining the current real-time value and historical data, and comparing the growth of the data, a trigger state is obtained, which can be divided into trend increase trigger warning and trend growth trigger warning.

[0073] In some embodiments, a complete triggering process system can significantly reduce missed alarms and false alarms, meeting users' needs for accurate and intelligent early warning. The early warning system divides early warnings into four levels, which can evaluate the equipment fault status and provide action suggestions when an alarm is triggered. These suggestions include monitoring operation, conducting necessary inspections / patrols at appropriate times, maintenance plans, and planned shutdowns for maintenance in the near future.

[0074] Combination Figure 3As shown, this embodiment of the present disclosure provides a system for determining the maintenance status of an equipment, including an acquisition module 301, a determination module 302, a calculation module 303, and a collection module 304. The acquisition module 301 acquires equipment operation and maintenance records of a target equipment within a preset time period. These records include equipment fault events and equipment operation and maintenance parameters. The determination module 302 determines the time period during which a equipment fault event occurs on the target equipment as a fault time period, determines at least one fault operating condition corresponding to the equipment fault event based on the equipment operation and maintenance parameters within the fault time period, and determines the time period during which the target equipment meets each fault condition as a condition time period for each fault operating condition. The calculation module 303 calculates the probability of a fault event occurring when the target equipment meets any fault operating condition based on the preset time period, the fault time period, and the condition time period. The collection module 304 collects real-time operating parameters of the target equipment, determines the target probability of the real-time operating parameters from the fault occurrence probability based on the comparison relationship between the real-time operating parameters and the fault operating conditions, and determines the equipment maintenance status of the target equipment based on the target probability.

[0075] The equipment maintenance status determination system provided in this disclosure acquires the equipment operation and maintenance records of the target equipment within a preset time period. Based on these records, it determines the fault time period of the equipment failure event and the condition time period of the corresponding fault operating conditions. Then, it calculates the probability of a fault event occurring when the target equipment meets any fault operating condition, based on the preset time period, the fault time period, and the condition time period. This allows for the determination of the probability of a fault event occurring on the target equipment when real-time operating parameters are collected, thus determining the equipment maintenance status. Compared to relying on maintenance personnel's experience to determine whether equipment needs maintenance, this system establishes a correlation between equipment failure events and equipment operation and maintenance parameters by calculating the probability of a fault event occurring when the target equipment meets any fault operating condition based on the equipment operation and maintenance records. This standardizes the determination of equipment maintenance status and improves its accuracy.

[0076] Figure 4 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 4As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0078] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0079] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of this application.

[0080] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0081] This disclosure also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements any of the methods in this embodiment.

[0082] The computer-readable storage medium in the embodiments of this disclosure will be understood by those skilled in the art: all or part of the steps of the above method embodiments can be implemented by hardware related to computer programs. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0083] The electronic device disclosed in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic device performs the various steps of the above method.

[0084] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0085] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), graphics processing units (GPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0086] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and subsamples of some embodiments may be included in or replace parts and subsamples of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used herein means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated subsamples, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other subsamples, wholes, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes the element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0087] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0088] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and 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 sub-samples may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for determining the maintenance status of equipment, characterized in that, include: Obtain the equipment operation and maintenance records of the target device within a preset time period. The equipment operation and maintenance records include equipment failure events and equipment operation and maintenance parameters. The time period during which the target device experiences the device failure event is defined as the failure time period. At least one failure operation condition corresponding to the device failure event is determined based on the device operation and maintenance parameters within the failure time period. The time period during which the target device satisfies each of the failure operation conditions is defined as the condition time period for each of the failure operation conditions. Determining at least one fault operating condition corresponding to the equipment fault event based on the equipment operation and maintenance parameters within the fault time period includes at least one of the following: determining multiple sampling time points within the fault time period, classifying the equipment operation and maintenance parameters corresponding to each sampling time point to obtain multiple fault operating conditions, wherein in any fault operating condition, the equipment operation and maintenance parameters corresponding to each sampling time point are the same; determining multiple sampling time periods within the fault time period, determining the operation and maintenance parameter intervals corresponding to each sampling time period based on the equipment operation and maintenance parameters within each sampling time period, classifying the operation and maintenance parameter intervals corresponding to each sampling time period to obtain multiple fault operating conditions, wherein in any fault operating condition, the operation and maintenance parameter intervals corresponding to each sampling time period are the same; The probability of a device failure event occurring when any fault operating condition is met is obtained by calculating based on the preset time period, the fault time period, and the condition time period. Collect real-time operating parameters of the target device, determine the target probability of the real-time operating parameters from the probability of fault occurrence based on the comparison relationship between the real-time operating parameters and the fault operating conditions, and determine the device maintenance status of the target device based on the target probability; Determining the equipment maintenance status of the target device based on the target probability includes at least one of the following: if the target probability is greater than or equal to a preset probability threshold, then the equipment maintenance status of the target device is determined to require maintenance; determining the growth trend of the target probability within a preset time period, and if the growth trend is greater than or equal to a preset trend threshold, then the equipment maintenance status of the target device is determined to require maintenance, wherein the growth trend includes growth value and / or growth rate.

