CN116611808A - Maintenance method and device for controlled equipment, control terminal and storage medium - Google Patents
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Abstract
The embodiment of the application relates to the technical field of equipment overhaul, and particularly provides an overhaul method and device for controlled equipment, a control terminal and a storage medium. The method comprises the following steps: acquiring historical operation data of controlled equipment and first operation data operated at the current moment; predicting second operation data corresponding to the current moment according to the historical operation data and the data prediction model; calculating data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from historical operation data; judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error; and when the first operation data is abnormal data, determining a target overhaul scheme of the controlled equipment, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme. And the corresponding overhaul scheme is obtained rapidly by analyzing the operation data of the controlled equipment, so that the normal operation of the controlled equipment is ensured.
Description
Technical Field
The present application relates to the field of equipment maintenance, and in particular, to a method and apparatus for maintaining a controlled device, a control terminal, and a storage medium.
Background
In the current industrial field, there are problems that the complexity and the number of devices are increased, the maintenance of the devices tends to be complicated, a great deal of labor cost is generally required to be consumed from the problem locating device to the problem locating device, and when each device is in different areas, the maintenance personnel of the devices generally need to run on the places of each device to perform primary analysis and deep analysis on the devices. And when the number of the devices is large, more time is required for determining and solving the fault problem, the normal operation of the whole system of the devices is seriously influenced, and the working efficiency and the working capacity of the whole working system are reduced.
Thus, there is a need for a method that can quickly locate malfunctioning devices and address the problem of malfunctions.
Disclosure of Invention
The embodiment of the application mainly aims to provide a method, a device, a control terminal and a storage medium for overhauling controlled equipment, and aims to rapidly acquire an overhauling scheme corresponding to abnormal data by analyzing operation data of the controlled equipment, improve overhauling efficiency of the controlled equipment and working efficiency of an overall working system, ensure normal operation of the controlled equipment, thereby improving productivity of the controlled equipment and reducing cost investment for overhauling the controlled equipment.
In a first aspect, an embodiment of the present application provides a method for overhauling a controlled device, which is applied to a control terminal, including:
and acquiring historical operation data of the controlled equipment and first operation data operated at the current moment.
And predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and the data prediction model.
And calculating the data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from the historical operation data.
And judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error.
When the first operation data is abnormal data, determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme.
In a second aspect, an embodiment of the present application further provides an overhaul apparatus for a controlled device, including:
the data acquisition module is used for acquiring historical operation data of the controlled equipment and first operation data operated at the current moment.
And the data prediction module is used for predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and the data prediction model.
The data analysis module is used for calculating the data errors of the first operation data and the second operation data and acquiring a target historical data sequence associated with the current moment from the historical operation data.
And the data judging module is used for judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error.
And the scheme determining module is used for determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data when the first operation data is abnormal data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme.
In a third aspect, an embodiment of the present application further provides a control terminal, where the control terminal includes a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for implementing a connection communication between the processor and the memory, where the computer program, when executed by the processor, implements the steps of the method for overhauling any one of the controlled devices as provided in the specification of the present application.
In a fourth aspect, an embodiment of the present application further provides a storage medium for computer readable storage, wherein the storage medium stores one or more programs executable by one or more processors to implement steps of a method for servicing a controlled apparatus as provided in any of the present specification.
The embodiment of the application provides a method, a device, a control terminal and a storage medium for overhauling controlled equipment, wherein the method comprises the steps of acquiring historical operation data of the controlled equipment and first operation data operated at the current moment; predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and the data prediction model; calculating data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from historical operation data; judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error; when the first operation data is abnormal data, determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme. By detecting the operation data of the controlled equipment in real time, analyzing according to the historical operation data of the controlled equipment and the operation data at the current moment, judging whether the controlled equipment is abnormal, and sending a corresponding maintenance scheme to the controlled equipment when the controlled equipment is abnormal, the abnormal condition of the controlled equipment can be responded quickly, the cost output in equipment maintenance is reduced, the maintenance efficiency of the controlled equipment and the working efficiency of the whole working system are improved, and accordingly the productivity of the controlled equipment is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an overhaul method of a controlled device according to an embodiment of the present application;
fig. 2 is a schematic diagram of a relationship between a controlled device and a control terminal according to an embodiment of the present application;
fig. 3 is a schematic diagram of a relationship between a controlled device, a control terminal, an interpreter and a scheme database according to an embodiment of the present application;
fig. 4 is a schematic block diagram of an overhaul device of a controlled device according to an embodiment of the present application;
fig. 5 is a schematic block diagram of a control terminal according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The embodiment of the application provides a method and a device for overhauling controlled equipment, a control terminal and a storage medium. The overhauling method of the controlled equipment can be applied to a control terminal, wherein the control terminal can be a mobile phone, a tablet computer, a notebook computer, a personal digital assistant, a wearable device or a server, and the server can be an independent server, a server cluster or a cloud server.
