CN116881526A - Data processing method, device and equipment - Google Patents
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Abstract
The invention provides a data processing method, a device and equipment, wherein the method comprises the following steps: acquiring a data display instruction; acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction; displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information. The scheme provided by the invention can realize real-time monitoring of the data and improve the flexibility of real-time display of the data from different sources.
Description
Technical Field
The present invention relates to the field of computer information technologies, and in particular, to a data processing method, apparatus, and device.
Background
Currently, industries such as water affairs, logistics, traffic, security protection, energy, medical treatment, construction, manufacturing, home, retail, agriculture and the like are important fields of application of the Internet of things, and data real-time monitoring and displaying are also applied to information-based system data real-time monitoring and displaying of the industries. The data sources of the current real-time monitoring and displaying of the data of the similar equipment generally adopt the data index input and reporting of a service system in a unified way, or the data sources are uniformly acquired in real time through the installation of a monitoring sensor, and the information is transmitted to the Internet of things system through a network and then transmitted to the monitoring system for displaying. But part of the devices in the same type are filled, and part of the devices are scenes of the real-time acquisition sources; the part time of the same equipment is acquisition, and the part time is a filled scene; and when the problem occurs and manual verification is needed, the problem that errors occur in real-time monitoring and displaying cannot be solved.
Disclosure of Invention
The invention aims to provide a data processing method, a device and equipment to realize real-time monitoring of data and improve the flexibility of real-time display of data from different sources.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a data processing method, comprising:
acquiring a data display instruction;
acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction;
displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information.
Optionally, displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set, including:
acquiring an identification value of at least one item of tag information corresponding to each item of index data in the index data set;
determining target index data to be displayed in the index data set according to the identification value of at least one item of label information;
and displaying the target index data according to the source identification information corresponding to the target index data and the preset display configuration information.
Optionally, at least one item of tag information includes:
acquiring opening condition label information;
filling open condition label information;
and opening the condition priority label information.
Optionally, determining target index data to be displayed in the index data set according to the identification value of at least one item of label information includes:
and when the identification value of the label information of the acquisition starting condition is effective, performing calibration processing on the current index data to obtain the target index data.
Optionally, determining target index data to be displayed in the index data set according to the valid value of at least one item of label information includes:
and when the identification value of the filling opening condition label information is effective, performing calibration processing on the current index data to obtain the target index data.
Optionally, determining target index data to be displayed in the index data set according to the valid value of at least one item of label information includes:
and when the identification value of the collected opening condition label information and the identification value of the filled opening condition label information are valid, performing calibration processing on the index data with the valid identification value of the opening condition priority label information according to the identification value of the opening condition priority label information corresponding to the index data to obtain the target index data.
Optionally, performing calibration processing on the index data to obtain target index data, including:
obtaining a difference value between the index data and preset standard index data;
performing calibration processing on the index data according to the difference value and a preset calibration model to obtain the target index data,
a data processing apparatus comprising:
the acquisition module is used for acquiring the data display instruction;
the processing module is used for acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction; displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information.
A computing device, comprising: a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, performs the steps of the method as described above.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, the data display instruction is obtained; acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction; displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects the identification information and/or the system fills the identification information so as to realize real-time monitoring of the data and improve the flexibility of real-time display of the data from different sources.
Drawings
FIG. 1 is a flow chart of a data processing method provided by an embodiment of the present invention;
FIG. 2 is a block diagram of a data processing apparatus according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention proposes a data processing method, including:
step 11, acquiring a data display instruction;
step 12, acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction;
step 13, displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information.
