CN116384959A

CN116384959A - Data processing method, device, computer equipment and storage medium

Info

Publication number: CN116384959A
Application number: CN202211716790.4A
Authority: CN
Inventors: 刘胜举; 赵艳军; 王雨田; 王子
Original assignee: Zhengzhou Sikun Biological Engineering Co ltd
Current assignee: Zhengzhou Sikun Biological Engineering Co ltd
Priority date: 2022-12-29
Filing date: 2022-12-29
Publication date: 2023-07-04

Abstract

The present disclosure provides a data processing method, apparatus, computer device, and storage medium, where the method includes: acquiring an operation log file corresponding to a target device; the operation log file contains operation information of the target device operated according to the historical execution instruction; analyzing the operation log file and determining the corresponding use parameter information of the target device; wherein the usage parameter information is related to a lifetime of the target device; and determining a loss evaluation result corresponding to the target device based on the use parameter information corresponding to the target device.

Description

Data processing method, device, computer equipment and storage medium

Technical Field

The disclosure relates to the field of computer technology, and in particular, to a data processing method, a device 5, computer equipment and a storage medium.

Background

In the industrial production process, mechanical equipment is needed to produce products, the mechanical equipment is often composed of different mechanical devices, and the mechanical devices can be worn and the like as the production progresses

And the mechanical device is consumed to a certain extent, so that the mechanical device needs to be replaced, and the normal production is not affected by 0 degree.

In the related art, a manufacturer of mechanical equipment often gives a service life parameter of a mechanical device, for example, the service life parameter of a certain device may be 1 year, and then the device needs to be replaced after being used for 1 year, but the device may not be completely worn when the device is replaced, so that the device may be wasted.

Disclosure of Invention

The embodiment of the disclosure at least provides a data processing method, a data processing device, computer equipment and a storage medium.

In a first aspect, an embodiment of the present disclosure provides a data processing method, including: 0, acquiring an operation log file corresponding to the target device; the operation log file contains operation information of the target device operated according to the historical execution instruction;

analyzing the operation log file and determining the corresponding use parameter information of the target device; wherein the usage parameter information is related to a lifetime of the target device;

and determining a loss evaluation result corresponding to the target device based on the use parameter information corresponding to the target device.

In a possible implementation manner, before the operation log file corresponding to the target device is acquired, the method further includes:

determining that the target device meets a loss evaluation condition;

wherein the loss evaluation condition includes:

receiving a loss evaluation instruction for the target device; or alternatively, the process may be performed,

the operation time length of the target device meets the requirement of a preset time length; or alternatively, the process may be performed,

the time interval from the last loss evaluation of the target device meets a preset time interval requirement.

In a possible embodiment, in the case that the target device is a pump, the usage parameter information includes at least one of the following parameters:

total number of suction and discharge, number of suction actions, total suction volume, total number of pump rotations, total pump rotation stroke.

In a possible embodiment, in the case that the target device is a valve, the usage parameter information includes at least one of the following parameters:

the total number of valve rotations, the total valve rotation stroke, the number of clockwise rotations, the clockwise rotation stroke, the number of counterclockwise rotations, the counterclockwise rotation stroke.

In a possible implementation manner, the determining, based on the usage parameter information corresponding to the target device, a loss evaluation result corresponding to the target device includes:

Obtaining standard parameter values corresponding to the use parameter information;

and under the condition that the parameter value of any one of the using parameter information exceeds the corresponding standard parameter value, determining the loss evaluation result corresponding to the target device as loss excess.

In a possible embodiment, the method further comprises:

and under the condition that the loss evaluation result is normal loss, determining a residual service life estimation result corresponding to the target device based on the use parameter information and the operation log file.

In a possible implementation manner, the determining, based on the usage parameter information and the running log file, a remaining service life estimation result corresponding to the target device includes:

analyzing the operation log file, and determining the corresponding use frequency of each piece of use parameter information;

and inputting the parameter values of the using parameter information and the using frequencies corresponding to the using parameter information into a pre-trained target network model, and determining the residual service life estimated result corresponding to the target device.

In a possible embodiment, the method further comprises training the target network model according to the steps of:

acquiring sample use parameter information, sample use frequency and sample labels of a sample device; the sample tag is used for representing the residual service life corresponding to the sample device under the use conditions of the sample use parameter information and the sample use frequency;

Inputting the sample use parameter information and the sample use frequency into a target network model to be trained, and obtaining a residual service life estimated result corresponding to the sample device output by the target network model;

and determining a target loss value of the training based on the residual service life estimated result corresponding to the sample device and the sample label, and adjusting network parameters of a target network model to be trained based on the target loss value.

