Energy data quality evaluation method, system and storage medium

Abstract

The embodiment of the present application discloses a quality evaluation method, system and storage medium for energy data, the method comprising: the intelligent device acquires the energy data to be evaluated, classifies and identifies the energy data to obtain the first type corresponding to the energy data; the intelligent device extracts the first data template corresponding to the first type and the first construction method of the first data template, and reconstructs the energy data according to the first construction method to obtain energy construction data; the intelligent device compares the first data template with the energy construction data to determine the similarity, and determines the quality level of the energy data according to the similarity. The present application has the advantage of low cost.

Description

Technical Field

[0001] The present application relates to the field of energy and data processing technology, and in particular to a quality evaluation method, system and storage medium for energy data. Background Art

[0002] With the continuous progress of society, the importance of environmental protection is increasing. Therefore, the importance of detecting pollutant emissions from enterprises is becoming increasingly prominent. The detected data needs to be compared and scored with qualified data to determine whether environmental protection is qualified.

[0003] In the prior art, the verification process of system data quality requires manual comparison and judgment between experimental data and expected data. Due to the huge amount of data, this undoubtedly increases the intensity of work and the cost of data processing. Summary of the invention

[0004] The embodiments of the present application provide a method, system and storage medium for evaluating the quality of energy data, so as to automatically evaluate the energy data and thereby reduce the cost of data processing.

[0005] In a first aspect, a method for evaluating the quality of energy data is provided, the method comprising the following steps:

[0006] The smart device obtains the energy data to be evaluated, and performs classification and identification processing on the energy data to obtain a first type corresponding to the energy data;

[0007] The smart device extracts a first data template corresponding to the first type and a first construction method of the first data template, and reconstructs the energy data according to the first construction method to obtain energy construction data;

[0008] The smart device compares the first data template with the energy construction data to determine similarity, and determines the quality level of the energy data according to the similarity.

[0009] Optionally, the classifying and identifying the energy data to obtain the first type corresponding to the energy data specifically includes:

[0010] The source MAC address of the data packet of the energy data is obtained, and the source type of the energy data is obtained by querying from the mapping relationship between the MAC address and the destination according to the source MAC address, and the source type is determined to be the first type.

[0011] Optionally, the reconstructing the energy data according to the first construction method to obtain energy construction data specifically includes:

[0012] Get the number x of columns in the first construction method, the x parameters corresponding to each column, and the x sorting methods, extract y1 values ​​of the first parameter corresponding to the first column from the energy data, arrange the y1 values ​​according to the sorting method corresponding to the first column to obtain the first column data of the energy construction data, and traverse the x columns of data to obtain the energy construction data.

[0013] Optionally, the smart device comparing the first data template with the energy construction data to determine the similarity specifically includes:

[0014] The first data template is compared with the energy construction data row by row. If the first row of data of the first data template is the same as the first row of data of the energy construction data, the score is increased by 1. If they are not the same, the score is 0. All row of data of the first data template and all row of data of the energy construction data are traversed to obtain the total score of the energy construction data, and the total score is determined as the similarity value.

[0015] Optionally, the obtaining of the energy data to be evaluated specifically includes: collecting data values ​​of a complete cycle as the energy data to be evaluated.

[0016] Optionally, the smart device comparing the first data template with the energy construction data to determine the similarity specifically includes:

[0017] Calculate W differences between the first data template and the energy construction data, count the number w1 of differences that are not zero, and the similarity value = (W-w1) / W*100%; where W is the total number of values ​​of the first data template.

[0018] In a second aspect, a quality evaluation system for energy data is provided, the system comprising:

[0019] An acquisition unit, used for acquiring energy data to be evaluated;

[0020] A classification unit, used to classify and identify the energy data to obtain a first type corresponding to the energy data;

[0021] A processing unit is used to extract a first data template corresponding to a first type and a first construction method of the first data template, and reconstruct the energy data according to the first construction method to obtain energy construction data; compare the first data template with the energy construction data to determine the similarity, and determine the quality level of the energy data based on the similarity.

[0022] Optional,

[0023] The classification unit is specifically used to obtain the source MAC address of the data packet of the energy data, query the source type of the energy data from the mapping relationship between the MAC address and the destination according to the source MAC address, and determine that the source type is the first type.

[0024] Optional,

[0025] The processing unit is specifically used to obtain the number x of horizontal columns in the first construction method, the x parameters corresponding to each horizontal column, and the x sorting methods, extract y1 values ​​of the first parameter corresponding to the first horizontal column from the energy data, arrange the y1 values ​​according to the sorting method corresponding to the first horizontal column to obtain the first horizontal column data of the energy construction data, and traverse the x horizontal column data to obtain the energy construction data.