The method in the embodiment can be applied to a data real-time monitoring display system, and can generate a data display instruction corresponding to the requirement index data according to the actual requirement of the service; accessing a bottom data layer according to the data display instruction, and acquiring an index data set in a preset period; here, the preset time period may be set according to an actual service requirement, and the index data in the index data set may include: process core parameters (including but not limited to parameters such as temperature, humidity, water quality, pressure, flow, etc.), equipment parameters (including but not limited to equipment status, equipment real-time faults, equipment current, equipment voltage, equipment frequency, etc.), equipment alarm parameters, environmental parameters, security parameters, etc.;
the index data set can comprise a plurality of different index data, each index data corresponds to a time stamp and corresponds to a time node when the index data is acquired, so that the corresponding index data can be displayed according to the specific requirement of time node information when the real-time display is performed;
each item of index data in the index data set corresponds to source identification information; the source identification information may include: the internet of things collects identification information and/or fills the identification information, when data display is carried out, the data with different sources can be displayed in real time according to the requirement of the actual preset source rule and the preset display configuration information, so that the flexibility of real-time monitoring of the data and real-time display of the data with different sources is realized; for the same index data, in the preset time period, two sources can be used, wherein one source is acquired through the Internet of things acquisition of equipment networking, and the other source is acquired through manual or database filling; therefore, the index data set can contain a plurality of different types of index data, and each index data has at least one index data with definite source; that is, each item of index data includes at least one index data acquired through the internet of things acquisition and/or at least one index data acquired through filling;
here, the internet of things acquisition identification information indicates that corresponding index data is acquired through various real-time monitoring sensors in a networking manner, network transmission is supported through equipment networking construction, internet of things software acquisition is performed, message middleware performs message subscription and pushing, and real-time acquisition and display of the corresponding index data of the data real-time monitoring display system can be realized; the system filling identification information indicates that corresponding index data are filled by human or system input or are directly obtained from a corresponding database;
the preset presentation configuration information may include: the name and type information of preset display data matched with the type of the index data, the plate information of the preset display data, the real-time acquisition and acquisition channel of the preset display data and/or the filling and recording channel of the preset display data; here, the preset display data real-time acquisition channel corresponds to the internet of things acquisition identification information, the preset display data filling input channel corresponds to the system filling identification information, and the preset display configuration information can be flexibly adjusted according to actual requirements; through the preset display configuration information and the source identification information, flexible and real-time monitoring and display of index data can be realized.
In an optional embodiment of the present invention, the step 13 may include:
step 131, obtaining an identification value of at least one item of tag information corresponding to each item of index data in the index data set;
step 132, determining target index data to be displayed in the index data set according to the identification value of at least one item of the label information;
and step 133, displaying the target index data according to the source identification information corresponding to the target index data and the preset display configuration information.
In this embodiment, before the identification value of at least one item of tag information corresponding to each item of index data in the index data set is obtained, the index data in the index data set is preprocessed, so as to realize preliminary screening of the data, so that some abnormal and obviously wrong index data are removed, the workload of subsequent processing is reduced, and meanwhile, the accuracy of the subsequent processing is ensured;
further, for the obtained index data set, reading an identification value of at least one item of tag information of each item of index data in the index data set, and determining target index data to be displayed according to the validity of the identification value; further, displaying the target index data according to the source identification information corresponding to the target index data and the preset display configuration information;
here, at least one item of the tag information includes:
acquiring opening condition label information;
filling open condition label information;
and opening the condition priority label information.
In this embodiment, the collection start condition tag information indicates a start condition of the index data, which is acquired through the internet of things collection, corresponding to the preset period, and an identification value of the collection start condition tag information may be denoted as M1;
the filling opening condition label information indicates that the index data corresponds to the opening condition obtained through system filling in the preset period, and the identification value of the filling opening condition label information can be represented as M2;
the starting condition priority label information represents a preset priority of the index data in a corresponding acquisition mode in the preset period and can be represented as P;
the tag information may further include: index data updating time tag information, wherein the index data updating time tag information represents a difference value of corresponding time node information of the index data in the preset period and can be represented as U;
according to the validity of the identification value of at least one item of label information, determining target index data to be displayed so as to ensure the accuracy and rationality of the display data; here, the identification value of the tag information may be set to two specific values according to actual needs, for example, the identification value may be set to 1 or 0, where 1 indicates that the identification value of the current tag information is valid and 0 indicates that the identification value of the current tag information is invalid; for example, when the identification value of the acquisition start condition label information corresponding to the current index data is 1, the identification value of the acquisition start condition label information of the current index data is effective, that is, the index data is acquired through the acquisition of the internet of things; when the identification value is 0, the identification value is invalid, namely the current index data is not acquired through the Internet of things, and if the identification value of the label information corresponding to the index data and reporting the opening condition is invalid at the moment, the current index data is confirmed to be error index data, and the accuracy and the validity of the target index data to be displayed are not possessed;
and further screening target index data to be displayed through at least one type of label information so as to ensure the accuracy of data display.
