A plant equipment informatization system and method based on internet of things technology

Abstract

This invention relates to the field of equipment information technology, and in particular to a factory equipment information system and method based on Internet of Things (IoT) technology, comprising: establishing a fault prediction model: the fault prediction model is obtained from historical fault data through a multinomial fitting method; establishing a fault correlation model: the fault correlation model is calculated from historical fault data and relevant weights; the fault prediction model can obtain the status of the component to be queried in real time, and according to its current status and working environment, query the corresponding fitting curve in the fault prediction model, thereby obtaining the predicted fault time of the component; if the equipment is maintained during the predicted fault time, the status of the component has changed, and it is re-acquired by the fault prediction model to obtain a new predicted fault time.

Description

Technical Field

[0001] This invention relates to the field of equipment information technology, and in particular to a factory equipment information system and method based on Internet of Things (IoT) technology. Background Technology

[0002] Enterprise information management is a crucial strategic resource for economic and social development, and more and more companies are focusing on it. Within enterprises, equipment is a vital component of productivity, holding a significant position both in terms of asset ownership and the scope of management tasks. While some companies have begun using equipment management information systems as enterprise informatization progresses, most domestic software still focuses on managing static information such as equipment records, failing to achieve dynamic management throughout the entire equipment lifecycle. Therefore, developing a comprehensive equipment management information system is imperative.

[0003] Existing technology CN112100447A proposes a factory equipment information system based on Internet of Things (IoT) technology, which includes a distributed sensing module, a transmission module, and a server. The server includes an indicator value determination module, a sensing information importance evaluation module, and a sensing information storage module. The distributed sensing module includes several sensors installed in different locations within the factory, used to collect operating status sensing information of factory equipment in corresponding locations. The transmission module transmits the operating status sensing information to the server via an IoT network. The indicator value determination module determines a queue storage location indicator value for the operating status sensing information based on the queue storage location indicator value. The sensing information storage module stores all operating status sensing information transmitted from the transmission module in the form of an information queue based on the queue storage location indicator value. However, it only proposes a method for collecting information from factory equipment.

[0004] To address this, a factory equipment information system and method based on Internet of Things (IoT) technology is proposed. Summary of the Invention

[0005] In view of this, the present invention aims to provide a factory equipment information system and method based on Internet of Things (IoT) technology to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option;

[0006] The technical solution of this invention is implemented as follows: Firstly, a method for information management of factory equipment based on Internet of Things (IoT) technology, comprising:

[0007] Establish a fault prediction model: The fault prediction model is obtained from historical fault data through a polynomial fitting method;

[0008] Establish a fault correlation model: The fault correlation model is calculated from historical fault data and relevant weights;

[0009] Methods for predicting equipment failures include the following steps:

[0010] S1: Obtain the status of the first component inside the equipment, and substitute the status of the first component into the fault prediction model to predict the time when it will fail under normal operation in real time;

[0011] S2: If the equipment has been maintained, a first maintenance factor is formed based on the degree of maintenance;

[0012] S3: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure.

[0013] S4: Substitute the first maintenance factor of the second component into the fault correlation model to obtain the second maintenance factor for the first component;

[0014] S5: Substitute the second maintenance factor into the fault prediction model, change the operating conditions of the first component, and obtain the new time of failure.

[0015] Furthermore, in the aforementioned method for information management of factory equipment based on Internet of Things (IoT) technology, the method for establishing a fault prediction model includes:

[0016] Collect the factors that caused the failures from the historical failure data of the first component, and classify them according to different types as the original feature factors;

[0017] The original feature factors are classified according to their degree of specificity, and these are used as the graded original feature factors.

[0018] Collect historical fault data to determine the time when the fault occurred;

[0019] Using the original hierarchical feature factors as constants, the time when the fault occurred was recorded and tabulated. A fault prediction model was then obtained using a multinomial fitting formula.

[0020]

[0021] Where A, B, and C are constants, X is the state of the first component, and Y is the predicted state of the first component.

[0022] Furthermore, the aforementioned method for information management of factory equipment based on Internet of Things technology generates different values ​​for constants A, B, and C according to the different hierarchical original feature factors, and each set of constants A, B, and C corresponds one-to-one with the hierarchical original feature factors.

