Method and system for predicting residual service life of equipment, equipment, medium and product

Abstract

The invention discloses a method and system for predicting the remaining service life of equipment, the equipment, a medium and a product, and the method comprises the steps: obtaining the measurement point data of the equipment, and obtaining an original measurement record of a historical measurement point; processing the original measurement record to obtain an aggregation measurement record; selecting data from the aggregation measurement record to configure a configuration node for labeling; the accuracy of a sampling prediction result is judged through a sampling trial function; selecting the intelligent prediction algorithm with the highest prediction result accuracy from the plurality of intelligent prediction algorithms; the intelligent prediction algorithm calls the data of the aggregated measurement records for prediction, and obtains a prediction result; carrying out validity verification on the prediction result through a prediction model verification algorithm; and calculating the residual service life of the equipment according to a prediction result. Through the processing scheme disclosed by the invention, the residual service life of the equipment can be predicted, the potential fault time of the used equipment can be predicted in advance, and various losses caused by sudden faults of the equipment are prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of predicting the remaining service life of equipment, and in particular to a method and system, equipment, medium and product for predicting the remaining service life of equipment. Background Art

[0002] Sudden failure of running equipment will have a great impact on life and production. With the development of big data, Internet of Things and artificial intelligence technology, the equipment maintenance model has shifted from the previous preventive maintenance to predictive maintenance. At present, there are three main prediction methods: reliability statistics, physical model-based and data-driven.

[0003] Among them, reliability statistics require a large number of reliability experiments and long-term data statistics. It is suitable for large quantities of simple components, but not for expensive equipment and systems. The prediction method based on physical models establishes a mathematical model that reflects the physical laws of equipment performance degradation according to the internal working mechanism of the equipment, such as the equipment degradation model and the bathtub curve. It does not require a large amount of data, but is not suitable for complex electromechanical systems. It is extremely dependent on experts, too idealistic, and easily deviates from actual working conditions, resulting in a decrease in accuracy. The data-driven prediction method extracts features from historical data of condition monitoring data to infer future conditions or lifespan. It uses the numerical prediction capability of intelligent algorithms to predict the indicator trend of the equipment during future operation based on the historical operation data of the equipment, thereby achieving the purpose of predicting the remaining service life of the equipment when it reaches the future indicator trend.

[0004] The existing prediction methods cannot detect the time when potential equipment failures will occur in advance, and cannot predict the remaining service life of the equipment before the actual failure occurs to avoid the major impact caused by sudden equipment failures. It can be seen that the above existing prediction methods are obviously still inconvenient and defective in use and urgently need to be improved. However, in comparison, there are huge differences in the use cost and the applicable scope of the prediction equipment in the above existing prediction methods. How to create a new, low-cost, easy-to-implement, and widely applicable method that can predict the remaining service life of equipment has become a goal that the industry urgently needs to improve. Summary of the invention

[0005] In view of this, an embodiment of the present disclosure provides a method for predicting the remaining useful life of a device, which at least partially solves the problems existing in the prior art.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for predicting the remaining useful life of a device, the method comprising the following steps:

[0007] Obtain the equipment measurement point data and obtain the original measurement records of historical measurement points;

[0008] Cleaning the original measurement records to remove noise data, and aggregating them on a daily basis to obtain aggregated measurement records;

[0009] Selecting data from the aggregated measurement records according to different data types and configuring them into configuration nodes for marking;

[0010] The accuracy of sampling prediction results can be judged through the sampling trial calculation function;

[0011] Select the intelligent prediction algorithm with the highest prediction result accuracy from multiple intelligent prediction algorithms;

[0012] The intelligent prediction algorithm retrieves the data of the aggregated measurement record to perform prediction and obtain a prediction result;

[0013] The prediction results are verified for validity by using the prediction model verification algorithm, and the remaining service life of the equipment is calculated by using the prediction results.