The embodiment of the application provides a method, a device, a control terminal and a storage medium for overhauling controlled equipment, wherein the method is applied to the control terminal and is used for acquiring historical operation data of the controlled equipment and first operation data operated at the current moment; predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and the data prediction model; calculating data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from historical operation data; judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error; when the first operation data is abnormal data, determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme. By detecting the operation data of the controlled equipment in real time, analyzing according to the historical operation data of the controlled equipment and the operation data at the current moment, judging whether the controlled equipment is abnormal, and sending a corresponding maintenance scheme to the controlled equipment when the controlled equipment is abnormal, the abnormal condition of the controlled equipment can be responded quickly, the cost output in equipment maintenance is reduced, the maintenance efficiency of the controlled equipment and the working efficiency of the whole working system are improved, and accordingly the productivity of the controlled equipment is improved.
Some embodiments of the application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of an overhaul method of a controlled device according to an embodiment of the present application.
As shown in fig. 1, the overhaul method of the controlled apparatus includes steps S1 to S5.
Step S1: and acquiring historical operation data of the controlled equipment and first operation data operated at the current moment.
Illustratively, in the current industry, there are problems in that the complexity of equipment and the number of equipment are increasing, the complexity of maintenance of equipment tends to be complex, and a lot of labor cost is generally required to solve the problem from locating a specific equipment to locating the equipment. And because of the maintenance requirements of all the devices, the maintenance personnel of the devices usually need to travel to the places where the devices are located to perform primary analysis and deep analysis on the devices.
As shown in fig. 2, 10 is denoted as a control terminal, may be a cloud server, and 20 is denoted as location information of controlled device deployment, may be different machine rooms in the same area, or may be different areas. 30, wherein A, B, C represents different controlled devices. Therefore, when the controlled equipment is deployed more and distributed in different areas, the overhaul difficulty of the controlled equipment is greatly increased.
Therefore, the historical operation data of the controlled equipment and the first operation data operated at the current moment can be obtained according to the control terminal, and further, the controlled equipment can be analyzed and judged in real time according to the historical operation data and the first operation data of the controlled equipment, and further, the problems can be rapidly located and solved.
For example, when the first operation data in the controlled device includes an operation temperature of the controlled device, an operation log of the controlled device, and the like, whether the controlled device operates normally or not may be overhauled according to the historical operation temperature, the historical operation log, and the operation temperature and the operation log at the current time of the controlled device.
In addition, when the first operation data of the controlled equipment at the current moment is obtained, the data can be obtained according to a preset time interval, so that the operation pressure of the control terminal is reduced. For example, when the controlled device is an initially deployed device, the time interval at which the controlled device acquires the first operation data of the current time operation may be set to half an hour, and the time interval may be adjusted to 1 day or 2 days, or the like, along with the stability of the controlled device operation. If the controlled equipment runs for a long time, the time interval can be reduced to prevent the internal components in the controlled equipment from aging and causing problems, so that when the first running data of the current moment in the controlled equipment is acquired, the time interval between the current moment and the next moment can be set according to requirements.
Step S2: and predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and the data prediction model.
The data prediction model is obtained by training according to historical operation data of the controlled equipment, and second operation data corresponding to the current moment is predicted according to the data prediction model.
For example, the historical operation data includes the operation time of the controlled device and the operation temperature corresponding to the operation time, and then the data regression can be performed on the historical operation data according to the regression model to obtain a data prediction model, and then the current time is input into the data prediction model to obtain second operation data corresponding to the current time.
In some embodiments, the data prediction model includes an LSTM network, and predicting, according to the historical operation data and the data prediction model, second operation data corresponding to a current time of the controlled device includes: carrying out standardization processing on the historical operation data to obtain standardized data corresponding to the historical operation data; inputting the standardized data into the LSTM network of the data prediction model to perform model training, so as to obtain model parameters of the data prediction model; and obtaining second operation data corresponding to the current moment of the controlled equipment according to the model parameters.
Illustratively, the data prediction model includes an LSTM network. Firstly, converting historical operation data into a certain range for normalization processing to obtain standardized data. And (3) taking the standardized data as training data, inputting the training data into an LSTM network for model training, and obtaining model parameters corresponding to a data prediction model. And transmitting the time corresponding to the current moment into a data prediction model, and obtaining second operation data corresponding to the current moment of the controlled equipment according to the model parameters. The data prediction model performs normalization processing on the historical operation data during training, so that inverse normalization processing is needed after the current time is input into the data prediction model to obtain a corresponding result.
For example, the historical training data is (t 1, x 1), (t 2, x 2), (tn, xn), then the maximum and minimum values of x1, x2, xn are obtained, and the historical training data is converted to [0,1 ] according to the maximum max and minimum min]And further, normalized data is obtained as shown in formula (1). In addition, data prediction is performed on the data at the current moment according to the model parameters of the data prediction model to obtain a data prediction resultThen, inverse normalization is carried out by utilizing the maximum value max and the minimum value min to obtain second operation data +. >As shown in equation (2).
And step S3, calculating the data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from the historical operation data.