[0023] Furthermore, in the aforementioned method for information management of factory equipment based on Internet of Things (IoT) technology, the step of establishing a fault correlation model includes:

[0024] A single-factor fuzzy matrix is ​​constructed. Experts score the impact of the second component on the first component. A higher score indicates a greater impact of the second component on the operation of the first component, meaning that a change in the state of the second component alters the operating environment of the first component. The method for calculating the relevant weight of the second component on the first component is as follows:

[0025] Let the expert pool be: The scoring result is set as follows: If the expert weights are all β, then:

[0026]

[0027] The formula indicates that the expert's score is out of 10, and Experts For the second component For the first component The extent of the impact ;

[0028] Let a single-factor fuzzy matrix be used. If the comprehensive evaluation of the collected fuzzy matrix is ​​S, then:

[0029]

[0030] W is the weight vector.

[0031] Furthermore, in the aforementioned method for information management of factory equipment based on Internet of Things (IoT) technology, the method for confirming the maintenance level in step S2 includes:

[0032] Calculate the available working hours and the required working hours separately;

[0033] Determine the relationship between available working hours and required working hours;

[0034] Calculate the maintenance and support coefficient;

[0035] Determine the capability weights and coefficients;

[0036] Calculate the level of maintenance and support.

[0037] Furthermore, the aforementioned method for information management of factory equipment based on Internet of Things (IoT) technology includes, in step S4:

[0038] The first component and the second component are related components, and the operating state of the second component determines the operating environment of the first component.

[0039] In another aspect, a factory equipment information system based on Internet of Things (IoT) technology is applied to the aforementioned factory equipment information method based on IoT technology, the system comprising:

[0040] The first construction module collects the factors that cause failures from the historical failure data of the first component and classifies them according to different types as original feature factors; it then classifies the original feature factors according to their specificity as graded original feature factors; it collects the time of failure from the historical failure data; it uses the graded original feature factors as constants, records the time of failure occurrence, and creates a table; and it uses a multinomial fitting formula to obtain a failure prediction model.

[0041] The second construction module: establishing a fault correlation model: the fault correlation model is calculated from historical fault data and relevant weights;

[0042] Acquisition module: Acquires the status of the first component within the device and substitutes the status of the first component into the fault prediction model to predict in real time the time when it will fail under normal operation;

[0043] Judgment module: Determines whether the equipment has undergone maintenance;

[0044] First Substitution Module: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure.

[0045] Second Substitution Module: Substitute the first maintenance factor of the second component into the fault-related model to obtain the second maintenance factor for the first component;

[0046] The third substitution module: Substitutes the second maintenance factor into the fault prediction model, changes the operating conditions of the first component, and obtains the new time of failure.

[0047] Calculation module: After the state of the first component changes, calculate the new time of failure.

[0048] Furthermore, in the aforementioned factory equipment information system based on Internet of Things (IoT) technology, the second building module further includes:

[0049] Construction Unit: Construct a single-factor fuzzy matrix.

[0050] Scoring Unit: Experts score the impact of the second component on the first component. A higher score indicates a greater impact of the second component on the operation of the first component.

[0051] Calculation unit: After the state of the second component changes, the operating environment of the first component also changes, and the calculation unit calculates the relevant weight of the second component on the first component.

[0052] Another aspect provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of any of the methods for information management of factory equipment based on Internet of Things technology.

[0053] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods for information management of factory equipment based on Internet of Things (IoT) technology.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] I. The fault prediction model proposed in this invention evaluates the information of internal components of equipment based on historical data, anticipates the wear and tear of these components as working time increases, and thus predicts when a component will fail, enabling condition-based maintenance. During the fault prediction process: the fault prediction model can obtain the status of the component to be queried in real time. Based on its current status and working environment, it retrieves the corresponding fitting curve in the fault prediction model, thereby obtaining the predicted fault time for that component. If the equipment undergoes maintenance during the predicted fault time, the status of the component changes, and it is re-acquired by the fault prediction model, resulting in a new predicted fault time. Simultaneously, during maintenance, components related to the operation of the component may also be maintained, meaning the component's working environment (operating conditions) also changes. Substituting these changes back into the fault prediction model yields different fitting curves, leading to new predicted fault times.