[0014] According to a specific implementation of the embodiment of the present disclosure, the accuracy of the sampling prediction result is judged by the sampling trial calculation function, and the prediction model verification algorithm is performed, including:

[0015] Randomly select measurement records at N time points, use the value of the Nth measurement point as the first control value, and use the values ​​of the first N-1 measurement points to independently predict the second control value;

[0016] Comparing the first control value with the second control value to determine the accuracy of the sampling prediction result;

[0017] A prediction model verification algorithm is performed based on the first control value and the second control value, and an intelligent prediction algorithm matching the current measurement record is selected from a plurality of intelligent prediction algorithms.

[0018] According to a specific implementation of the embodiment of the present disclosure, the original measurement records are cleaned to remove noise data, and then aggregated by day to obtain aggregated measurement records, including:

[0019] IoT data and manually collected fusion data are cleaned and noise data is removed through ELT technology to obtain cleaned data, and the cleaned data is aggregated on a daily basis to obtain the daily maximum, minimum, average and absolute value of each measurement point;

[0020] The configuration node is used to mark the maximum value, minimum value, maximum offset and absolute value of the aggregate measurement record.

[0021] According to a specific implementation of the embodiment of the present disclosure, the sampling trial calculation function includes:

[0022] Comparing the first control value with the second control value, if the difference between the first control value and the second control value is large, the prediction result is inaccurate;

[0023] The prediction model verification algorithm includes:

[0024] A residual test algorithm and a posterior difference test algorithm, wherein the residual test algorithm reflects the relative gap between the first control value and the second control value; the posterior difference test algorithm reflects the degree of fit between the current prediction algorithm and the measurement record; the residual test algorithm and the posterior difference are cross-validated to select an intelligent prediction algorithm that matches the current measurement record.

[0025] According to a specific implementation method of an embodiment of the present disclosure, the multiple intelligent prediction algorithms are independently packaged pluggable Python algorithm packages, which package the multiple prediction algorithms, provide input interfaces and output interfaces, and form a pluggable Python algorithm package for addition or replacement.

[0026] According to a specific implementation of the embodiment of the present disclosure, the intelligent prediction algorithm includes:

[0027] Predicting based on the data of the aggregated measurement record to obtain a first operating value at a next time point of the current measurement point;

[0028] By means of a recursive loop, the obtained first running value is added to the aggregated measurement record, and the second running value at the next time point is measured again, until a terminal running value is predicted;

[0029] A threshold range is determined for the value of the termination operation value. When the termination operation value exceeds the threshold range, the prediction is stopped, and the last time point is obtained as the time point when the potential failure occurs, so as to obtain the remaining service life of the equipment.

[0030] In a second aspect, an embodiment of the present disclosure provides a system for predicting the remaining useful life of a device, the system comprising:

[0031] A control module is configured to obtain device measurement point data and obtain original measurement records of historical measurement points;

[0032] A data processing module is configured to process the original measurement records, clean the original measurement records to remove noise data, and aggregate the records by day to obtain aggregated measurement records;

[0033] The prediction parameter configuration module retrieves the aggregated measurement records after aggregation through the task scheduling control and orchestration center, and is used to select data from the aggregated measurement records according to different data types and configure them into configuration nodes for annotation;

[0034] The algorithm processing module is configured to use the sampling trial calculation function to determine the accuracy of the sampling prediction results, and select the intelligent prediction algorithm with the highest prediction result accuracy from multiple intelligent prediction algorithms;

[0035] A result acquisition module, used to obtain a prediction result obtained by an intelligent prediction algorithm calling the data of the aggregated measurement record for prediction;

[0036] The RUL calculation module, the prediction model verification algorithm verifies the validity of the prediction result, and calculates the remaining service life of the equipment through the prediction result.

[0037] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0038] at least one processor; and,

[0039] a memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor performs the method for predicting the remaining useful life of a device as described in any one of the first aspect or any one of the implementations of the first aspect.

