SF6 density table data credibility evaluation method, storage medium and equipment

Abstract

The invention relates to an SF6 density table data credibility assessment method, a storage medium and equipment, and the method comprises the following steps: obtaining a group of to-be-assessed uploaded data of an SF6 density table, judging whether the group of to-be-assessed uploaded data is invalid data, if yes, setting a credibility assessment value to be 0, and if not, setting a credibility assessment value to be 0; if yes, credibility evaluation is carried out on the to-be-evaluated uploaded data based on a constructed data credibility evaluation model, and the data credibility evaluation model comprises data statistics credibility, data acquisition credibility and data content credibility. And obtaining a credibility evaluation value of the to-be-evaluated uploaded data based on weighted summation of the data statistics credibility, the data acquisition credibility and the data content credibility. Compared with the prior art, the method has the advantages that the accuracy of SF6 density table data credibility evaluation is effectively improved, and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of SF6 density monitoring, and in particular to a method, storage medium and device for evaluating the credibility of SF6 density meter data. Background Art

[0002] In high-voltage electrical equipment, SF6 gas is widely used as an insulating medium and arc-extinguishing medium. Because of the large-scale use of SF6 gas, its safety has also attracted widespread attention; objectively speaking, SF6 gas is a colorless, odorless, heavier than air, inert gas that is not easily mixed with air, and non-toxic to the human body. However, under the action of high-voltage arcs or high temperatures, SF6 gas will partially decompose, and its decomposition products are often highly toxic. Even a trace amount can cause death. When an indoor switch using SF6 gas as an insulating and arc-extinguishing medium leaks during use, the leaked SF6 gas and its decomposition products will accumulate in the lower space of the room, causing local hypoxia and toxicity, posing a serious danger to the life safety of the staff entering the room. Therefore, in order to ensure the safe and stable operation of the equipment, the density of SF6 gas must be monitored. Traditional density monitoring usually relies on the SF6 density meter installed in the equipment. However, since the density meter may be affected by various factors such as ambient temperature changes, mechanical vibration, and electromagnetic interference, the accuracy and reliability of its measurement data may be affected.

[0003] In the existing technology, the reliability of the measured data is usually improved by regularly calibrating the density meter or adopting a redundant design (installing multiple density meters), but these methods have problems such as high cost and complex operation. Therefore, there is an urgent need for a method that can evaluate the SF6 density meter data in real time to evaluate the credibility of the data and promptly detect and handle abnormal situations, thereby improving the safety and reliability of the power system. Summary of the invention

[0004] The purpose of the present invention is to provide a method, storage medium and device for evaluating the credibility of SF6 density meter data, which can effectively improve the accuracy of the credibility evaluation of SF6 density meter data by considering the characteristics of equipment data changes, verification conditions, historical case records and other aspects.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for evaluating the credibility of SF6 density table data comprises the following steps:

[0007] Obtain a group of uploaded data to be evaluated of the SF6 density table, determine whether the group of uploaded data to be evaluated is invalid data, if so, set the credibility evaluation value to 0, if not, perform credibility evaluation on the uploaded data to be evaluated based on the constructed data credibility evaluation model, wherein:

[0008] The data credibility evaluation model includes data statistical credibility, data collection credibility and data content credibility, and the credibility evaluation value of the uploaded data to be evaluated is obtained based on the weighted sum of the data statistical credibility, data collection credibility and data content credibility.

[0009] Furthermore, the invalid data includes one or more of a null value, a value out of range, and a value that does not conform to the SF6 gas state equation.

[0010] Further, the data statistical credibility is determined based on the weighted sum of the confidences of data mutation, recent statistical availability, and historical statistical availability;

[0011] The data collection credibility is determined based on the confidence of the verification situation;

[0012] The data content credibility is determined based on the confidence of the historical false alarm rate.

[0013] Furthermore, the weights used for weighted summation of the confidences of the data mutation, recent statistical availability and historical statistical availability are obtained based on the 9-scaling method and meet general consistency verification requirements.