[0026] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0027] In a fourth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0028] It can be seen that the technical solution provided by the present application obtains the corresponding type by classifying and identifying the energy data, and then extracts the template data and construction method of the type, and constructs the energy data according to the construction method to obtain energy construction data, so as to achieve comparison with the template data to determine the similarity, and determine the quality level of the energy data based on the similarity. This method adopts automatic processing and automatic construction methods, which can improve data processing efficiency and reduce the cost of data quality evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 It is a flow chart of the energy data quality evaluation method provided in the embodiment of the present application.

[0031] Figure 2 It is a flowchart of the energy data quality evaluation method provided in Example 1 of the present application.

[0032] Figure 3A schematic diagram of the structure of an energy data quality evaluation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1 , Figure 1 A method for evaluating the quality of energy data is provided. The method can be executed by an intelligent device, such as a smart phone, tablet computer, personal computer, server, cloud platform, etc. The technical scenarios implemented by this embodiment are, for example, distributed photovoltaic power stations, photovoltaic power stations, smart power plants, substations, etc. Taking a distributed photovoltaic power station as an example, the distributed photovoltaic power station has multiple photovoltaic strings, smart devices and power equipment (such as inverters, transformers, etc.), wherein a communication connection is established between the smart device and the power equipment. The above communication connection includes but is not limited to: wired connection (such as optical fiber), and can also be a wireless connection (such as wifi, Bluetooth, etc.). Each photovoltaic string may include multiple photovoltaic panels, and the geographical locations between the multiple photovoltaic strings may be concentrated or spaced apart. The specific method is not limited in this application. Each photovoltaic string is electrically connected, such as Figure 1 As shown, the above method comprises the following steps:

[0035] Step S101: The smart device obtains energy data to be evaluated, and classifies and identifies the energy data to obtain a first type corresponding to the energy data;

[0036] For example, the above classification method for classifying and identifying energy data can be executed by using an existing classifier, such as a neural network model or a support vector machine, etc. to obtain the corresponding first type. Of course, in practical applications, the type can also be distinguished by obtaining the source of the energy data. For example, if the energy data is obtained from a distributed photovoltaic power station, the first type is determined to be a photovoltaic power station. If the energy data is obtained from a thermal power station, the first type is determined to be a thermal power station. If the energy data is obtained from a substation, the first type is determined to be a substation. Of course, there can be other ways to obtain the first type, which are not limited by this application.

[0037] In an optional technical solution, the above-mentioned classification and identification processing of the energy data to obtain the first type corresponding to the energy data may specifically include:

[0038] The source MAC address of the data packet of the energy data is obtained, and the source type of the energy data is obtained by querying from the mapping relationship between the MAC address and the destination according to the source MAC address, and the source type is determined to be the first type.

[0039] For example, if the source MAC address of the data packet is MAC1-MAC10, and it is found according to the mapping relationship that MAC1-MAC10 are all substations, then the first type is determined to be a substation.

[0040] Step S102: the smart device extracts a first data template corresponding to the first type and a first construction method of the first data template, and reconstructs the energy data according to the first construction method to obtain energy construction data;

[0041] For example, the energy construction data obtained by reconstructing the energy data in the first construction mode may specifically include:

[0042] Obtain the number x of horizontal columns in the first construction method, the x parameters corresponding to each horizontal column, and the x sorting methods (the x sorting methods can be based on the same sorting principle or different sorting principles), extract y1 values ​​of the first parameter corresponding to the first horizontal column from the energy data, arrange the y1 values ​​according to the sorting method corresponding to the first horizontal column to obtain the first horizontal column data of the energy construction data, and traverse the x horizontal column data to obtain the energy construction data.

[0043] Let's take a practical example to illustrate. Assume that x is 3 in the first construction mode, which are power, voltage, and current respectively. The three arrangement modes can all be arranged in ascending order by acquisition time. Assume that the energy data has 3 power data, P1, P2, and P3, 3 voltage data, V1, V2, and V3, and 3 current data, I1, I2, and I3; then the energy construction data after arrangement may include:

[0044] P1 P2 P3

[0045] V1 V2 V3

[0046] I1 I2 I3

[0047] Step S103: The smart device compares the first data template with the energy construction data to determine similarity, and determines the quality level of the energy data according to the similarity.

[0048] For example, the smart device comparing the first data template with the energy construction data to determine the similarity may specifically include:

[0049] The first data template is compared with the energy construction data row by row. If the first row of data of the first data template is the same as the first row of data of the energy construction data, the score is increased by 1. If they are not the same, the score is 0. All row of data of the first data template and all row of data of the energy construction data are traversed to obtain the total score of the energy construction data, and the total score is determined as the similarity value.

[0050] Of course, the above similarity can also be obtained in other ways, such as directly calculating the W differences between the first data template and the energy construction data, and counting the number of differences w1 that are not zero, and the similarity value = (W-w1) / W*100%; where W is the total number of values ​​of the first data template.