[0041] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by at least one processor, the at least one processor executes the method for predicting the remaining useful life of a device in the aforementioned first aspect or any implementation of the first aspect.

[0042] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, and when the program instructions are executed by a computer, the computer executes the method for predicting the remaining useful life of a device in the aforementioned first aspect or any implementation of the first aspect.

[0043] The embodiments of the present invention have the following advantages:

[0044] The method for predicting the remaining service life of equipment in the disclosed embodiment obtains the equipment measurement point data, selects the intelligent prediction algorithm that matches the current measurement record from multiple intelligent prediction algorithms to predict the prediction result, and calculates the remaining service life of the equipment. When the enterprise uses this system and successfully predicts the remaining service life of the equipment, it can predict the potential failure time of the equipment in advance, so as to purchase and prepare spare parts in advance, adjust the maintenance strategy or manually intervene in the process, and prevent various losses caused by sudden equipment failure. On the one hand, it can greatly reduce the maintenance cost of previous preventive maintenance, and on the other hand, through the detection of the remaining service life, the goal of predicting failures in advance can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0046] Figure 1 A schematic diagram of a method flow for predicting the remaining useful life of equipment provided by an embodiment of the present disclosure;

[0047] Figure 2 A schematic diagram of a workflow of a system for predicting the remaining useful life of equipment provided by an embodiment of the present disclosure;

[0048] Figure 3 A schematic diagram of the structure of a system for predicting the remaining useful life of equipment provided by an embodiment of the present disclosure;

[0049] Figure 4 A flowchart of working modules of a system for predicting the remaining useful life of equipment provided by an embodiment of the present disclosure;

[0050] Figure 5 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0052] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0053] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0054] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0055] An embodiment of the present invention provides a method for predicting the remaining service life of equipment. The data-driven prediction method extracts features from historical data of status monitoring data to infer future conditions or lifespan, and uses the numerical prediction capability of intelligent algorithms to predict the indicator trend of the equipment during future operation based on the historical operation data of the equipment, thereby achieving the purpose of predicting the remaining service life of the equipment when it reaches the future indicator trend. When an enterprise uses this system and successfully predicts the remaining service life of the equipment, it can predict the potential failure time of the equipment in use in advance, thereby purchasing and preparing spare parts, adjusting maintenance strategies or manually intervening in advance, and preventing various losses caused by sudden equipment failures. This can greatly reduce the maintenance cost of previous preventive maintenance and achieve the goal of predicting failures in advance through the detection of the remaining service life.

[0056] Figure 1 A schematic flow chart of a method for predicting the remaining useful life of equipment provided in an embodiment of the present disclosure.

[0057] Figure 2 A schematic diagram of the workflow of a system for predicting the remaining useful life of equipment provided in an embodiment of the present disclosure.

[0058] like Figure 1 and Figure 2 As shown, the method comprises the following steps:

[0059] Step S110: Acquire the equipment measurement point data and obtain the original measurement records of the historical measurement points;

[0060] Step S120: cleaning the original measurement records to remove noise data, and performing daily aggregation processing to obtain aggregated measurement records;

[0061] Step S130: selecting data from the aggregated measurement records according to different data types and configuring them into configuration nodes for annotation;

[0062] Step S140: judging the accuracy of the sampling prediction results through the sampling trial calculation function;

[0063] Step S150: performing a prediction model verification algorithm to select the intelligent prediction algorithm with the highest prediction result accuracy from multiple intelligent prediction algorithms;

[0064] Step S160: the intelligent prediction algorithm retrieves the data of the aggregated measurement record to perform prediction and obtain a prediction result;

[0065] Step S170: verifying the validity of the prediction results through a prediction model verification algorithm, and calculating the remaining service life of the equipment through the prediction results.