[0056] Second, the fault correlation model proposed in this invention is based on the correlation between components during operation, which further improves the accuracy of the fault prediction model. By constructing a single-factor fuzzy matrix, experts score the degree of influence of the second component on the first component in the equipment. The higher the score, the greater the influence of the second component on the operation of the first component. That is, after the state of the second component changes, the operating environment of the first component also changes. This changes the operating environment of the first component, and the first component corresponds to different fitting curves in the fault prediction model, thus obtaining a new fault prediction time. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1This is a flowchart illustrating a factory equipment information management method based on Internet of Things (IoT) technology according to the present invention.

[0059] Figure 2 This is a schematic diagram of a factory equipment information system based on Internet of Things (IoT) technology according to the present invention;

[0060] Figure 3 This is a fitting curve diagram of an embodiment of a factory equipment information method based on Internet of Things technology according to the present invention;

[0061] Figure 4 This is a schematic diagram of the structure of a computer device for a factory equipment information system based on Internet of Things (IoT) technology, according to the present invention. Detailed Implementation

[0062] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below;

[0063] It is important to note that terms such as "first," "second," "symmetric," and "array" are used only to distinguish between descriptive and positional descriptions and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified with terms such as "first" or "symmetric" may explicitly or implicitly include one or more of that feature; similarly, when the quantity of certain features is not limited by words such as "two" or "three," it should be noted that such features also explicitly or implicitly include one or more features.

[0064] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature. Simultaneously, all axial descriptions, such as the X-axis, Y-axis, Z-axis, one end of the X-axis, the other end of the Y-axis, or the other end of the Z-axis, are based on the Cartesian coordinate system.

[0065] In this invention, unless otherwise explicitly specified and limited, terms such as "installation," "connection," and "fixation" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral molding; they can refer to a mechanical connection, a direct connection, a welding connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the accompanying drawings and specific circumstances.

[0066] With the improvement of information processing capabilities and the development of computer technology, various sensors, and the refinement of advanced control theories such as expert systems and fuzzy control theory, equipment fault diagnosis algorithms have begun to be studied and developed in depth. However, existing equipment fault diagnosis methods are still relatively simple, and the parameters they rely on require specially designed sensors for acquisition, resulting in unsatisfactory fault diagnosis results and a limited number of fault types that can be diagnosed. Therefore, please refer to... Figure 1-4 The present invention provides a technical solution to solve the above-mentioned technical problems: a factory equipment information system based on Internet of Things technology and its usage method;

[0067] In some specific embodiments of this application, please refer to the relevant documents. Figure 1 :

[0068] A method for information management of factory equipment based on Internet of Things (IoT) technology includes:

[0069] Establish a fault prediction model: The fault prediction model is obtained from historical fault data through a polynomial fitting method;

[0070] Establish a fault correlation model: The fault correlation model is calculated from historical fault data and relevant weights;

[0071] Methods for predicting equipment failures include the following steps:

[0072] S1: Obtain the status of the first component inside the equipment, and substitute the status of the first component into the fault prediction model to predict the time when it will fail under normal operation in real time;

[0073] S2: If the equipment has been maintained, a first maintenance factor is formed based on the degree of maintenance;

[0074] S3: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure.

[0075] S4: Substitute the first maintenance factor of the second component into the fault correlation model to obtain the second maintenance factor for the first component;

[0076] S5: Substitute the second maintenance factor into the fault prediction model, change the operating conditions of the first component, and obtain the new time of failure.

[0077] In this embodiment, the fault prediction model evaluates the information of internal components of the equipment based on historical data, anticipates the wear and tear of these components as working time increases, and predicts when a component will fail, thus enabling condition-based maintenance. The fault correlation model further improves the accuracy of the fault prediction model based on the correlation between components during operation. During the fault prediction process: the fault prediction model can obtain the status of the component to be queried in real time, and based on its current status and working environment, retrieve the corresponding fitting curve in the fault prediction model to obtain the predicted fault time of the component. If the equipment undergoes maintenance during the predicted fault time, the status of the component changes, and it is re-acquired by the fault prediction model to obtain a new predicted fault time. At the same time, during the maintenance process, components related to the operation of the component may also be maintained, that is, the working environment (operating conditions) of the component also changes. Substituting these changes back into the fault prediction model yields different fitting curves, which can then be used to obtain a new predicted fault time.