Illustratively, the first operational data and the second operational data are subtracted and taken as absolute values to obtain a data error between the first operational data and the second operational data. And screening normal historical operation data corresponding to continuous normal operation time of the controlled equipment from the historical operation data, and solving a data error of the operation data between two connected time in the normal historical operation data to further obtain a target historical data sequence associated with the current time.
For example, the historical operation data includes 30 days of operation data, wherein the historical operation data from the 3 rd day to the 10 th day is the data of the normal operation of the controlled device, and if the current time is 3 pm on the 31 st day, the data in any time period from the 3 rd day to the 10 th day before the 3 pm can be obtained as the normal historical operation data corresponding to the current time. Assuming that (t 1, x 1), (t 2, x 2), (tn, xn) is included in the normal history operation data, the target history data sequence is (t 21, abs (x 2-x 1)), (t 32, abs (x 3-x 2)), (tn (n-1), abs (xn-x (n-1))).
In some embodiments, the historical operation data includes operation time and third operation data corresponding to the operation time, and the obtaining, from the historical operation data, a target historical data sequence associated with the current time includes: determining a sliding region, and determining a corresponding operation time range in the historical operation data according to the current moment and the sliding region, wherein the sliding region is used for representing the time length selected from the historical operation data; selecting the third operation data corresponding to the operation time when the operation time satisfies the operation time range from the historical operation data; determining an initial historical data sequence corresponding to the current moment according to the running time and the third running data; and determining a difference value between the third operation data of adjacent operation time in the initial historical data sequence according to the initial historical data sequence, and further determining a target historical data sequence associated with the current moment.
The historical operation data includes operation time and third operation data corresponding to the operation time. And determining the data quantity extracted from the historical operation data, further determining a sliding area, and determining the operation time range in the historical operation data according to the data in the range of the sliding area extracted forward at the current moment. And obtaining third operation data meeting the operation time from the historical operation data according to the operation time range in the historical operation data, and further obtaining an initial historical data sequence corresponding to the current moment. And carrying out difference operation between third operation data of adjacent operation time in the initial historical data sequence, and further carrying out target historical data sequence associated with the current moment.
For example, when the current time is t, if the data amount to be extracted is 100, and the time interval of the historical operation data of the controlled device stored in the historical operation data is 3s, the sliding area is (t-100×3, t-1*3), and then the operation time range in the historical operation data can be obtained according to the sliding area.
And S4, judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error.
The absolute value difference between the data error and each value in the target historical data sequence is calculated, the sum of the absolute value differences is obtained, and when the sum of the errors is larger than or equal to a preset sum of the errors, the first operation data is judged to be abnormal data.
For example, the target historical data sequence is e1, e2, & gt, en, the data error is err, the error sum between the data error and the target historical data sequence is abs (e 1-err) +abs (e 2-err) +the & gt, abs (en-err), and if the preset error sum is set to 13, the first operation data is judged to be normal data when the error sum is less than 13; when the error sum is greater than or equal to 13, the first operation data is judged to be abnormal data.
Or comparing the data error with each value in the target historical data sequence to obtain a duty ratio of the data error larger than the median of the target historical data sequence, and judging the first operation data as abnormal data when the duty ratio is larger than a preset duty ratio.
For example, the target historical data sequence is e1, e2, & gt, en, the data error is err, the comparison result between the data error and the target historical data sequence is e1> err, e2< err, & gt, en < err, if the number of the data error is 10 when the data error is larger than the median of the target historical data sequence, the data quantity of the target historical data sequence is 100, the preset accounting ratio is 60%, the accounting ratio of the data error is 10% when the data error is larger than the median of the target historical data sequence, and the first operation data is judged to be normal data; if the number of the data errors is 70 and the data quantity of the target historical data sequence is 100, the ratio of the data errors to the median of the target historical data sequence is 70% and the ratio of the data errors to the median of the target historical data sequence is 60%, and the first operation data is judged to be abnormal data.
In some embodiments, the determining whether the first operational data is anomalous data based on the target historical data sequence and the data error includes: obtaining a normal distribution model corresponding to the target historical data sequence according to the target historical data sequence; determining a probability density value corresponding to the data error according to the normal distribution model; and determining whether the first operation data is abnormal data or not according to the probability density value and a preset condition.
The method includes the steps that a normal distribution model is established by utilizing a target historical data sequence, a probability density value corresponding to a data error is obtained according to the normal distribution model, the first operation data is judged to be abnormal data when the probability density value is larger than or equal to a preset condition, and the first operation data is judged to be normal data when the probability density value is smaller than the preset condition. The probability density value represents the degree of deviation of the data error from the data in the target historical data sequence.
For example, if the preset condition is set to 0.6, the probability density value corresponding to the data error is 0.7, which indicates that the first operation data is an abnormal value.
In some embodiments, the obtaining the normal distribution model corresponding to the target historical data sequence according to the target historical data sequence includes: determining a variance and a standard deviation corresponding to the target historical data sequence according to the target historical data sequence; and determining the normal distribution model corresponding to the target historical data sequence according to the variance and the standard deviation.