In a second aspect, an embodiment of the present disclosure further provides a data processing apparatus, including:

the acquisition module is used for acquiring the operation log file corresponding to the target device; the operation log file contains operation information of the target device operated according to the historical execution instruction;

the first determining module is used for analyzing the operation log file and determining the use parameter information corresponding to the target device; wherein the usage parameter information is related to a lifetime of the target device;

and the second determining module is used for determining a loss evaluation result corresponding to the target device based on the use parameter information corresponding to the target device.

In a possible implementation manner, before the acquiring the running log file corresponding to the target device, the acquiring module further includes:

Determining that the target device meets a loss evaluation condition;

wherein the loss evaluation condition includes:

In a possible implementation manner, the second determining module is configured to, when determining, based on the usage parameter information corresponding to the target device, a loss evaluation result corresponding to the target device:

In a possible implementation manner, the second determining module is further configured to:

In a possible implementation manner, the second determining module is configured to, when determining, based on the usage parameter information and the running log file, a residual service life estimation result corresponding to the target device:

In a possible implementation manner, the second determining module is further configured to train the target network model according to the following steps:

In a third aspect, embodiments of the present disclosure further provide a computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect, or any of the possible implementations of the first aspect.

In a fourth aspect, the presently disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the first aspect, or any of the possible implementations of the first aspect.

According to the data processing method, the data processing device, the computer equipment and the storage medium, the usage parameter information related to the service life of the target device can be determined by analyzing the operation log file corresponding to the obtained target device, so that the loss evaluation result corresponding to the target device can be determined based on the usage parameter information. Therefore, compared with the situation that the device is replaced by directly using the theoretical life, the device is replaced by analyzing the operation log file, the use parameter information which can better represent the actual use condition of the device can be obtained, so that a more accurate actual loss evaluation result can be determined based on the use parameter information, more accurate data support is provided for the replacement of the device, the waste of the device can be avoided, and the use efficiency of the device is improved.

The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for the embodiments are briefly described below, which are incorporated in and constitute a part of the specification, these drawings showing embodiments consistent with the present disclosure and together with the description serve to illustrate the technical solutions of the present disclosure. It is to be understood that the following drawings illustrate only certain embodiments of the present disclosure and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may admit to other equally relevant drawings without inventive effort.

FIG. 1 illustrates a flow chart of a data processing method provided by an embodiment of the present disclosure;

FIG. 2 illustrates a schematic architecture of a data processing apparatus provided by an embodiment of the present disclosure;

fig. 3 shows a schematic structural diagram of a computer device according to an embodiment of the disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments.

The components of the disclosed embodiments, which are generally described and illustrated in the figures herein, may be arranged and designed in a variety of different configurations 5. Thus, the following detailed description of the embodiments of the present disclosure provided in the accompanying drawings is not intended to limit the scope of the disclosure, as claimed, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be made by those skilled in the art based on the embodiments of this disclosure without making any inventive effort, are intended to be within the scope of this disclosure.

It should be noted that: like reference numerals and letters refer to like items in the following figures, and thus, a 0 denier item is defined in one figure, no further definition or explanation thereof is necessary in subsequent figures.

The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: separately exist A, simultaneously exist A and B, separately exist

In three cases B. In addition, the term "at least one" herein means any one of a plurality 5 or any combination of at least two of a plurality, e.g., including at least one of A, B, C, may be

To represent a representation comprising any one or more elements selected from the group consisting of A, B and C.

It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.

0, for example, upon receiving an active request from a user, a prompt is sent to the user to

The user is explicitly prompted that his request for the performance of an operation will require the acquisition and use of personal information to the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.

5 as an alternative but non-limiting implementation manner, in response to receiving an active request from a user, the manner of sending the prompt information to the user may be, for example, a popup window, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.

It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.

According to research, a manufacturer of mechanical equipment often gives service life parameters of mechanical devices, and in order to ensure that the mechanical devices can still be normally used in the whole service life period under a high-strength working condition, the service life given by the manufacturer is often conservative, for example, the actual service life of a certain device can be 2 years, but in order to ensure that the device can still be normally used in the whole service life period under the high-strength working condition, the manufacturer can set the service life parameters of the device to 1 year, which means that the device needs to be replaced after being used for 1 year, but because the service frequencies of the device are different in different factories, the device can not be completely lost when the device replacement time corresponding to the service life parameters is reached, and the device can be normally used, and if the device is replaced at this time, the device is wasted.

