CN111178775A - Data evaluation method and device, readable medium and electronic equipment - Google Patents
Data evaluation method and device, readable medium and electronic equipment Download PDFInfo
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- CN111178775A CN111178775A CN201911425145.5A CN201911425145A CN111178775A CN 111178775 A CN111178775 A CN 111178775A CN 201911425145 A CN201911425145 A CN 201911425145A CN 111178775 A CN111178775 A CN 111178775A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
Abstract
The invention discloses a data evaluation method, a data evaluation device, a readable medium and electronic equipment, wherein the data evaluation device comprises the following steps: acquiring data to be evaluated, and determining the data type of the data to be evaluated; determining a corresponding evaluation template according to the data type; evaluating the data value of the data to be evaluated by utilizing the evaluation template to determine an evaluation result; and evaluating the data value of the data to be evaluated of a specific type by using the evaluation template to determine an evaluation result, thereby determining whether the data to be evaluated has numerical value abnormality or not and efficiently determining the authenticity and the accuracy of the data to be evaluated.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data evaluation method, an apparatus, a readable medium, and an electronic device.
Background
In the process of realizing data analysis based on the platform of the Internet of things, data acquisition can be a prerequisite step. And in the data acquisition process, data are acquired through various instruments and sensors under the platform of the Internet of things. However, in the actual use process, due to the influence of the space environment where the instruments and the sensors are located and the network environment, the actually acquired data may be different from the real value.
Inaccurate data can directly influence subsequent data analysis and processing, and the overall performance of the platform of the internet of things is reduced. And the data volume related to the platform of the internet of things is very huge, so that it is difficult to find out which data is inaccurate from a large amount of data in the prior art.
Disclosure of Invention
The invention provides a data evaluation method, a data evaluation device, a readable medium and electronic equipment, which are used for realizing quality evaluation of data in an Internet of things platform, so that possibly inaccurate data can be found in time, and related workers can process the data in time.
In a first aspect, the present invention provides a data evaluation method, including:
acquiring data to be evaluated, and determining the data type of the data to be evaluated;
determining a corresponding evaluation template according to the data type;
and evaluating the data value of the data to be evaluated by utilizing the evaluation template so as to determine an evaluation result.
Preferably, before acquiring the data to be evaluated, the method further includes:
classifying the acquired original data to determine data to be evaluated;
the data to be evaluated comprises a data value, a data type and a time stamp.
Preferably, the acquiring the data to be evaluated includes:
and acquiring data to be evaluated in a preset time period.
Preferably, the method further comprises the following steps:
and when the evaluation result meets a preset condition, generating early warning information aiming at the data to be evaluated, and pushing the early warning information.
Preferably, the data types include:
accumulated value type data, instantaneous value type data, and/or state value type data.
Preferably, at least one evaluation item is included in the evaluation template; then, the evaluating the data value of the data to be evaluated by using the evaluation template to determine an evaluation result includes:
determining an evaluation index of the data to be evaluated by using each evaluation item;
and determining the evaluation result according to the evaluation index.
Preferably, the evaluation item includes:
a value of null, a value of 0, a value of negative, a value of constant, and/or a value of varying magnitude.
In a second aspect, the present invention provides a data evaluation apparatus comprising:
the data acquisition module is used for acquiring data to be evaluated and determining the data type of the data to be evaluated;
the template determining module is used for determining a corresponding evaluation template according to the data type;
and the evaluation module is used for evaluating the data value of the data to be evaluated by utilizing the evaluation template so as to determine an evaluation result.
In a third aspect, the present invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the data evaluation method according to any one of the first aspect.
In a fourth aspect, the present invention provides an electronic device, including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor executes the data evaluation method according to any one of the first aspect.
The invention provides a data evaluation method, a data evaluation device, a readable medium and electronic equipment.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flow chart of a data evaluation method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating another data evaluation method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a data evaluation apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the data acquisition process, data are acquired through various instruments and sensors under the platform of the Internet of things. However, in the actual use process, due to the influence of the space environment where the instruments and the sensors are located and the network environment, the actually acquired data may be different from the real value. The sensors and meters can be influenced by environmental factors to obtain unreal data values, or data can be lost, errors occur in the network transmission process, and the like. Inaccurate data can directly influence subsequent data analysis and processing, and the overall performance of the platform of the internet of things is reduced. And the data volume related to the platform of the internet of things is very huge, so that it is difficult to find out which data is inaccurate from a large amount of data in the prior art.
In view of this, the invention provides a data evaluation method and device, which are used for realizing quality evaluation of data in an internet of things platform, so that possibly inaccurate data can be found in time, and relevant workers can process the data in time.