Illustratively, the variance and standard deviation corresponding to the target historical data sequence are obtained by calculation according to the target historical data sequence, and the variance and standard deviation are substituted into the normal distribution function formula 3, so that the normal distribution model formula 4 corresponding to the target historical data sequence is obtained.
F＝N～(μ, 2 ) (equation 3)
Wherein μ is a variance corresponding to the target historical data sequence and is a standard deviation corresponding to the target historical data sequence, x is a data error corresponding to the current time, and f (x) is a probability density value corresponding to the data error.
Optionally, in order to reduce randomness of the target historical data sequence, multiple groups of target historical data sequences may be selected to perform judgment for multiple times, so as to comprehensively consider whether the first operation data is abnormal data, and reduce situations of misjudgment of the abnormal judgment of the first operation data caused by randomness.
Optionally, a voting mechanism may be used to determine that the data error is abnormal for multiple sets of target historical data sequences.
And S5, when the first operation data are abnormal data, determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme.
When the first operation data is abnormal data, determining a target overhaul scheme of the controlled device according to the first operation data and data related to the first operation data in the historical operation data, and then sending the target overhaul scheme to the controlled device, so that the controlled device performs corresponding overhaul operation according to the target overhaul scheme.
For example, according to a large amount of historical experience, summarizing the overhaul scheme of the controlled equipment to obtain abnormal data and an overhaul scheme corresponding to the abnormal data, storing the abnormal data and the overhaul scheme corresponding to the abnormal data in a database, and when the first operation data is obtained as the abnormal data, obtaining data related to the first operation data in the historical operation data at the same time, and commonly inputting the data to the database to inquire the overhaul scheme according to the abnormal data, thereby obtaining the target overhaul scheme.
In some embodiments, the determining a target overhaul scheme of the controlled device according to the historical operating data and the first operating data includes: determining a backtracking time point according to the first operation data, and screening from the historical operation data according to the backtracking time point to obtain backtracking operation data; and determining a target overhaul scheme of the controlled equipment by the backtracking operation data and the first operation data.
For example, when it is determined that the first operation data is abnormal data, some minute abnormal change should have occurred before the time corresponding to the first operation data, and thus, the historical operation data corresponding to the adjacent time to the current time corresponding to the first operation data is obtained to perform the determination of the target overhaul scheme.
The backtracking time point is an initial time point of the backward from the current time point as an end point, and when the backtracking time point is determined, the corresponding backtracking operation data between the backtracking time point and the current time point can be obtained through screening from the historical operation data, wherein the backtracking operation data does not contain the first operation data corresponding to the current time point. And further determining a target overhaul scheme of the controlled equipment according to the backtracking operation data and the first operation data.
For example, according to the backtracking operation data and the first operation data together form a data abnormal sequence, the data abnormal sequence is subjected to data curve fitting to obtain a first fitting curve, the similarity between the second fitting curve corresponding to the abnormal data and the first fitting curve in an overhaul scheme database corresponding to the abnormal data is calculated, the abnormal data corresponding to the second fitting curve with the largest similarity is taken as the data closest to the data abnormal sequence, and then the overhaul scheme corresponding to the abnormal data corresponding to the second fitting curve with the largest similarity is taken as the target overhaul scheme.
In some embodiments, the determining the backtracking operational data and the first operational data to determine a target overhaul scheme of the controlled device includes: performing data exception analysis according to the backtracking operation data and the first operation data to obtain a target exception category corresponding to the first operation data; and determining a target overhaul scheme of the controlled equipment according to the target abnormal category.
The data anomaly sequence is input into the anomaly classification model to obtain anomaly class according to the retrospective operation data and the first operation data together to form the data anomaly sequence, and then a target overhaul scheme of the controlled equipment corresponding to the data anomaly sequence is determined according to the anomaly class.
For example, the anomaly classification model can be classified into 5 anomaly categories based on a summary of a large number of anomaly data for the controlled device. In order to ensure the accuracy of the abnormal classification model, other types of abnormal classification models can be added to represent possibly newly added abnormal types out of 5 abnormal types, so that the inclusion of the abnormal classification model is improved.
In some embodiments, the determining the target overhaul scheme of the controlled device according to the target abnormality category includes: matching according to the target abnormal category and a scheme database to obtain a similarity result corresponding to the target abnormal category; determining an initial overhaul scheme corresponding to the target abnormal category in the scheme database according to the similarity result, and sending the similarity result and the initial overhaul scheme to an interpreter which is in communication connection with the control terminal so as to display the corresponding similarity result and the initial overhaul scheme in a first display area of a display screen of the interpreter; and receiving a selection instruction generated by triggering the interpreter by a user, and determining a target overhaul scheme from the initial overhaul schemes according to the selection instruction.
For example, the abnormal data of the controlled device is collected, and the abnormal reasons are numerous, so that the abnormal reasons can be classified into large categories first, the target abnormal categories, namely the target abnormal categories corresponding to the large categories, are obtained according to the abnormal category model, and then the small category judgment is performed according to the target abnormal categories.