In an alternative embodiment of the present invention, the step 132 may include:
and 1321a, when the identification value of the acquisition start condition label information is valid, performing calibration processing on the current index data to obtain the target index data.
In this embodiment, when the identification value of the acquired open condition label information corresponding to the index data is valid, it may be confirmed that the current index data is the index data to be displayed, and at this time, the identification value of the filled open condition label information corresponding to the index data is invalid;
further, performing calibration processing on the index data to obtain target index data to be finally displayed; and performing calibration processing on the index data to remove the influence of the interference item impurity data on the target index data, and further ensuring the accuracy and stability of the target index data during display.
In an alternative embodiment of the present invention, the step 132 may include:
and 1321b, when the identification value of the filling opening condition label information is valid, performing calibration processing on the current index data to obtain the target index data.
In this embodiment, when the identification value of the filling start condition label information corresponding to the index data is valid, it may be confirmed that the current index data is the index data to be displayed, and at this time, the identification value of the collection start condition label information corresponding to the index data is invalid;
further, performing calibration processing on the index data to obtain target index data to be finally displayed; and performing calibration processing on the index data to remove the influence of the interference item impurity data on the target index data, and further ensuring the accuracy and stability of the target index data during display.
In an alternative embodiment of the present invention, the step 132 may include:
and 1321c, when the identification value of the collected start condition label information and the identification value of the filled start condition label information are both valid, performing calibration processing on the index data with the valid identification value of the start condition priority label information according to the identification value of the start condition priority label information corresponding to the index data, so as to obtain the target index data.
In this embodiment, when the index data set has the valid identification value of the acquired open condition label information and the valid identification value of the filled open condition label information of the same item of index data within the preset period, it is indicated that in the preset period, the acquired index data corresponds to the index data which is acquired through the acquisition of the material network and also is acquired through the manual or database filling, at the moment, the identification value of the open condition priority label information of the two source index data is correspondingly read, and the index data with the larger identification value of the open condition priority label information in the two source index data is correspondingly confirmed to be the index data to be displayed;
further, confirming the index data to be displayed to perform calibration processing so as to obtain the target index data to be displayed finally; the method comprises the steps of carrying out a first treatment on the surface of the And performing calibration processing on the index data to remove the influence of the interference item impurity data on the target index data, and further ensuring the accuracy and stability of the target index data during display.
In an optional embodiment of the present invention, performing calibration processing on the index data to obtain target index data includes:
step 21, obtaining the difference value between the index data and preset standard index data;
step 22, performing calibration processing on the index data according to the difference value and a preset calibration model to obtain the target index data,
in this embodiment, the preset calibration model is obtained by fitting based on the historical index data and a second difference between the historical index data and the preset standard index data; the preset standard index data is a standard value corresponding to the index data, and can be set according to different kinds of index data and actual requirements;
after confirming index data to be displayed, acquiring a first difference value of each index data and preset standard index data, and inputting the first difference value into a preset calibration model obtained through fitting to obtain a standard difference value; further obtaining calibrated target index data according to the labeling difference value and the index data; the method has the advantages that the index data are calibrated, so that errors of the acquired data of the equipment of the Internet of things or errors of the manually filled data according to the equipment display are eliminated, the quality, reliability and usability of the index data are improved, the accurate display of the subsequent target index data is further ensured, and meanwhile, a reliable data basis is provided for analysis and decision of equipment application based on the displayed calibrated target index data;
because the index data are data generated by the process equipment within a preset period, each index data corresponds to a time stamp or a time node, and under the time stamp or the time node, the index data correspond to preset standard index data;
further, fitting is performed based on the second difference value between the historical index data and the preset standard index data and the historical index data to obtain the preset calibration model, and the method specifically comprises the following steps:
step 31, acquiring a plurality of sets of historical index data corresponding to a plurality of sets of equipment in a preset database;
step 32, calculating a second difference value between each historical index data in each set of historical index data and each preset standard index data corresponding to each historical index data;