Referring to fig. 1, a specific embodiment of the data evaluation method provided by the present invention is shown. In this embodiment, the method specifically includes the following steps:
In this embodiment, the data to be evaluated is the object to be evaluated. The data to be evaluated can be various data generated in the platform of the internet of things, and specifically can be data collected and uploaded by various sensors and instruments. The method in the embodiment evaluates and analyzes the data to be evaluated to determine whether the data is true or accurate.
In fact, the sensors or meters do not upload data one by one during the process of uploading data. The raw data directly acquired may include a certain amount of data of different types, and are uploaded in batches in the form of message queues. Since the raw data is not classified according to type, the raw data cannot be directly used as the data to be evaluated.
Therefore, before acquiring the data to be evaluated, it is preferable to further include: and classifying the acquired original data to determine the data to be evaluated. That is, the data to be evaluated is generally data classified according to type. Different types of data need to be evaluated in a corresponding manner. The data to be evaluated comprises a data value, a data type and a time stamp. The data collected by the same sensor or instrument can be further formed into time sequence data according to the time stamp.
In addition, because the data volume in the platform of the internet of things is large, the evaluation can be performed periodically in the embodiment, so that the evaluation efficiency and the real-time performance are improved. That is, a time period can be preset according to requirements, and data to be evaluated in the time period can be obtained. And further analyzing whether the numerical value and the numerical value change condition of the data to be evaluated in the time period range are real and accurate.
And 102, determining a corresponding evaluation template according to the data type.
Different types of data need to be evaluated in a corresponding manner, namely, each data type needs to have a corresponding evaluation template, and the evaluation template comprises evaluation rules and evaluation calculation logic for the type of data. Specifically, the evaluation template includes at least one evaluation item, and each evaluation item can evaluate the data quality from a specific dimension.
And 103, evaluating the data value of the data to be evaluated by utilizing the evaluation template to determine an evaluation result.
Determining a corresponding evaluation index of the data to be evaluated by utilizing each evaluation item; and then, carrying out comprehensive calculation according to the evaluation index to determine the evaluation result.
In this embodiment, when the evaluation result meets a preset condition, early warning information for the data to be evaluated may be further generated, and the early warning information is pushed. Therefore, the staff of the operation and maintenance Internet of things platform can timely learn the error condition of the data and timely process and correct the error condition.
According to the technical scheme, the beneficial effects of the embodiment are as follows: and evaluating the data value of the data to be evaluated of a specific type by using the evaluation template to determine an evaluation result, thereby determining whether the data to be evaluated has numerical value abnormality or not and efficiently determining the authenticity and the accuracy of the data to be evaluated.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
Fig. 2 shows another embodiment of the data evaluation method according to the present invention. The present embodiment is specifically described with reference to the scenario on the basis of the foregoing embodiment. The method specifically comprises the following steps:
In this embodiment, the type of the data to be evaluated may include accumulated value type data, instantaneous value type data, and state value type data. Assuming that the temperature data is to be evaluated in this embodiment, the temperature data should be regarded as instantaneous value data representing a "temperature state" of a specific place at a specific time. The time period is assumed to be 12 hours. Specifically, the form of the data to be evaluated for the temperature of the machine room acquired in this embodiment is shown in the following table:
| time of day | Temperature (degree centigrade) |
| 2019-12-12 01:00:00 | 21.2 |
| 2019-12-12 02:00:00 | 20 |
| 2019-12-12 03:00:00 | 20 |
| 2019-12-12 04:00:00 | 20 |
| 2019-12-12 05:00:00 | 22.0 |
| 2019-12-12 06:00:00 | 56.2 |
| 2019-12-12 07:00:00 | 22.3 |
| 2019-12-12 08:00:00 | 21.4 |
| 2019-12-12 09:00:00 | - |
| 2019-12-12 10:00:00 | 22.3 |
| 2019-12-12 11:00:00 | 22.1 |
| 2019-12-12 12:00:00 | 21.6 |
The data to be evaluated represent 12 months and 12 days in 2019, and the data to be evaluated represent 01: 00-12: and collecting temperature data of the machine room within the time range of 00. During which a total of 12 data points are obtained by sampling every hour.
In the embodiment, an application scene is assumed to be a machine room, and the temperature in the machine room is constant and floats within the range of 20-25 ℃. Then each evaluation item in the evaluation template can be determined by taking the evaluation item as a standard to judge whether the actually obtained data to be evaluated is in accordance with the objective condition. In this embodiment, the evaluation item may include a value being null, a value being unchanged, and a value exceeding a preset range (i.e., 20 to 25 ℃).