[0066] In an embodiment of the present invention, in step S110, the equipment measurement point data is acquired to obtain the original measurement records of the historical measurement points; the equipment measurement point data is acquired through professional industrial control system collection and manual regular inspection, which can be used to predict the indicator trend of the equipment during future operation, thereby predicting the remaining service life of the equipment when it reaches the future indicator trend.

[0067] Specifically, in step S120, the original measurement records are cleaned to remove noise data, and the aggregated measurement records are obtained by daily aggregation processing, including: IoT data and manually collected fusion data are cleaned to remove noise data by ELT technology to obtain cleaned data, and the cleaned data are aggregated daily to obtain the maximum value, minimum value, average value and absolute value of each measurement point;

[0068] More specifically, in step S130, data is selected from the aggregated measurement record according to different data types and configured into a configuration node for marking. When it is necessary to predict the remaining life of a certain device, different devices refer to different data types of measurement points. Some devices refer to the maximum value, some refer to the minimum value, or refer to the maximum offset or take the absolute value of the measurement record for prediction. By providing a configuration node for marking its needs, the configuration node marks the maximum value, minimum value, maximum offset and absolute value of the aggregated measurement record for easy access.

[0069] More specifically, in step S140, the measurement records of N time points are randomly selected through the sampling trial calculation function, the value of the Nth measurement point is used as the first control value, and the values ​​of the first N-1 measurement points are used to independently predict the second control value; the first control value is compared with the second control value to determine the accuracy of the sampling prediction result; the sampling trial calculation function is provided to verify the accuracy of the intelligent algorithm, randomly select the measurement records of N time points, the Nth value is used as the control value, recorded as M, and the first N-1 values ​​are used to independently predict a new value, recorded as K. For system users, first, an intuitive feeling after the comparison between M and K can be provided. If the difference between M and K is large, the prediction is inaccurate.

[0070] More specifically, in step S150, a prediction model verification algorithm is performed based on the first control value and the second control value, and an intelligent prediction algorithm with the highest prediction result accuracy is selected from multiple intelligent prediction algorithms.

[0071] In step S150 of the embodiment of the present invention, the prediction model verification algorithm includes two prediction model verification algorithms, namely, a residual verification algorithm and a posterior difference verification algorithm. The residual verification algorithm reflects the relative difference between the first control value and the second control value; the posterior difference verification algorithm reflects the degree of fit between the current prediction algorithm and the measurement record; the residual verification algorithm and the posterior difference are cross-validated to select the intelligent prediction algorithm with the highest prediction result accuracy.

[0072] At step S150, multiple intelligent prediction algorithms are independently packaged pluggable Python algorithm packages. Multiple prediction algorithms are packaged, and input interfaces and output interfaces are provided to form pluggable Python algorithm packages for addition or replacement. The algorithms need to perform numerical predictions, so various prediction algorithms are simply packaged, and an input and output interface is provided to form pluggable Python algorithm packages. A certain algorithm can be easily added or replaced, and the model algorithm can be quickly replaced to adapt to different equipment scenarios and operating conditions.

[0073] In step S160 of the embodiment of the present invention, the intelligent prediction algorithm retrieves the data of the aggregated measurement record to perform prediction and obtain a prediction result. The steps of performing prediction by the intelligent prediction algorithm include:

[0074] Based on the data of the aggregated measurement records, a prediction is made to obtain the first running value at the next time point of the current measurement point;

[0075] By means of a recursive loop, the first running value is added to the aggregated measurement record, and the second running value at the next time point is measured again, until the terminal running value is predicted;

[0076] The threshold range is judged for the value of the termination operation value. When the termination operation value exceeds the threshold range, the prediction is stopped, and the last time point is obtained as the time point when the potential failure occurs, and the remaining service life of the equipment is obtained.

[0077] More specifically, in step S170, the prediction result is verified by the prediction model verification algorithm to obtain the model verification result, and the remaining service life of the equipment is calculated. For equipment in normal use, the value of any measurement point will remain within a reasonable threshold range. Once this threshold range is exceeded, it can be regarded as a potential fault, which requires timely maintenance or manual intervention.