Based on the above study, the disclosure provides a data processing method, a device, a computer device and a storage medium, which can determine usage parameter information related to the service life of a target device by analyzing an operation log file corresponding to the obtained target device, so that a loss evaluation result corresponding to the target device can be determined based on the usage parameter information. Therefore, compared with the situation that the device is replaced by directly using the theoretical life, the device is replaced by analyzing the operation log file, the use parameter information which can better represent the actual use condition of the device can be obtained, so that a more accurate actual loss evaluation result can be determined based on the use parameter information, more accurate data support is provided for the replacement of the device, the waste of the device can be avoided, and the use efficiency of the device is improved.

For the sake of understanding the present embodiment, first, a detailed description will be given of a data processing method disclosed in an embodiment of the present disclosure, where an execution body of the data processing method provided in the embodiment of the present disclosure is generally a computer device having a certain computing capability, where the computer device includes, for example: the terminal device or server or other processing device may be a User Equipment (UE), a mobile device, a User terminal, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like. In some possible implementations, the data processing method may be implemented by way of a processor invoking computer readable instructions stored in a memory.

Referring to fig. 1, a flowchart of a data processing method according to an embodiment of the disclosure is shown, where the method includes S101 to S103, where:

s101: acquiring an operation log file corresponding to a target device; the operation log file contains operation information of the target device operated according to the historical execution instruction.

S102: analyzing the operation log file and determining the corresponding use parameter information of the target device; wherein the usage parameter information is related to a lifetime of the target device.

S103: and determining a loss evaluation result corresponding to the target device based on the use parameter information corresponding to the target device.

The following is a detailed description of the above steps.

Aiming at S101,

The target device may be a mechanical device, for example, a pump, a valve, etc.; the operation log file can be used for recording operation information in the operation process of the target device; the operation information of the target device according to the operation of the historical execution instruction can comprise the use parameter information corresponding to the historical execution instruction and the execution result corresponding to the historical execution instruction.

The usage parameter information will be described in detail below, and will not be further described herein.

For example, the usage parameter information corresponding to the history execution instruction may be rotated 2 circles clockwise, including two usage parameter information of "clockwise rotation" and "rotation travel 2 circles", and the execution result corresponding to the history execution instruction may be that the execution is successful.

In a possible implementation manner, before the operation log file corresponding to the target device is obtained, whether the target device meets the loss evaluation condition or not may be further determined, if the target device is determined to meet the loss evaluation condition, the operation log file corresponding to the target device may be obtained, and loss evaluation may be performed on the target device according to a subsequent step.

Wherein the loss evaluation condition may include at least one of the following conditions:

condition 1, receipt of loss evaluation instruction for the target device

Here, the loss evaluation instruction is used for instructing to perform loss evaluation on the target device, and after receiving the loss evaluation instruction for the target device, it can be determined that the target device meets a loss evaluation condition, so that the loss evaluation can be performed on the target device according to a subsequent step.

Condition 2, the operation time length of the target device meets the preset time length requirement

Here, the operation time period of the target device may be an operation time period calculated from the time when the target device is installed; the preset duration requirement may be that the operation duration reaches a preset duration, and if the operation duration of the target device reaches the preset duration, it may be determined that the target device meets a loss evaluation condition, so that loss evaluation may be performed on the target device according to a subsequent step.

For example, taking the preset time length of 5000 hours as an example, when the operation time length of the target device is detected to reach 5000 hours, it may be determined that the operation time length of the target device meets the preset time length requirement, and it is determined that the target device meets the loss evaluation condition, so that loss evaluation can be performed on the target device according to subsequent steps.

Condition 3, the time interval from the last loss evaluation of the target device meets the preset time interval requirement

Here, the preset time interval requirement may be that the time interval from the last loss evaluation reaches a preset time interval, and if the time interval from the last loss evaluation of the target device reaches a preset time interval, it may be determined that the target device meets a loss evaluation condition, so that loss evaluation may be performed on the target device according to a subsequent step.

For example, taking the preset time interval as 1000 hours as an example, in the case that the time interval from the last loss evaluation of the target device is detected to reach 1000 hours, it may be determined that the time interval from the last loss evaluation of the target device meets the preset time interval requirement, and it is determined that the target device meets the loss evaluation condition, so that loss evaluation may be performed on the target device according to the subsequent steps.