For example, the controlled device comprises two parts of software and hardware, so that the large class of the controlled device can be divided into software abnormality and hardware abnormality, and then the large class of the controlled device can be divided into small classes according to the software abnormality and the hardware abnormality. Taking software exception as an example, software exception may also be divided into front-end exception, back-end exception, algorithm exception, and so on.
The method comprises the steps of determining the large direction of controlled equipment abnormality according to a target abnormality category, screening in a scheme database according to the target abnormality category to obtain preliminary screening abnormality data and a preliminary screening repair scheme corresponding to the preliminary screening abnormality data, and performing similarity calculation on a data abnormality sequence formed by the retrospective operation data and the first operation data together and the preliminary screening abnormality data to obtain a similarity result; and determining an initial overhaul scheme corresponding to the target abnormal category in the scheme database according to the similarity result, wherein the initial overhaul scheme possibly comprises a plurality of overhaul schemes.
For example, the primary screening repair scheme includes 30 repair schemes, and the similarity result is obtained after the similarity calculation is performed on the primary screening abnormal data and the data abnormal sequence, if the primary screening repair scheme corresponding to the similarity result greater than 80% is used as the primary repair scheme at this time, a plurality of abnormal data with the similarity result greater than 80% may exist in the primary screening abnormal data, and therefore, the primary repair scheme may include a plurality of repair schemes.
Alternatively, the solution database contains a large amount of knowledge and experience of expert level in a certain field, and is constructed according to the knowledge and experience of the expert.
For example, to ensure that the target overhaul scheme has greater effectiveness and interpretability, the initial overhaul scheme and a similarity result corresponding to the initial overhaul scheme may be sent to an interpreter communicatively connected to the control terminal, and displayed in a first display area of the interpreter, so that a user may trigger a selection instruction in the first display area of the interpreter, and further the control terminal may obtain the target overhaul scheme selected from the initial overhaul schemes through the selection instruction.
A schematic diagram of the relationship among the control terminal 10, the controlled device 30, the scheme database 101, and the interpreter 102 is shown in fig. 3. The controlled device 30 transmits the historical operation data and the first operation data to the control terminal 10, the control terminal 10 determines the second operation data according to the historical operation data and the first operation data, further obtains a target historical data sequence and a data error, judges whether the first operation data is abnormal data according to the target historical data sequence and the data error, transmits the historical operation data and the first operation data to the scheme database 101 connected with the control terminal 10 when the first operation data is abnormal data, obtains an initial maintenance scheme through the scheme database 101, further transmits the initial maintenance scheme to the control terminal 10, the control terminal 10 transmits the initial maintenance scheme to the interpreter 102 connected with the control terminal 10, further controls the terminal 10 to determine the target maintenance scheme according to a selection instruction of a user in the interpreter 102, and further transmits the target maintenance scheme to the controlled device 30, so that the controlled device 30 completes maintenance according to the target maintenance scheme.
Alternatively, the selection instruction may be obtained by voice, gesture, mouse, or the like, and the specific manner is not limited.
In some embodiments, the determining a target service plan from the initial service plans according to the selection instruction includes: screening a preselected overhaul scheme from the initial overhaul scheme according to the selection instruction, and displaying the preselected overhaul scheme in a second display area of a display screen of the interpreter; and receiving an editing instruction of a user for the preselected overhaul scheme displayed in the second display area, and correcting the preselected overhaul scheme according to the editing instruction to obtain a target overhaul scheme.
The method includes the steps that an initial overhaul scheme is selected according to a selection instruction, the preselected overhaul scheme is displayed in a second display area of a display screen of an interpreter, a user edits the displayed preselected overhaul scheme in the second display area, the preselected overhaul scheme is obtained, and a target overhaul scheme is obtained after the user edits.
For example, the initial overhaul scheme comprises 3 overhaul schemes, a user obtains 2 overhaul schemes as preselected overhaul schemes after inputting a selection instruction in a first display area of a display screen of the interpreter, the preselected overhaul schemes are further displayed in a second display area, and the user can edit the preselected overhaul schemes in the second display area, so that a more accurate target overhaul scheme is formulated for controlled equipment in a targeted mode.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating an overhaul apparatus 200 for a controlled device according to an embodiment of the present application, which is applied to a control terminal, where the overhaul apparatus 200 for a controlled device includes: the system comprises a data acquisition module 201, a data prediction module 202, a data analysis module 203, a data judgment module 204 and a scheme determination module 205, wherein the data acquisition module 201 is used for acquiring historical operation data of controlled equipment and first operation data operated at the current moment; and the data prediction module 202 is configured to predict second operation data corresponding to the current time of the controlled device according to the historical operation data and the data prediction model. The data analysis module 203 is configured to calculate a data error of the first operation data and the second operation data, and obtain a target historical data sequence associated with the current time from the historical operation data. A data determining module 204, configured to determine whether the first operation data is abnormal data according to the target historical data sequence and the data error; and the solution determining module 205 is configured to determine, when the first operation data is abnormal data, a target overhaul scheme of the controlled device according to the historical operation data and the first operation data, and send the target overhaul scheme to the controlled device, so that the controlled device executes a corresponding overhaul operation according to the target overhaul scheme.