[0078] In practical implementation, failure mode analysis (FMEA) is employed in the establishment of the fault prediction model. Through both theoretical analysis and post-accident analysis, a fault model database for the entire system and its individual components is gradually enriched and improved based on typical faults of major equipment. Machine learning and deep learning methods are used to analyze and reason about the relationship between the field database and the fault model database, gradually achieving fault diagnosis and prediction. The results of fault diagnosis and prediction are used to manage the after-sales maintenance and support system, gradually realizing condition-based maintenance and comprehensive logistical support. Simultaneously, a single-factor fuzzy matrix is ​​constructed in the fault-related model. Experts score the impact of a second component on a first component; a higher score indicates a greater impact of the second component on the operation of the first component, meaning that a change in the state of the second component alters the operating environment of the first component. This changes the operating environment of the first component, resulting in different fitting curves for the first component in the fault prediction model, leading to new fault prediction times.

[0079] Furthermore, the method for establishing the fault prediction model includes:

[0080] Collect the factors that caused the failures from the historical failure data of the first component, and classify them according to different types as the original feature factors;

[0081] The original feature factors are classified according to their degree of specificity, and these are used as the graded original feature factors.

[0082] Collect historical fault data to determine the time when the fault occurred;

[0083] Using the original hierarchical feature factors as constants, the time when the fault occurred was recorded and tabulated. A fault prediction model was then obtained using a multinomial fitting formula.

[0084]

[0085] Wherein, A, B, and C are constants, X is the state of the first component, and Y is the predicted state of the first component; the values ​​of the generated constants A, B, and C are different depending on the different original feature factors of the classification, and each set of constants A, B, and C corresponds one-to-one with the original feature factors of the classification.

[0086] In one embodiment, taking a milling cutter in an automated machine tool as an example, under the condition that other operating environments are the same, the data table of time (in units of 10 hours) and status values ​​is shown in Table 1:

[0087] Table 1

[0088]

[0089] The fault prediction model formed under these conditions is:

[0090]

[0091] Formation as Figure 3 The fitted curve has constants A, B, and C of -0.0003, -0.0072, and 0.9981, respectively.

[0092] When the current state of the milling cutter is obtained, its failure prediction time can be obtained. In this embodiment, when the state value of the milling cutter is less than 0.8, the product produced will have problems, proving that the milling cutter has failed. If the state value of the milling cutter is 0.94 at this time, it can be predicted that the milling cutter will fail after 90 hours.

[0093] Furthermore, the step of establishing the fault-related model includes:

[0094] A single-factor fuzzy matrix is ​​constructed. Experts score the impact of the second component on the first component. A higher score indicates a greater impact of the second component on the operation of the first component, meaning that a change in the state of the second component alters the operating environment of the first component. The method for calculating the relevant weight of the second component on the first component is as follows:

[0095] Let the expert pool be: The scoring result is set as follows: If the expert weights are all β, then:

[0096]

[0097] The formula indicates that the expert's score is out of 10, and Experts For the second component For the first component The extent of the impact ;

[0098] Let a single-factor fuzzy matrix be used. If the comprehensive evaluation of the collected fuzzy matrix is ​​S, then:

[0099]

[0100] W is the weight vector.

[0101] In this embodiment, the components in the device need to be connected to each other or interact with each other to affect each other. During the operation of one component, the different states of another component will change its operating environment. For example, the speed of a motor is 100 r / s, but if the motor connecting bearings or gears have problems such as poor fit or gear wear, the motor speed will decrease or even fail. During the operation of the device, the various components need to work together. Therefore, when predicting the failure of a single component, it is also necessary to obtain the state of its related components in order to improve the operating environment of the component in the whole device.