Therefore, by presetting the loss evaluation conditions, the loss evaluation can be automatically performed on the target device under the condition that the target device meets the loss evaluation conditions, and the efficiency of the loss evaluation in the actual application process can be improved.

Aiming at S102,

Here, the usage parameter information may be parameter information related to the lifetime of the device, for example, may include fatigue parameters indicating the degree of loss of the device, and the like.

Specifically, when the running log file is parsed, a target script may be called to parse the running log file, where the target script may be, for example, a script written in Python language.

In practical applications, the usage parameter information related to the lifetime of the target device may also be different in case the target device is of different kinds.

Next, usage parameter information corresponding to each of the different types of target devices will be described:

type 1 pump

Here, in the case where the target device is a pump, the usage parameter information includes at least one of the following parameters:

Wherein the total number of suction and discharge is used to characterize the sum of the number of times the pump performs a suction operation and a discharge operation, which may be performed for a gas or a liquid; the number of inhalation actions is used for representing the number of times the pump performs an inhalation operation; the total volume of inhalation is used to characterize the total volume of gas or liquid inhaled when performing the inhalation operation; the total number of pump rotations is used for representing the number of times the valve head of the pump rotates in the running process; the total pump rotational travel is used to characterize the travel of the valve head of the pump as it rotates during operation.

Type 2 valve

Here, in the case where the target device is a valve, the usage parameter information includes at least one of the following parameters:

Wherein the total number of valve rotations is used to characterize the number of valve rotations during operation; the total valve rotation stroke is used for representing the rotation stroke of the valve in the running process; the clockwise rotation times are used for representing the clockwise rotation times of the valve in the running process; the clockwise rotation stroke is used for representing the stroke of the valve when the valve rotates clockwise in the running process; the counter-clockwise rotation times are used for representing the counter-clockwise rotation times of the valve in the running process; the counterclockwise rotational travel is used to characterize the travel of the valve as it rotates counterclockwise during operation.

In this way, by analyzing the operation log file, the usage parameter information related to the service life of the target device is determined, and the loss evaluation result of the target device can be determined by using the usage parameter information according to the subsequent steps.

For S103,

Here, the loss evaluation result corresponding to the target device is used for representing the loss degree corresponding to the target device, and the loss result may include loss excess used for representing the device needing to be replaced and normal loss used for representing that the current loss degree of the target device is in a normal use range.

In practical application, because the usage parameter information corresponding to the target device can be used for representing the usage degree of the target device, the loss evaluation result corresponding to the target device can be determined based on the usage parameter information corresponding to the target device.

In a possible implementation manner, when determining the loss evaluation result corresponding to the target device based on the usage parameter information corresponding to the target device, the following steps A1-A2 may be used:

a1: and obtaining standard parameter values corresponding to the using parameter information.

Here, for any piece of usage parameter information, the standard parameter value corresponding to the usage parameter information may be used to represent the loss excessive threshold corresponding to the usage parameter information, and if the parameter value corresponding to the usage parameter information exceeds the standard parameter value, it may be indicated that the target device has loss excessive in the dimension corresponding to the usage parameter information.

Specifically, the usage parameter information corresponding to the target device may include a plurality of usage parameter information, and when determining the loss evaluation result corresponding to the target device, standard parameter values corresponding to the usage parameter information respectively may be obtained.

A2: and under the condition that the parameter value of any one of the using parameter information exceeds the corresponding standard parameter value, determining the loss evaluation result corresponding to the target device as loss excess.

For example, with the target device as the pump, the usage parameter information includes a total number of pump rotations, the standard parameter value corresponding to the usage parameter information "total number of pump rotations" is 100000 times, and if the parameter value corresponding to the usage parameter information "total number of pump rotations" is 110000 times, and exceeds the standard parameter value corresponding to the usage parameter information "total number of pump rotations", it may be determined that the loss evaluation result corresponding to the target device "pump" is loss excessive.

Further, the loss evaluation result corresponding to the target device may further include a loss excessive cause.

Specifically, when the parameter value of any one of the usage parameter information exceeds the corresponding standard parameter value, the usage parameter information may be used as a loss excess factor corresponding to the target device.

Further, in the case that the loss evaluation result is a normal loss, the remaining service life estimation result corresponding to the target device may be determined based on the usage parameter information and the running log file.

In a possible implementation manner, when determining the estimated remaining service life corresponding to the target device, the following steps B1 to B2 may be adopted:

b1: analyzing the operation log file and determining the corresponding use frequency of each use parameter information.