[0051] For example, determining the quality level of the energy data based on the similarity may specifically include:

[0052] A first interval in which the similarity value is located is determined according to the similarity value, and a first quality level corresponding to the first interval is determined as the quality level of the energy data.

[0053] Here it is assumed that the first data template has 100 horizontal columns of data, which are divided into 5 intervals, 0-20, 20-40, 40-60, 60-80, 80-100; the corresponding quality levels are: extremely poor, poor, medium, good, and excellent. Of course, the above quality levels can also be set to other levels, such as A, B, C, D, E, etc.

[0054] The technical solution provided in the present application obtains the corresponding type by classifying and identifying the energy data, then extracts the template data and the construction method of the type, and constructs the energy data according to the construction method to obtain the energy construction data, thereby realizing the similarity determined by comparison with the template data, and determining the quality level of the energy data according to the similarity. This method adopts automatic processing and automatic construction methods, which can improve data processing efficiency and reduce the cost of data quality evaluation.

[0055] For example, the above method may further include:

[0056] If the quality level is lower than the level threshold, the smart device sends an alarm message to the network side, and the alarm message includes: the quality level and the MAC address of the source.

[0057] Embodiment 1

[0058] Embodiment 1 of the present application provides a method for evaluating the quality of energy data. Figure 2 The flowchart of the quality assessment method of energy data is as follows: Figure 2 As shown, the above method comprises the following steps:

[0059] S200, collecting raw data, collecting the data to be scored through network transmission, USB flash drive copying, etc.;

[0060] S201, reading raw data, and importing the collected data into a computer or other display device with computing function;

[0061] S202, importing a fixed template, making an original qualified data template and importing it into the same computer or other display device with computing function as the one for importing the collected data;

[0062] S203, scanning and comparing data, comparing the collected data with the imported fixed template data row by row, the score of the data that matches the fixed template data is 1, otherwise it is 0;

[0063] S204, analyzing and calculating scores, adding up the accumulated scores to obtain a total score, and adding up the scan comparison scores to obtain a total score, the total score calculation formula is: (number of qualified data items / total number of data items)*100%;

[0064] S205, generating a score report, the score report is divided into five graded score systems: 0-20, 20-40, 40-60, 60-80, and 80-100, and the grades are arranged in order from top to bottom.

[0065] The embodiment of the present application utilizes a computer to automatically compare experimental data with qualified fixed template data, and scores and counts the experimental data by scanning the data similarity between the experimental data and the qualified fixed template data to produce a scoring report. This not only reduces the manual workload, but also greatly improves the efficiency of comparing and scoring experimental data, and is also convenient for organization and summary, and for easy reference.

[0066] The collected original data is a complete data value or a complete cycle of data value.

[0067] Read the original data and arrange the original data in the same layout as the fixed template.

[0068] Scan and compare the data, and compare the original data with the same data in the template data one by one. If the original data is the same as the template data, it is qualified and the score is increased by 1, otherwise the score is 0.

[0069] One of the embodiments;

[0070] Copy the experimental data to the computer via a USB flash drive.

[0071] The collected experimental data is read through calculation.

[0072] Then make a qualified experimental data table template and import it into the same computer.

[0073] Scan and compare the collected experimental data and the qualified experimental data table template one by one. If the collected experimental data is the same as the qualified experimental data table template, it is qualified and the score is increased by 1. Otherwise, the score is 0.

[0074] Finally, the scores are analyzed and calculated. The scores of the scan comparisons are accumulated and added up to get the total score, which is (the number of comparison score items / the total number of comparison items)*100%;

[0075] The computer can then be operated to export the score values ​​and arrange them from top to bottom according to the five-level score system of 0-20, 20-40, 40-60, 60-80, and 80-100, so that the score results can be viewed easily.

[0076] See also Figure 3 , Figure 3 A schematic diagram of the structure of an energy data quality evaluation system provided in this application, the system comprising:

[0077] An acquisition unit 301 is used to acquire energy data to be evaluated;

[0078] A classification unit 302, configured to classify and identify the energy data to obtain a first type corresponding to the energy data;

[0079] The processing unit 303 is used to extract a first data template corresponding to the first type and a first construction method of the first data template, and reconstruct the energy data according to the first construction method to obtain energy construction data; compare the first data template with the energy construction data to determine the similarity, and determine the quality level of the energy data based on the similarity.

[0080] Optional,

[0081] The classification unit is specifically used to obtain the source MAC address of the data packet of the energy data, query the source type of the energy data from the mapping relationship between the MAC address and the destination according to the source MAC address, and determine that the source type is the first type.