step 33, establishing a linear regression model of the historical index data and the second difference value: f (x) =mx+b; wherein F (x) represents the second difference, m represents the slope of the linear regression model, b represents the intercept of the linear regression model, and x represents the index data variable;
step 34, obtaining the slope and intercept of the linear regression model by a least square method;
step 341, respectively obtaining a first average value of each set of historical index data and a second average value of preset standard index data corresponding to each set of historical index data;
step 342, respectively obtaining a third difference value between each historical index data in each set of historical index data and the corresponding first mean value thereof, and a fourth difference value between each preset standard index data and the corresponding second mean value thereof;
step 343, calculating the slope of the linear regression model according to a first preset function, specifically as follows:
；
wherein m represents the slope of the linear regression model, x i Represents the ith historical index data in the set of historical index data,representing a first mean value, r, of the set of historical index data i Representing the ith historical index data x in the set of historical index data i Corresponding preset standard index data->Representing preset standard index dataI=1, 2, …, N being a positive integer, N representing the number of the set of historical index data; here, since a plurality of sets of history index data are initially acquired, there are a plurality of slopes m of the corresponding fitting,
in step 344, the intercept of the linear regression model is calculated by a second preset function, which is specifically as follows:
；
wherein b represents the intercept of the linear regression model, m represents the slope of the linear regression model,a first mean value representing the set of historical index data, < >>Representing a second average value of preset standard index data; here, since the initial acquisition is a plurality of sets of history index data, there are a plurality of slopes m of the corresponding fitting, so there are a plurality of intercepts b of the corresponding fitting;
step 35, the slope and the intercept determined after fitting are carried into the linear regression model in step 33, and a preset calibration model is obtained;
here, in the process of performing data fitting, in order to ensure the accuracy of fitting and finally obtain the accuracy of a preset calibration model, parameters of the preset calibration model can be further adjusted through a square loss function, so that the accuracy of performing calibration processing of the preset calibration model is further improved; the square loss function may be expressed by the following formula:
；
wherein S represents a loss function, y j Representing a second difference value actually calculated by any one of the historical index data and the corresponding preset standard index data,representation according to any ofHistorical index data x j And presetting a second difference value predicted by the calibration model; the meaning of the squaring loss function is to square the difference between the actual observed value of each data point and the predicted value of the fitting model and sum the differences of all data points; by minimizing the square loss function, the parameters of the fitting model can be found to minimize the difference between the fitting model and the actual observed value, and further, the closed solution of the optimal solution can be obtained by conducting derivation on the loss function or iterative solution can be conducted by using an optimization algorithm so as to adjust the parameters (slope or intercept) of the preset calibration model, and the accuracy of the preset calibration model on index data calibration can be ensured;
further, according to the obtained preset calibration model, inputting the first difference value into the preset calibration model to obtain a standard difference value, further obtaining a sum value of the standard difference value and corresponding index data, and determining the sum value as target index data to be finally displayed;
taking the example of determining the index data to be displayed as a temperature value, and calibrating the temperature value of which the current measured temperature is 35;
the set of historical measured temperatures obtained were: [20, 30, 40, 50, 60], the corresponding standard temperatures are: [19.8, 31.2, 40.5, 50.3, 60.2]
The polynomial function obtained by regression fitting is: calibration temperature difference = 0.0022× (current measured temperature-corresponding to preset standard temperature) 2 +0.0144×current measured temperature+0.0014;
by substituting the measured temperature into a polynomial function, a calibrated temperature difference value, namely, a second difference value after the difference value between the current temperature value and the preset standard temperature is calibrated is 0.29, namely, the calibrated temperature value is 35.29;
according to the embodiment of the invention, the linear preset calibration model is obtained by fitting the data, meanwhile, the parameters of the preset calibration model are adjusted through the square loss function, and the index data are calibrated according to the adjusted calibration model, so that the target index data are obtained, and are more similar to the standard index data, the accuracy and consistency of the target index data are improved, and the accuracy and rationality of index data display are further ensured.
According to the embodiment of the invention, the data display instruction is acquired; acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction; displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills the identification information so as to realize real-time monitoring of data, and improves the flexibility of real-time display of data from different sources, so that the management level and decision level of enterprises are improved through real-time monitoring, and an intelligent first step is taken.