Here, the usage frequency is used to characterize a frequency at which the target device operates according to the usage parameter information, and taking the target device as a pump as an example, and the usage frequency corresponding to the usage parameter information "the number of times of suction operation" may be a frequency at which the pump performs suction operation.

Specifically, the running log file may further include execution time corresponding to each historical execution instruction, and the use frequency corresponding to each use parameter information is determined according to the execution time corresponding to each historical execution instruction and the running information corresponding to each historical execution instruction.

B2: and inputting the parameter values of the using parameter information and the using frequencies corresponding to the using parameter information into a pre-trained target network model, and determining the residual service life estimated result corresponding to the target device.

Here, the input of the target network model is a parameter value of the usage parameter information corresponding to the target device, and the usage frequency corresponding to each usage parameter, and the target network model may determine the type of the target device according to the type corresponding to the input usage parameter information; the output of the target network model is the residual service life estimated result corresponding to the target device; the network type of the target network model may be a neural network capable of deep learning, for example, a convolutional neural network (Convolutional Neural Networks, CNN).

In the following, it will be described how the target network is trained:

in one possible implementation, the target network model may be trained by the following steps C1-C3:

c1: acquiring sample use parameter information, sample use frequency and sample labels of a sample device; the sample tag is used for representing the residual life corresponding to the sample device under the conditions of the sample use parameter information and the sample use frequency.

C2: and inputting the sample use parameter information and the sample use frequency into a target network model to be trained, and obtaining a residual service life estimated result corresponding to the sample device output by the target network model.

And C3: and determining a target loss value of the training based on the residual service life estimated result corresponding to the sample device and the sample label, and adjusting network parameters of a target network model to be trained based on the target loss value.

Here, in determining the target loss value, the target loss value of the current training may be determined based on the residual life prediction result corresponding to the sample device, the sample tag, and a preset target loss function, where the target loss value is used to characterize a difference between the residual life prediction result corresponding to the sample device and the residual life corresponding to the sample device in the sample tag.

In this way, through a pre-trained target network model, the residual service life estimation result corresponding to the target device can be automatically estimated according to the data input into the target network model, so that the replacement time of the target device can be determined according to the residual service life estimation result, and the target device can be detected or replaced according to the replacement time.

It should be noted that, the information type of the input data used in determining the residual service life estimation result by the target network model is only exemplary, and the factors affecting the residual service life of the target device in practical application may also include other factors such as environmental factors, so that the input data of the target network model may also include other information such as environmental information corresponding to the target device, where the environmental information may include, for example, temperature information, humidity information, air pressure information, and the like of an actual operating environment where the target device is located; correspondingly, because the input data of the target network model are different in information type, the input data, the sample label and the method for determining the target loss value during training the target network model can be adaptively adjusted, and specific training modes can refer to the related content and are not described herein.

According to the data processing method provided by the embodiment of the disclosure, the acquired operation log file corresponding to the target device is analyzed, so that the use parameter information related to the service life of the target device can be determined, and the loss evaluation result corresponding to the target device can be determined based on the use parameter information. Therefore, compared with the situation that the device is replaced by directly using the theoretical life, the device is replaced by analyzing the operation log file, the use parameter information which can better represent the actual use condition of the device can be obtained, so that a more accurate actual loss evaluation result can be determined based on the use parameter information, more accurate data support is provided for the replacement of the device, the waste of the device can be avoided, and the use efficiency of the device is improved.

It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.

Based on the same inventive concept, the embodiments of the present disclosure further provide a data processing device corresponding to the data processing method, and since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to that of the data processing method in the embodiments of the present disclosure, the implementation of the device may refer to the implementation of the method, and the repetition is omitted.

Referring to fig. 2, a schematic architecture diagram of a data processing apparatus according to an embodiment of the disclosure is provided, where the apparatus includes: an acquisition module 201, a first determination module 202, a second determination module 203; wherein, the liquid crystal display device comprises a liquid crystal display device,

an obtaining module 201, configured to obtain an operation log file corresponding to a target device; the operation log file contains operation information of the target device operated according to the historical execution instruction;

a first determining module 202, configured to parse the running log file, and determine usage parameter information corresponding to the target device; wherein the usage parameter information is related to a lifetime of the target device;

and the second determining module 203 is configured to determine a loss evaluation result corresponding to the target device based on the usage parameter information corresponding to the target device.