[0102] In one embodiment, a milling cutter in the equipment is used as an example. The milling cutter is used to cut on the product surface. Normal malfunctions occur due to tool wear. The tool movement control system is a component related to the milling cutter. If the tool movement control system deviates on the Z-axis, the milling cutter will move closer to or further away from the product. When it moves closer to the product, the contact area between the milling cutter and the product increases, resulting in a deeper initial cut, but the milling cutter wears more easily. In this case, the movement system and tool wear are positively correlated. When it moves further away from the product, the contact area between the milling cutter and the product decreases, resulting in a shallower initial cut, and the milling cutter wears less easily. In this case, the movement system and tool wear are negatively correlated. (Single-factor fuzzy matrix) The "2" in the equation refers to the positive and negative correlation characteristics between the two components. If the moving system experiences errors on the X or Y axis, it will not affect the wear rate of the milling cutter, and is therefore an unrelated characteristic, which will not be considered. In this embodiment, a fault correlation model can be used.

[0103] Furthermore, in step S2, the method for confirming the degree of repair includes:

[0104] Calculate the available working hours and the required working hours separately;

[0105] Determine the relationship between available working hours and required working hours;

[0106] Calculate the maintenance and support coefficient;

[0107] Determine the capability weights and coefficients;

[0108] Calculate the level of maintenance and support.

[0109] Furthermore, step S4 includes:

[0110] The first component and the second component are related components, and the operating state of the second component determines the operating environment of the first component.

[0111] In another aspect, a factory equipment information system based on Internet of Things (IoT) technology is applied to the aforementioned factory equipment information method based on IoT technology, the system comprising:

[0112] The first construction module collects the factors that cause failures from the historical failure data of the first component and classifies them according to different types as original feature factors; it then classifies the original feature factors according to their specificity as graded original feature factors; it collects the time of failure from the historical failure data; it uses the graded original feature factors as constants, records the time of failure occurrence, and creates a table; and it uses a multinomial fitting formula to obtain a failure prediction model.

[0113] The second construction module: establishing a fault correlation model: the fault correlation model is calculated from historical fault data and relevant weights;

[0114] Acquisition module: Acquires the status of the first component within the device and substitutes the status of the first component into the fault prediction model to predict in real time the time when it will fail under normal operation;

[0115] Judgment module: Determines whether the equipment has undergone maintenance;

[0116] First Substitution Module: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure.

[0117] Second Substitution Module: Substitute the first maintenance factor of the second component into the fault-related model to obtain the second maintenance factor for the first component;

[0118] The third substitution module: Substitutes the second maintenance factor into the fault prediction model, changes the operating conditions of the first component, and obtains the new time of failure.

[0119] Calculation module: After the state of the first component changes, calculate the new time of failure.

[0120] Furthermore, the second building module also includes:

[0121] Construction Unit: Construct a single-factor fuzzy matrix.

[0122] Scoring Unit: Experts score the impact of the second component on the first component. A higher score indicates a greater impact of the second component on the operation of the first component.

[0123] Calculation unit: After the state of the second component changes, the operating environment of the first component also changes, and the calculation unit calculates the relevant weight of the second component on the first component.

[0124] Reference Figure 4 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 4 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores historical fault data and other data. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a factory equipment information management method based on Internet of Things (IoT) technology.

[0125] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0126] One embodiment of this application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of a factory equipment informatization method based on Internet of Things (IoT) technology, specifically:

[0127] Establish a fault prediction model: The fault prediction model is obtained from historical fault data through a polynomial fitting method;

[0128] Establish a fault correlation model: The fault correlation model is calculated from historical fault data and relevant weights;

[0129] Methods for predicting equipment failures include the following steps:

[0130] S1: Obtain the status of the first component inside the equipment, and substitute the status of the first component into the fault prediction model to predict the time when it will fail under normal operation in real time;

[0131] S2: If the equipment has been maintained, a first maintenance factor is formed based on the degree of maintenance;

[0132] S3: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure.

[0133] S4: Substitute the first maintenance factor of the second component into the fault correlation model to obtain the second maintenance factor for the first component;

[0134] S5: Substitute the second maintenance factor into the fault prediction model, change the operating conditions of the first component, and obtain the new time of failure.