In some embodiments, the data prediction model includes an LSTM network, and the data prediction module 202 performs, in the process of predicting the second operation data corresponding to the current time of the controlled device according to the historical operation data and the data prediction model:
carrying out standardization processing on the historical operation data to obtain standardized data corresponding to the historical operation data;
inputting the standardized data into the LSTM network of the data prediction model to perform model training, so as to obtain model parameters of the data prediction model;
and obtaining second operation data corresponding to the current moment of the controlled equipment according to the model parameters.
In some embodiments, the historical operation data includes operation time and third operation data corresponding to the operation time, and the data analysis module 203 performs, in a process of obtaining the target historical data sequence associated with the current time from the historical operation data:
determining a sliding region, and determining a corresponding operation time range in the historical operation data according to the current moment and the sliding region, wherein the sliding region is used for representing the time length selected from the historical operation data;
Selecting the third operation data corresponding to the operation time when the operation time satisfies the operation time range from the historical operation data;
determining an initial historical data sequence corresponding to the current moment according to the running time and the third running data;
and determining a difference value between the third operation data of adjacent operation time in the initial historical data sequence according to the initial historical data sequence, and further determining a target historical data sequence associated with the current moment.
In some embodiments, the data determination module 204 performs, in the determining whether the first operation data is abnormal data according to the target historical data sequence and the data error:
obtaining a normal distribution model corresponding to the target historical data sequence according to the target historical data sequence;
determining a probability density value corresponding to the data error according to the normal distribution model;
and determining whether the first operation data is abnormal data or not according to the probability density value and a preset condition.
In some embodiments, the data determination module 204 performs, in the process of obtaining the normal distribution model corresponding to the target historical data sequence according to the target historical data sequence:
Determining a variance and a standard deviation corresponding to the target historical data sequence according to the target historical data sequence;
and determining the normal distribution model corresponding to the target historical data sequence according to the variance and the standard deviation.
In some embodiments, the solution determining module 205 performs, in the determining the target overhaul solution for the controlled device according to the historical operating data and the first operating data:
determining a backtracking time point according to the first operation data, and screening from the historical operation data according to the backtracking time point to obtain backtracking operation data;
and determining a target overhaul scheme of the controlled equipment by the backtracking operation data and the first operation data.
In some embodiments, the solution determining module 205 performs, in the process of determining the backtracking operation data and the first operation data to determine the target overhaul solution of the controlled device:
performing data exception analysis according to the backtracking operation data and the first operation data to obtain a target exception category corresponding to the first operation data;
and determining a target overhaul scheme of the controlled equipment according to the target abnormal category.
In some embodiments, the solution determining module 205 performs, in the determining the target overhaul solution of the controlled device according to the target abnormality category:
matching according to the target abnormal category and a scheme database to obtain a similarity result corresponding to the target abnormal category;
determining an initial overhaul scheme corresponding to the target abnormal category in the scheme database according to the similarity result, and sending the similarity result and the initial overhaul scheme to an interpreter which is in communication connection with the control terminal so as to display the corresponding similarity result and the initial overhaul scheme in a first display area of a display screen of the interpreter;
and receiving a selection instruction generated by triggering the interpreter by a user, and determining a target overhaul scheme from the initial overhaul schemes according to the selection instruction.
In some embodiments, the solution determination module 205 performs, in the determining the target service solution from the initial service solutions according to the selection instruction:
screening a preselected overhaul scheme from the initial overhaul scheme according to the selection instruction, and displaying the preselected overhaul scheme in a second display area of a display screen of the interpreter;
And receiving an editing instruction of a user for the preselected overhaul scheme displayed in the second display area, and correcting the preselected overhaul scheme according to the editing instruction to obtain a target overhaul scheme.
It should be noted that, for convenience and brevity of description, a specific working process of the apparatus described above may refer to a corresponding process in the foregoing embodiment of the method for overhauling the controlled device, which is not described herein again.
Referring to fig. 5, fig. 5 is a schematic block diagram of a control terminal according to an embodiment of the present application.
As shown in fig. 5, the control terminal 300 includes a processor 301 and a memory 302, and the processor 301 and the memory 302 are connected by a bus 303, such as an I2C (Inter-integrated Circuit) bus.
In particular, the processor 301 is used to provide computing and control capabilities, supporting the operation of the entire server. The processor 301 may be a central processing unit (Central Processing Unit, CPU), the processor 301 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Specifically, the Memory 302 may be a Flash chip, a Read-Only Memory (ROM) disk, an optical disk, a U-disk, a removable hard disk, or the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion of the structure related to the embodiment of the present application, and does not constitute a limitation of the control terminal to which the embodiment of the present application is applied, and a specific control terminal may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
The processor 301 is configured to run a computer program stored in the memory, and implement the method for overhauling the controlled device provided in any embodiment of the present application when the computer program is executed.