[0078] The method for predicting the remaining service life of equipment proposed in the present invention is based on various intelligent budgeting algorithms for numerical values, which are converted to predict the remaining service life of the equipment in use. After this method successfully predicts the remaining service life of the equipment, the potential failure time of the equipment in use can be predicted in advance, so as to purchase and prepare spare parts in advance, adjust maintenance strategies or manually intervene to prevent various losses caused by sudden equipment failure.

[0079] Figure 3 and Figure 4 The system 400 for predicting the remaining service life of equipment provided by the present invention includes a control module 410 , a data processing module 420 , a prediction parameter configuration module 430 , an algorithm processing module 440 , a structure acquisition module 450 and a RUL calculation module 460 .

[0080] The control module 410 is configured to import IoT data, sample aggregated measurement records across data sources; obtain numerical prediction results and model verification results, calculate RUL to obtain prediction results, and obtain device measurement point data to obtain original measurement records of historical measurement points;

[0081] The data processing module 420 is configured to process the original measurement records, remove the noise data from the IoT data and the manually collected fusion data by cleaning to obtain the cleaned data, aggregate the cleaned data by day to obtain the aggregated measurement data, and the control module samples the data processing module;

[0082] The prediction parameter configuration module 430 retrieves the aggregated measurement records after aggregation through the task scheduling control and orchestration center, and is used to select data from the aggregated measurement records according to different data types and configure them into configuration nodes for annotation;

[0083] The algorithm processing module 440 is configured to use the intelligent prediction algorithm to predict the aggregated measurement records, and to determine the accuracy of the sampled prediction results through a sampling trial calculation function, and to select the intelligent prediction algorithm with the highest prediction result accuracy from multiple intelligent prediction algorithms;

[0084] The result acquisition module 450 is used to obtain the prediction result obtained by the intelligent prediction algorithm calling the data of the aggregated measurement records for prediction;

[0085] The RUL calculation module 460, the prediction model verification algorithm verifies the validity of the prediction result, and calculates the remaining service life of the equipment through the prediction result.

[0086] See also Figure 5 The present disclosure also provides an electronic device 50, which includes:

[0087] at least one processor; and,

[0088] a memory communicatively connected to the at least one processor; wherein,

[0089] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for predicting the remaining service life of a device in the aforementioned method embodiment.

[0090] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the method of predicting the remaining service life of a device in the aforementioned method embodiment.

[0091] An embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, cause the computer to execute the method for predicting the remaining useful life of a device in the aforementioned method embodiment.

[0092] Reference below Figure 5, which shows a schematic diagram of the structure of an electronic device 50 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 5 As shown, the electronic device 50 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 50 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0094] Typically, the following devices may be connected to the I / O interface 505: input devices 506 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 508 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 509. The communication device 509 may allow the electronic device 50 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 50 with various devices is shown in the figure, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0095] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0096] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0097] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0098] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0099] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0100] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0101] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0103] It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0104] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for predicting the remaining useful life of equipment, characterized in that: The method comprises the following steps: Obtain the equipment measurement point data and obtain the original measurement records of historical measurement points; Cleaning the original measurement records to remove noise data, and aggregating them on a daily basis to obtain aggregated measurement records; Selecting data from the aggregated measurement records according to different data types and configuring them into configuration nodes for marking; The accuracy of sampling prediction results can be judged through the sampling trial calculation function; Select the intelligent prediction algorithm with the highest prediction result accuracy from multiple intelligent prediction algorithms; The intelligent prediction algorithm retrieves the data of the aggregated measurement record to perform prediction and obtain a prediction result; The prediction results are verified for validity by using the prediction model verification algorithm, and the remaining service life of the equipment is calculated by using the prediction results.