As shown in fig. 2, an embodiment of the present invention further provides a data processing apparatus 20, including:
an acquisition module 21, configured to acquire a data display instruction;
the processing module 22 is configured to obtain, according to the data display instruction, an index data set of process equipment in a process line within a preset period; displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information.
Optionally, the processing module 22 displays the index data according to preset display configuration information and source identification information of each item of index data in the index data set, which is specifically configured to:
acquiring an identification value of at least one item of tag information corresponding to each item of index data in the index data set;
determining target index data to be displayed in the index data set according to the identification value of at least one item of label information;
and displaying the target index data according to the source identification information corresponding to the target index data and the preset display configuration information.
Optionally, at least one item of tag information includes:
acquiring opening condition label information;
filling open condition label information;
and opening the condition priority label information.
Optionally, the processing module 22 determines target index data to be displayed in the index data set according to the identification value of at least one item of the tag information, which is specifically configured to:
and when the identification value of the label information of the acquisition starting condition is effective, performing calibration processing on the current index data to obtain the target index data.
Optionally, the processing module 22 determines target index data to be displayed in the index data set according to the valid value of at least one item of the tag information, which is specifically configured to:
and when the identification value of the filling opening condition label information is effective, performing calibration processing on the current index data to obtain the target index data.
Optionally, the processing module 22 determines target index data to be displayed in the index data set according to the valid value of at least one item of the tag information, which is specifically configured to:
and when the identification value of the collected opening condition label information and the identification value of the filled opening condition label information are valid, performing calibration processing on the index data with the valid identification value of the opening condition priority label information according to the identification value of the opening condition priority label information corresponding to the index data to obtain the target index data.
Optionally, the processing module 22 performs calibration processing on the index data to obtain target index data, which is specifically configured to:
obtaining a difference value between the index data and preset standard index data;
performing calibration processing on the index data according to the difference value and a preset calibration model to obtain the target index data,
it should be noted that, the device is a device corresponding to the data processing method, and all implementation manners in the method embodiment are applicable to the device embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium comprising instructions which, when run on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., 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 an indirect coupling or communication connection via some interfaces, devices or units, 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 the embodiments of the present invention 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 computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A method of data processing, comprising:
acquiring a data display instruction;
acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction;
displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information.
2. The data processing method according to claim 1, wherein displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set, comprises:
acquiring an identification value of at least one item of tag information corresponding to each item of index data in the index data set;
determining target index data to be displayed in the index data set according to the identification value of at least one item of label information;
and displaying the target index data according to the source identification information corresponding to the target index data and the preset display configuration information.
3. The data processing method according to claim 2, wherein at least one item of the tag information includes:
acquiring opening condition label information;
filling open condition label information;
and opening the condition priority label information.
4. A data processing method according to claim 3, wherein determining target index data to be displayed in the index data set according to the identification value of at least one item of the tag information comprises:
and when the identification value of the label information of the acquisition starting condition is effective, performing calibration processing on the current index data to obtain the target index data.
5. A data processing method according to claim 3, wherein determining target index data to be displayed in the index data set based on a valid value of at least one item of the tag information comprises:
and when the identification value of the filling opening condition label information is effective, performing calibration processing on the current index data to obtain the target index data.
6. A data processing method according to claim 3, wherein determining target index data to be displayed in the index data set based on a valid value of at least one item of the tag information comprises:
and when the identification value of the collected opening condition label information and the identification value of the filled opening condition label information are valid, performing calibration processing on the index data with the valid identification value of the opening condition priority label information according to the identification value of the opening condition priority label information corresponding to the index data to obtain the target index data.
7. The data processing method according to any one of claims 4 to 6, wherein performing calibration processing on the index data to obtain target index data includes:
obtaining a difference value between the index data and preset standard index data;
and carrying out calibration processing on the index data according to the difference value and a preset calibration model to obtain the target index data.
8. A data processing apparatus, comprising:
the acquisition module is used for acquiring the data display instruction;
the processing module is used for acquiring an index data set of process equipment in a process line within a preset period according to the data display instruction; displaying the index data according to preset display configuration information and source identification information of each item of index data in the index data set; the source identification information includes: the internet of things collects identification information and/or the system fills in the identification information.
9. A computing device, comprising: a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the method of any of claims 1 to 7.
10. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the steps of the method of any of claims 1 to 7.
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