[0082] Optional,

[0083] The processing unit is specifically used to obtain the number x of horizontal columns in the first construction method, the x parameters corresponding to each horizontal column, and the x sorting methods, extract y1 values ​​of the first parameter corresponding to the first horizontal column from the energy data, arrange the y1 values ​​according to the sorting method corresponding to the first horizontal column to obtain the first horizontal column data of the energy construction data, and traverse the x horizontal column data to obtain the energy construction data.

[0084] Optional,

[0085] The processing unit is specifically used to compare the first data template with the energy construction data row by row. If the first row data of the first data template is the same as the first row data of the energy construction data, the score is increased by 1. If they are not the same, the score is 0. All row data of the first data template and all row data of the energy construction data are traversed to obtain the total score of the energy construction data, and the total score is determined as the similarity value.

[0086] Optional,

[0087] The acquisition unit is specifically used for acquiring the energy data to be evaluated, which specifically includes: collecting data values ​​of a complete cycle as the energy data to be evaluated.

[0088] Optional,

[0089] The processing unit is specifically used to calculate W differences between the first data template and the energy construction data, and count the number w1 of differences that are not zero, and the similarity value = (W-w1) / W*100%; wherein W is the total number of values ​​of the first data template.

[0090] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0091] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0092] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0093] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0094] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0095] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0096] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0097] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0098] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0099] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for evaluating the quality of energy data, characterized in that: The method comprises the following steps: The smart device obtains the energy data to be evaluated, and performs classification and identification processing on the energy data to obtain a first type corresponding to the energy data; The smart device extracts a first data template corresponding to the first type and a first construction method of the first data template, and reconstructs the energy data according to the first construction method to obtain energy construction data; The smart device compares the first data template with the energy construction data to determine similarity, and determines the quality level of the energy data according to the similarity; The energy data is reconstructed in the first construction mode to obtain energy construction data, which specifically includes: Obtain the number x of rows in the first construction method, x parameters corresponding to each row, and x sorting methods, extract y1 values ​​of the first parameter corresponding to the first row from the energy data, arrange the y1 values ​​according to the sorting method corresponding to the first row to obtain the first row data of the energy construction data, and traverse the x row data to obtain the energy construction data; The smart device compares the first data template with the energy construction data to determine the similarity specifically including: The first data template is compared with the energy construction data row by row. If the first row of data of the first data template is the same as the first row of data of the energy construction data, the score is increased by 1. If they are not the same, the score is 0. All row of data of the first data template and all row of data of the energy construction data are traversed to obtain the total score of the energy construction data, and the total score is determined as the similarity value.

2. The energy data quality evaluation method according to claim 1, characterized in that: The classifying and identifying the energy data to obtain the first type corresponding to the energy data specifically includes: The source MAC address of the data packet of the energy data is obtained, and the source type of the energy data is obtained by querying from the mapping relationship between the MAC address and the destination according to the source MAC address, and the source type is determined to be the first type.

3. The energy data quality evaluation method according to claim 1, characterized in that: The obtaining of the energy data to be evaluated specifically includes: collecting data values ​​of a complete cycle as the energy data to be evaluated.

4. The energy data quality evaluation method according to claim 1, characterized in that: The smart device compares the first data template with the energy construction data to determine the similarity specifically including: Calculate W differences between the first data template and the energy construction data, count the number w1 of differences that are not zero, and the similarity value = (W-w1) / W*100%; where W is the total number of values ​​of the first data template.

5. A quality evaluation system for energy data, characterized in that: The system comprises: An acquisition unit, used for acquiring energy data to be evaluated; A classification unit, used to classify and identify the energy data to obtain a first type corresponding to the energy data; a processing unit, configured to extract a first data template corresponding to the first type and a first construction method of the first data template, reconstruct the energy data according to the first construction method to obtain energy construction data; compare the first data template with the energy construction data to determine similarity, and determine a quality level of the energy data based on the similarity; The processing unit is specifically used to obtain the number x of horizontal columns in the first construction method, the x parameters corresponding to each horizontal column, and the x sorting methods, extract y1 values ​​of the first parameter corresponding to the first horizontal column from the energy data, arrange the y1 values ​​according to the sorting method corresponding to the first horizontal column to obtain the first horizontal column data of the energy construction data, and traverse the x horizontal column data to obtain the energy construction data; The comparing the first data template with the energy construction data to determine the similarity specifically includes: The first data template is compared with the energy construction data row by row. If the first row of data of the first data template is the same as the first row of data of the energy construction data, the score is increased by 1. If they are not the same, the score is 0. All row of data of the first data template and all row of data of the energy construction data are traversed to obtain the total score of the energy construction data, and the total score is determined as the similarity value.

6. The energy data quality evaluation system according to claim 5, characterized in that: The classification unit is specifically used to obtain the source MAC address of the data packet of the energy data, query the source type of the energy data from the mapping relationship between the MAC address and the destination according to the source MAC address, and determine that the source type is the first type.

7. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 4.

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