2. The method for predicting the remaining useful life of equipment according to claim 1, characterized in that: The accuracy of the sampling prediction results is judged by the sampling trial calculation function, and the prediction model verification algorithm is performed, including: Randomly select measurement records at N time points, use the value of the Nth measurement point as the first control value, and use the values ​​of the first N-1 measurement points to independently predict the second control value; Comparing the first control value with the second control value to determine the accuracy of the sampling prediction result; A prediction model verification algorithm is performed based on the first control value and the second control value, and an intelligent prediction algorithm matching the current measurement record is selected from a plurality of intelligent prediction algorithms.

3. The method for predicting the remaining useful life of equipment according to claim 1, characterized in that: The original measurement records are cleaned to remove noise data, and then aggregated by day to obtain aggregated measurement records, including: IoT data and manually collected fusion data are cleaned and noise data is removed through ELT technology to obtain cleaned data, and the cleaned data is aggregated on a daily basis to obtain the daily maximum, minimum, average and absolute value of each measurement point; The configuration node is used to mark the maximum value, minimum value, maximum offset and absolute value of the aggregate measurement record.

4. The method for predicting the remaining useful life of equipment according to claim 2, characterized in that: The sampling calculation function includes: Comparing the first control value with the second control value, if the difference between the first control value and the second control value is large, the prediction result is inaccurate; The prediction model verification algorithm includes: A residual test algorithm and a posterior difference test algorithm, wherein the residual test algorithm reflects the relative gap between the first control value and the second control value; the posterior difference test algorithm reflects the degree of fit between the current prediction algorithm and the measurement record; the residual test algorithm and the posterior difference are cross-validated to select an intelligent prediction algorithm that matches the current measurement record.

5. The method for predicting the remaining useful life of equipment according to claim 4, characterized in that: The multiple intelligent prediction algorithms are independently packaged pluggable Python algorithm packages, which package the multiple prediction algorithms, provide input interfaces and output interfaces, and form a pluggable Python algorithm package for addition or replacement.

6. The method for predicting the remaining useful life of equipment according to claim 1, characterized in that: The intelligent prediction algorithm includes: Predicting based on the data of the aggregated measurement record to obtain a first operating value at a next time point of the current measurement point; By means of a recursive loop, the obtained first running value is added to the aggregated measurement record, and the second running value at the next time point is measured again, until a terminal running value is predicted; A threshold range is determined for the value of the termination operation value. When the termination operation value exceeds the threshold range, the prediction is stopped, and the last time point is obtained as the time point when the potential failure occurs, so as to obtain the remaining service life of the equipment.

7. A system for predicting the remaining useful life of equipment, characterized in that: The system comprises: A control module is configured to obtain device measurement point data and obtain original measurement records of historical measurement points; A data processing module is configured to process the original measurement records, clean the original measurement records to remove noise data, and aggregate the records by day to obtain aggregated measurement records; The prediction parameter configuration module retrieves the aggregated measurement records after aggregation through the task scheduling control and orchestration center, and is used to select data from the aggregated measurement records according to different data types and configure them into configuration nodes for annotation; The algorithm processing module is configured to use the sampling trial calculation function to determine the accuracy of the sampling prediction results, and select the intelligent prediction algorithm with the highest prediction result accuracy from multiple intelligent prediction algorithms; A result acquisition module, used to obtain a prediction result obtained by an intelligent prediction algorithm calling the data of the aggregated measurement record for prediction; The RUL calculation module, the prediction model verification algorithm verifies the validity of the prediction result, and calculates the remaining service life of the equipment through the prediction result.

8. An electronic device, characterized in that: The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes the method for predicting the remaining service life of a device as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when executed by at least one processor, cause the at least one processor to perform the method for predicting the remaining useful life of a device as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program / instruction, which, when executed by a processor, implements the steps of the method for predicting the remaining service life of a device as claimed in any one of claims 1 to 6.

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