In a possible implementation manner, before the acquiring the running log file corresponding to the target device, the acquiring module 201 further includes:

determining that the target device meets a loss evaluation condition;

wherein the loss evaluation condition includes:

In a possible implementation manner, the second determining module 203 is configured to, when determining, based on the usage parameter information corresponding to the target device, a loss evaluation result corresponding to the target device:

In a possible implementation manner, the second determining module 203 is further configured to:

In a possible implementation manner, the second determining module 203 is configured to, when determining, based on the usage parameter information and the running log file, a remaining service life estimation result corresponding to the target device:

In a possible implementation manner, the second determining module 203 is further configured to train the target network model according to the following steps:

According to the data processing device provided by the embodiment of the disclosure, the acquired operation log file corresponding to the target device is analyzed, so that the use parameter information related to the service life of the target device can be determined, and the loss evaluation result corresponding to the target device can be determined based on the use parameter information. Therefore, compared with the situation that the device is replaced by directly using the theoretical life, the device is replaced by analyzing the operation log file, the use parameter information which can better represent the actual use condition of the device can be obtained, so that a more accurate actual loss evaluation result can be determined based on the use parameter information, more accurate data support is provided for the replacement of the device, the waste of the device can be avoided, and the use efficiency of the device is improved.

The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.

Based on the same technical concept, the embodiment of the disclosure also provides computer equipment. Referring to fig. 3, a schematic diagram of a computer device 300 according to an embodiment of the disclosure includes a processor 301, a memory 302, and a bus 303. The memory 302 is configured to store execution instructions, including a memory 3021 and an external memory 3022; the memory 3021 is also referred to as an internal memory, and is used for temporarily storing operation data in the processor 301 and data exchanged with the external memory 3022 such as a hard disk, and the processor 301 exchanges data with the external memory 3022 through the memory 3021, and when the computer device 300 operates, the processor 301 and the memory 302 communicate with each other through the bus 303, so that the processor 301 executes the following instructions:

acquiring an operation log file corresponding to a target device; the operation log file contains operation information of the target device operated according to the historical execution instruction;

In a possible implementation manner, the instructions of the processor 301, before obtaining the operation log file corresponding to the target device, further include:

determining that the target device meets a loss evaluation condition;

wherein the loss evaluation condition includes:

In a possible implementation manner, in the instruction of the processor 301, in a case where the target device is a pump, the usage parameter information includes at least one of the following parameters:

In a possible implementation manner, in the instruction of the processor 301, in a case where the target device is a valve, the usage parameter information includes at least one of the following parameters:

In a possible implementation manner, in the instruction of the processor 301, the determining, based on the usage parameter information corresponding to the target device, a loss evaluation result corresponding to the target device includes:

In a possible implementation manner, the instructions of the processor 301 further include:

In a possible implementation manner, in the instructions of the processor 301, the determining, based on the usage parameter information and the running log file, a remaining service life estimation result corresponding to the target device includes:

In a possible implementation manner, the instructions of the processor 301 further include training the target network model according to the following steps:

The disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the data processing method described in the method embodiments above. Wherein the storage medium may be a volatile or nonvolatile computer readable storage medium.

Embodiments of the present disclosure further provide a computer program product, where the computer program product carries program code, where instructions included in the program code may be used to perform steps of a data processing method described in the foregoing method embodiments, and specifically reference may be made to the foregoing method embodiments, which are not described herein.

Wherein the above-mentioned computer program product may be realized in particular by means of hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied as a computer storage medium, and in another alternative embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), or the like.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present disclosure, and are not intended to limit the scope of the disclosure, but the present disclosure is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, it is not limited to the disclosure: any person skilled in the art, within the technical scope of the disclosure of the present disclosure, may modify or easily conceive changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features thereof; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A method of data processing, comprising:

2. The method of claim 1, wherein prior to obtaining the log file corresponding to the target device, the method further comprises:

determining that the target device meets a loss evaluation condition;

wherein the loss evaluation condition includes:

3. The method according to claim 1, wherein in case the target device is a pump, the usage parameter information comprises at least one of the following parameters:

4. The method according to claim 1, wherein determining the loss evaluation result corresponding to the target device based on the usage parameter information corresponding to the target device includes:

5. The method according to claim 1, wherein the method further comprises:

6. The method of claim 5, wherein determining a remaining lifetime prediction corresponding to the target device based on the usage parameter information and the operation log file comprises:

7. The method of claim 6, further comprising training the target network model according to the steps of:

8. A data processing apparatus, comprising:

9. A computer device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via the bus when the computer device is running, said machine readable instructions when executed by said processor performing the steps of the data processing method according to any of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when run by a processor, performs the steps of the data processing method according to any of claims 1 to 7.