In some embodiments, the processor 301 is configured to run a computer program stored in a memory, apply to a control terminal, and implement the following steps when executing the computer program:
acquiring historical operation data of controlled equipment and first operation data operated at the current moment;
predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and a data prediction model;
Calculating data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from the historical operation data;
judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error;
when the first operation data is abnormal data, determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme.
In some embodiments, the data prediction model includes an LSTM network, and the processor 301 performs, in the predicting, according to the historical operation data and the data prediction model, the second operation data corresponding to the current time of the controlled device:
carrying out standardization processing on the historical operation data to obtain standardized data corresponding to the historical operation data;
inputting the standardized data into the LSTM network of the data prediction model to perform model training, so as to obtain model parameters of the data prediction model;
And obtaining second operation data corresponding to the current moment of the controlled equipment according to the model parameters.
In some embodiments, the historical operation data includes operation time and third operation data corresponding to the operation time, and the processor 301 performs, in a process of obtaining the target historical data sequence associated with the current time from the historical operation data:
determining a sliding region, and determining a corresponding operation time range in the historical operation data according to the current moment and the sliding region, wherein the sliding region is used for representing the time length selected from the historical operation data;
selecting the third operation data corresponding to the operation time when the operation time satisfies the operation time range from the historical operation data;
determining an initial historical data sequence corresponding to the current moment according to the running time and the third running data;
and determining a difference value between the third operation data of adjacent operation time in the initial historical data sequence according to the initial historical data sequence, and further determining a target historical data sequence associated with the current moment.
In some embodiments, the processor 301 performs, in the determining whether the first operation data is abnormal data according to the target historical data sequence and the data error:
Obtaining a normal distribution model corresponding to the target historical data sequence according to the target historical data sequence;
determining a probability density value corresponding to the data error according to the normal distribution model;
and determining whether the first operation data is abnormal data or not according to the probability density value and a preset condition.
In some embodiments, the processor 301 performs, in the process of obtaining the normal distribution model corresponding to the target historical data sequence according to the target historical data sequence:
determining a variance and a standard deviation corresponding to the target historical data sequence according to the target historical data sequence;
and determining the normal distribution model corresponding to the target historical data sequence according to the variance and the standard deviation.
In some embodiments, the processor 301 performs, in the determining the target overhaul scheme of the controlled device according to the historical operation data and the first operation data:
determining a backtracking time point according to the first operation data, and screening from the historical operation data according to the backtracking time point to obtain backtracking operation data;
and determining a target overhaul scheme of the controlled equipment by the backtracking operation data and the first operation data.
In some embodiments, the processor 301 performs, in the determining the backtracking operation data and the first operation data to the target overhaul plan of the controlled device:
performing data exception analysis according to the backtracking operation data and the first operation data to obtain a target exception category corresponding to the first operation data;
and determining a target overhaul scheme of the controlled equipment according to the target abnormal category.
In some embodiments, the processor 301 performs, in the determining the target overhaul scheme of the controlled apparatus according to the target abnormality category:
matching according to the target abnormal category and a scheme database to obtain a similarity result corresponding to the target abnormal category;
determining an initial overhaul scheme corresponding to the target abnormal category in the scheme database according to the similarity result, and sending the similarity result and the initial overhaul scheme to an interpreter which is in communication connection with the control terminal so as to display the corresponding similarity result and the initial overhaul scheme in a first display area of a display screen of the interpreter;
and receiving a selection instruction generated by triggering the interpreter by a user, and determining a target overhaul scheme from the initial overhaul schemes according to the selection instruction.
In some embodiments, the processor 301 performs, in the determining the target service plan from the initial service plans according to the selection instruction:
screening a preselected overhaul scheme from the initial overhaul scheme according to the selection instruction, and displaying the preselected overhaul scheme in a second display area of a display screen of the interpreter;
and receiving an editing instruction of a user for the preselected overhaul scheme displayed in the second display area, and correcting the preselected overhaul scheme according to the editing instruction to obtain a target overhaul scheme.
It should be noted that, for convenience and brevity of description, a specific working process of the control terminal described above may refer to a corresponding process in the foregoing embodiment of the method for overhauling the controlled device, which is not described herein again.
The embodiment of the application also provides a storage medium for computer readable storage, the storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the steps of the method for overhauling any controlled device provided in the embodiment of the specification of the application.
The storage medium may be an internal storage unit of the control terminal of the foregoing embodiment, for example, a control terminal memory. The storage medium may also be an external storage device of the control terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the control terminal.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, functional modules/units in the apparatus disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware embodiment, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
It should be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. The present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the scope of the present application, and these modifications and substitutions are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (12)
1. A method for servicing a controlled device, the method being applied to a control terminal, the method comprising:
acquiring historical operation data of controlled equipment and first operation data operated at the current moment;
predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and a data prediction model;
calculating data errors of the first operation data and the second operation data, and acquiring a target historical data sequence associated with the current moment from the historical operation data;
judging whether the first operation data is abnormal data or not according to the target historical data sequence and the data error;
when the first operation data is abnormal data, determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme.