[0135] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0136] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0137] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0138] Although embodiments of this application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for information management of factory equipment based on Internet of Things (IoT) technology, characterized in that, include: Establish a fault prediction model: The fault prediction model is obtained from historical fault data through a polynomial fitting method; Establish a fault correlation model: The fault correlation model is calculated from historical fault data and relevant weights; Methods for predicting equipment failures include the following steps: S1: Obtain the status of the first component inside the equipment, and substitute the status of the first component into the fault prediction model to predict the time when it will fail under normal operation in real time; S2: If the equipment has been maintained, a first maintenance factor is formed based on the degree of maintenance; S3: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure. S4: Substitute the first maintenance factor of the second component into the fault correlation model to obtain the second maintenance factor for the first component; S5: Substitute the second maintenance factor into the fault prediction model, change the operating conditions of the first component, and obtain the new time of failure. The method for establishing the fault prediction model includes: Collect the factors that caused the failures from the historical failure data of the first component, and classify them according to different types as the original feature factors; The original feature factors are classified according to their degree of specificity, and these are used as the graded original feature factors. Collect historical fault data to determine the time when the fault occurred; Using the original hierarchical feature factors as constants, the time when the fault occurred was recorded and tabulated. A fault prediction model was then obtained using a multinomial fitting formula. ； Where A, B, and C are constants, X is the state of the first component, and Y is the predicted state of the first component; The steps for establishing the fault correlation model include: A single-factor fuzzy matrix is ​​constructed. Experts score the impact of the second component on the first component. A higher score indicates a greater impact of the second component on the operation of the first component, meaning that a change in the state of the second component alters the operating environment of the first component. The method for calculating the relevant weight of the second component on the first component is as follows: Let the expert pool be: The scoring result is set as follows: If the expert weights are all β, then: ； The formula indicates that the expert's score is out of 10, and Experts For the second component For the first component The extent of the impact ; Let a single-factor fuzzy matrix be used. If the comprehensive evaluation of the collected fuzzy matrix is ​​S, then: ； W is the weight vector.

2. The method for information management of factory equipment based on Internet of Things (IoT) technology according to claim 1, characterized in that: Depending on the different original feature factors of the classification, the values ​​of the generated constants A, B, and C are also different, and each set of constants A, B, and C corresponds one-to-one with the original feature factors of the classification.

3. The method for information management of factory equipment based on Internet of Things (IoT) technology according to claim 1, characterized in that: In step S2, the method for confirming the degree of repair includes: Calculate the available working hours and the required working hours separately; Determine the relationship between available working hours and required working hours; Calculate the maintenance and support coefficient; Determine the capability weights and coefficients; Calculate the level of maintenance and support.

4. The method for information management of factory equipment based on Internet of Things (IoT) technology according to claim 1, characterized in that, Step S4 includes: The first component and the second component are related components, and the operating state of the second component determines the operating environment of the first component.

5. A factory equipment information system based on Internet of Things (IoT) technology, characterized in that, The system, which is applied to the factory equipment information method based on Internet of Things (IoT) technology according to any one of claims 1-4, comprises: The first construction module collects the factors that cause failures from the historical failure data of the first component and classifies them according to different types as original feature factors; it then classifies the original feature factors according to their specificity as graded original feature factors; it collects the time of failure from the historical failure data; it uses the graded original feature factors as constants, records the time of failure occurrence, and creates a table; and it uses a multinomial fitting formula to obtain a failure prediction model. The second construction module: establishing a fault correlation model: the fault correlation model is calculated from historical fault data and relevant weights; Acquisition module: Acquires the status of the first component within the device and substitutes the status of the first component into the fault prediction model to predict in real time the time when it will fail under normal operation; Judgment module: Determines whether the equipment has undergone maintenance; First Substitution Module: Substitute the first maintenance factor into the fault prediction model, change the state of the first component, and obtain the new time of failure. Second Substitution Module: Substitute the first maintenance factor of the second component into the fault-related model to obtain the second maintenance factor for the first component; The third substitution module: Substitutes the second maintenance factor into the fault prediction model, changes the operating conditions of the first component, and obtains the new time of failure. Calculation module: After the state of the first component changes, calculate the new time of failure.

6. The factory equipment information system based on Internet of Things technology according to claim 5, characterized in that, The second building module also includes: Construction Unit: Construct a single-factor fuzzy matrix. Scoring Unit: Experts score the impact of the second component on the first component of the equipment. A higher score indicates a greater impact of the second component on the operation of the first component. Calculation unit: After the state of the second component changes, the operating environment of the first component also changes, and the calculation unit calculates the relevant weight of the second component on the first component.

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