2. The method according to claim 1, characterized in that, The equipment operation and maintenance parameters include the overall equipment efficiency, which is obtained using the following formula: The target device is obtained at the following time units within a preset first time unit: device power-on time, device power-off time, device initialization time period, device failure time period, product production quantity, predicted production time period for a single product, and product qualified quantity. The planned operating time period is determined based on the equipment startup time and the equipment shutdown time. The planned operating time period is calculated based on the equipment initialization time period and the equipment failure time period to obtain the actual operating time period. The time utilization rate of the target equipment is determined based on the ratio between the actual operating time period and the planned operating time period. The theoretical production time period is determined based on the production quantity of the product and the predicted production time period of the individual product. The capacity utilization rate of the target equipment is determined based on the ratio between the theoretical production time period and the actual operating time period. The yield rate of the target equipment is determined based on the ratio between the number of qualified products and the number of products produced. The overall equipment efficiency of the target equipment is determined based on the time utilization rate, the capacity utilization rate, and the yield rate.

3. The method according to claim 2, characterized in that, The equipment operation and maintenance parameters also include at least one of the following: Equipment operating parameters include one or more of the following: equipment operating time, equipment current, and equipment voltage; Equipment environmental parameters include one or more of the following: equipment ambient temperature, equipment ambient humidity, equipment ambient air quality, and equipment ambient light intensity. Equipment maintenance parameters include one or more of the following: frequency of equipment maintenance, type of equipment maintenance, and maintenance interval.

4. The method according to any one of claims 1 to 3, characterized in that, The probability of a device malfunction occurring when any fault condition is met is calculated based on the preset time period, the fault time period, and the conditional time period, including: The total probability of the occurrence of the equipment failure event is determined based on the preset time period and the failure time period. The condition time period corresponding to each of the fault operation conditions is calculated according to the preset time period to obtain the total probability of satisfying each of the fault operation conditions. Based on the fault time period, the condition time period corresponding to each fault operation condition is calculated to obtain the fault superposition condition probability of each fault operation condition. The probability of a fault occurring when the target device meets any fault operating condition is calculated based on the total probability of the fault occurring, the total probability of the condition being met, and the probability of the fault being superimposed. The total probability of the fault occurring and the probability of the fault being superimposed affect the probability of the fault occurring in a positive correlation, while the total probability of the condition being met affects the probability of the fault occurring in a negative correlation.

5. The method according to claim 4, characterized in that, The probability of a fault occurring is determined using the following formula: ， in, The target device experiences a device malfunction event. For any fault operating condition, the first Each device operation and maintenance parameter, the This represents the probability of a device malfunction occurring when the target device meets the aforementioned fault operating conditions. This represents the total probability of the aforementioned fault occurring. The conditional probability of the fault superposition. The total probability that the fault operation conditions are met.

6. A system for determining the maintenance status of equipment, characterized in that, include: The acquisition module is used to acquire the equipment operation and maintenance records of the target device within a preset time period. The equipment operation and maintenance records include equipment failure events and equipment operation and maintenance parameters. The determination module is used to determine the time period during which the target device experiences the device failure event as the failure time period, determine at least one failure operation condition corresponding to the device failure event based on the device operation and maintenance parameters within the failure time period, and determine the time period during which the target device satisfies each of the failure operation conditions as the condition time period for each of the failure operation conditions. The determining module determines at least one fault operating condition corresponding to the equipment fault event through at least one of the following methods: determining multiple sampling time points within the fault time period, classifying the equipment operation and maintenance parameters corresponding to each sampling time point to obtain multiple fault operating conditions, wherein the equipment operation and maintenance parameters corresponding to each sampling time point are the same in any fault operating condition; determining multiple sampling time periods within the fault time period, determining the operation and maintenance parameter intervals corresponding to each sampling time period based on the equipment operation and maintenance parameters within each sampling time period, classifying the operation and maintenance parameter intervals corresponding to each sampling time period to obtain multiple fault operating conditions, wherein the operation and maintenance parameter intervals corresponding to each sampling time period are the same in any fault operating condition; The calculation module is used to calculate, based on the preset time period, the fault time period, and the conditional time period, the probability of the occurrence of the equipment fault event when the target equipment meets any fault operation condition. The acquisition module is used to acquire the real-time operating parameters of the target device, determine the target probability of the real-time operating parameters from the probability of fault occurrence based on the comparison relationship between the real-time operating parameters and the fault operating conditions, and determine the device maintenance status of the target device based on the target probability. The acquisition module determines the equipment maintenance status of the target device in the following ways: if the target probability is greater than or equal to a preset probability threshold, the equipment maintenance status of the target device is determined to require maintenance; the growth trend of the target probability within a preset time period is determined, and if the growth trend is greater than or equal to a preset trend threshold, the equipment maintenance status of the target device is determined to require maintenance, wherein the growth trend includes the growth value and / or the growth rate.

7. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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