As can be seen from the above data to be evaluated, at 02: 00-04: 00 in three data points, the temperature is 20 ℃ and is not changed for a long time; 06: the value of 00 hours is 56.2 ℃, and exceeds the preset range; 09: the value at 00 is null. The evaluation item is combined for judgment, and the data points are considered to be abnormal data points.
The manner of determining the evaluation index from the respective evaluation items is as follows: assuming that the full score of a piece of data to be evaluated is 100 scores, the evaluation index determined by each evaluation item may be a score that should be deducted, the deducted score depending on the proportion of abnormal data points to all data points relative to the evaluation item and the weight of the evaluation item.
Taking the evaluation term of "constant value" as an example, there are 3 abnormal data points for the evaluation term, which are 02: 00-04: 00 three data points. The total number of data points was 12. The proportion of outlier data points to all data points is 25% to 3/12. In the embodiment, 3 evaluation items are involved, and each evaluation item has equal weight and is 1/3. Then, the score is converted into a 100-point system, which corresponds to 100 × 25% (1/3) ═ 8.3 points. The evaluation index corresponding to the evaluation item is 25 points. And similarly, the evaluation index corresponding to the numerical value being null is 2.8 points, and the evaluation index corresponding to the numerical value exceeding the preset range is 2.8 points.
And step 204, determining the evaluation result according to the evaluation index.
Further, the evaluation result was obtained as 86.1 points from 100 to 8.3 to 2.8.2.8.
It should be noted that, in this embodiment, the weights of the evaluation items are the same, and evaluation calculation is performed in a 100-point deduction calculation manner. In other cases, other different calculation methods may be adopted, which are not described herein.
Fig. 3 shows an embodiment of the data evaluation device according to the present invention. The apparatus of this embodiment is a physical apparatus for performing the method described in fig. 1-2. The technical solution is essentially the same as that in the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device in this embodiment includes:
the data obtaining module 301 is configured to obtain data to be evaluated and determine a data type of the data to be evaluated.
A template determining module 302, configured to determine a corresponding evaluation template according to the data type.
The evaluation module 303 is configured to evaluate a data value of the data to be evaluated by using the evaluation template to determine an evaluation result.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory into the memory and then runs the execution instruction, and the corresponding execution instruction can also be obtained from other equipment so as to form the data evaluation device on a logic level. The processor executes the execution instructions stored in the memory to implement the data evaluation method provided in any embodiment of the invention through the executed execution instructions.
The method performed by the data evaluation device according to the embodiment of the invention shown in fig. 3 can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
An embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores an execution instruction, and when the stored execution instruction is executed by a processor of an electronic device, the electronic device can be caused to execute the data evaluation method provided in any embodiment of the present invention, and is specifically configured to execute the method shown in fig. 1 or fig. 2.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method of data evaluation, comprising:
acquiring data to be evaluated, and determining the data type of the data to be evaluated;
determining a corresponding evaluation template according to the data type;
and evaluating the data value of the data to be evaluated by utilizing the evaluation template so as to determine an evaluation result.
2. The method of claim 1, wherein before obtaining the data to be evaluated, further comprising:
classifying the acquired original data to determine data to be evaluated;
the data to be evaluated comprises a data value, a data type and a time stamp.
3. The method of claim 1, wherein the obtaining data to be evaluated comprises:
and acquiring data to be evaluated in a preset time period.
4. The method of claim 1, further comprising:
and when the evaluation result meets a preset condition, generating early warning information aiming at the data to be evaluated, and pushing the early warning information.
5. The method according to any one of claims 1 to 4, wherein the data types include:
accumulated value type data, instantaneous value type data, and/or state value type data.
6. The method according to any one of claims 1 to 4, wherein the evaluation template comprises at least one evaluation item; then, the evaluating the data value of the data to be evaluated by using the evaluation template to determine an evaluation result includes:
determining an evaluation index of the data to be evaluated by using each evaluation item;
and determining the evaluation result according to the evaluation index.
7. The method of claim 6, wherein the evaluation term comprises:
a value of null, a value of 0, a value of negative, a value of constant, and/or a value of varying magnitude.
8. A data evaluation apparatus, comprising:
the data acquisition module is used for acquiring data to be evaluated and determining the data type of the data to be evaluated;
the template determining module is used for determining a corresponding evaluation template according to the data type;
and the evaluation module is used for evaluating the data value of the data to be evaluated by utilizing the evaluation template so as to determine an evaluation result.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the data evaluation method of any of claims 1 to 7.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the data evaluation method of any one of claims 1 to 7 when the processor executes the execution instructions stored by the memory.
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