2. The method of claim 1, wherein the data prediction model comprises an LSTM network, and wherein predicting the second operation data corresponding to the current time of the controlled device according to the historical operation data and the data prediction model comprises:
Carrying out standardization processing on the historical operation data to obtain standardized data corresponding to the historical operation data;
inputting the standardized data into the LSTM network of the data prediction model to perform model training, so as to obtain model parameters of the data prediction model;
and obtaining second operation data corresponding to the current moment of the controlled equipment according to the model parameters.
3. The method according to claim 1, wherein the historical operation data includes operation time and third operation data corresponding to the operation time, and obtaining a target historical data sequence associated with the current time from the historical operation data includes:
determining a sliding region, and determining a corresponding operation time range in the historical operation data according to the current moment and the sliding region, wherein the sliding region is used for representing the time length selected from the historical operation data;
selecting the third operation data corresponding to the operation time when the operation time satisfies the operation time range from the historical operation data;
determining an initial historical data sequence corresponding to the current moment according to the running time and the third running data;
And determining a difference value between the third operation data of adjacent operation time in the initial historical data sequence according to the initial historical data sequence, and further determining a target historical data sequence associated with the current moment.
4. The method of claim 1, wherein said determining whether the first operational data is anomalous based on the target historical data sequence and the data error comprises:
obtaining a normal distribution model corresponding to the target historical data sequence according to the target historical data sequence;
determining a probability density value corresponding to the data error according to the normal distribution model;
and determining whether the first operation data is abnormal data or not according to the probability density value and a preset condition.
5. The method according to claim 4, wherein the obtaining a normal distribution model corresponding to the target historical data sequence according to the target historical data sequence includes:
determining a variance and a standard deviation corresponding to the target historical data sequence according to the target historical data sequence;
and determining the normal distribution model corresponding to the target historical data sequence according to the variance and the standard deviation.
6. The method of claim 1, wherein said determining a target service plan for the controlled device based on the historical operating data and the first operating data comprises:
determining a backtracking time point according to the first operation data, and screening from the historical operation data according to the backtracking time point to obtain backtracking operation data;
and determining a target overhaul scheme of the controlled equipment by the backtracking operation data and the first operation data.
7. The method of claim 6, wherein said determining the backtracking operational data and the first operational data to determine a target service plan for the controlled device comprises:
performing data exception analysis according to the backtracking operation data and the first operation data to obtain a target exception category corresponding to the first operation data;
and determining a target overhaul scheme of the controlled equipment according to the target abnormal category.
8. The method of claim 7, wherein the determining a target service plan for the controlled device based on the target anomaly category comprises:
matching according to the target abnormal category and a scheme database to obtain a similarity result corresponding to the target abnormal category;
Determining an initial overhaul scheme corresponding to the target abnormal category in the scheme database according to the similarity result, and sending the similarity result and the initial overhaul scheme to an interpreter which is in communication connection with the control terminal so as to display the corresponding similarity result and the initial overhaul scheme in a first display area of a display screen of the interpreter;
and receiving a selection instruction generated by triggering the interpreter by a user, and determining a target overhaul scheme from the initial overhaul schemes according to the selection instruction.
9. The method of claim 8, wherein said determining a target service plan from said initial service plans based on said selection instruction comprises:
screening a preselected overhaul scheme from the initial overhaul scheme according to the selection instruction, and displaying the preselected overhaul scheme in a second display area of a display screen of the interpreter;
and receiving an editing instruction of a user for the preselected overhaul scheme displayed in the second display area, and correcting the preselected overhaul scheme according to the editing instruction to obtain a target overhaul scheme.
10. An inspection device for a controlled apparatus, comprising:
The data acquisition module is used for acquiring historical operation data of the controlled equipment and first operation data operated at the current moment;
the data prediction module is used for predicting second operation data corresponding to the current moment of the controlled equipment according to the historical operation data and the data prediction model;
the data analysis module is used for calculating the data errors of the first operation data and the second operation data and acquiring a target historical data sequence associated with the current moment from the historical operation data;
the data judging module is used for judging whether the first operation data are abnormal data or not according to the target historical data sequence and the data error;
and the scheme determining module is used for determining a target overhaul scheme of the controlled equipment according to the historical operation data and the first operation data when the first operation data is abnormal data, and sending the target overhaul scheme to the controlled equipment so that the controlled equipment executes corresponding overhaul operation according to the target overhaul scheme.
11. A control terminal, characterized in that the control terminal comprises a processor and a memory;
the memory is used for storing a computer program;
The processor is configured to execute the computer program and to implement the method of servicing a controlled apparatus according to any of claims 1 to 9 when the computer program is executed.
12. A computer-readable storage medium, which, when executed by one or more processors, causes the one or more processors to perform the steps of the method of servicing a controlled apparatus as claimed in any of claims 1 to 9.
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