[0014] Furthermore, the data mutation, the confidence of recent statistical availability and historical statistical availability, and the confidence of historical false alarm rate are obtained based on a preset state confidence score table.

[0015] Furthermore, the confidence score of the verification situation is obtained based on the following formula:

[0016]

[0017] Where T is the calibration cycle, t1 is the time of the most recent maintenance / calibration, and t2 is the credibility evaluation time.

[0018] Furthermore, the weights used in weighted summation of the data statistical credibility, data collection credibility and data content credibility are obtained based on the 9-scaling method and meet general consistency verification requirements.

[0019] Furthermore, the method further comprises:

[0020] When the credibility evaluation value is lower than a set threshold, an alarm signal indicating that the data is unavailable is generated.

[0021] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the SF6 density table data credibility assessment method as described above.

[0022] The present invention also provides an electronic device, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method for evaluating the credibility of SF6 density table data as described above.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention first determines whether the data is valid, and then evaluates the data credibility of the SF6 density meter based on the data validity, combining the equipment data change characteristics, verification conditions and historical case records, thereby effectively improving the accuracy of the SF6 density meter data credibility evaluation.

[0025] 2. The present invention can reduce the burden on staff through automated analysis and evaluation of data, timely discover equipment problems and take corresponding measures, and has great practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of the process of the evaluation method of the present invention;

[0027] Figure 2 A data credibility evaluation system constructed in the implementation of the present invention;

[0028] Figure 3 Schematic diagram of the confidence scoring curve for the verification situation used in the implementation. DETAILED DESCRIPTION

[0029] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0030] Example 1

[0031] This embodiment provides a method for evaluating the credibility of SF6 density table data. Figure 1 As shown, the method comprises the following steps:

[0032] S1. Obtain a group of uploaded data to be evaluated of the SF6 density table, and determine whether the group of uploaded data to be evaluated is invalid data. If so, set the credibility evaluation value to 0; if not, execute step S2;

[0033] S2. Perform credibility evaluation on the uploaded data to be evaluated based on the constructed data credibility evaluation model, wherein the data credibility evaluation model includes data statistical credibility, data collection credibility and data content credibility, and obtains the credibility evaluation value of the uploaded data to be evaluated based on the weighted sum of the data statistical credibility, data collection credibility and data content credibility.

[0034] The above method evaluates the credibility of SF6 density meter data by combining the equipment data change characteristics, verification status and historical cases, thereby improving the intelligence level and accuracy of SF6 density meter status evaluation, helping maintenance personnel to understand the operating status of the equipment in a timely manner, reducing the burden on staff, and promptly discovering equipment problems and taking corresponding measures.

[0035] The SF6 density table includes gas pressure, temperature, and density (expressed as SF6 gas pressure converted to 20°C). Invalid data usually includes the following: null value (data loss), value out of range, and value that does not conform to the SF6 gas state equation (the density value of SF6 gas is obtained by substituting the temperature and pressure values ​​into the formula). Invalid data is usually caused by electromagnetic interference during data transmission or disturbance of the sensor. Therefore, in step S1 of this embodiment, the invalid data is first evaluated, and the credibility evaluation value of the invalid data is directly set to 0.

[0036] In step S2, a data credibility evaluation model is constructed by a hierarchical decision method, such as Figure 2 As shown in the figure, the credibility of data is measured from three levels: data statistical credibility, data collection credibility and data content credibility. Among them, data statistical credibility describes the continuously sampled data and measures the data availability from a statistical perspective, including three indicators: data mutation, recent statistical availability and historical statistical availability, which are determined by the weighted sum of the confidence of data mutation, recent statistical availability and historical statistical availability; data collection credibility is used to describe the impact of data collection equipment on credibility, such as whether sensors and measuring equipment are maintained and calibrated on time, including an indicator of calibration status, that is, the confidence determination based on the calibration status; data content credibility is used to examine the quality of sampled data from the perspective of data content, including an indicator of historical false alarm rate, that is, the confidence determination based on the historical false alarm rate.

[0037] In this embodiment, the weights used in weighted summation of the data statistical credibility, data collection credibility and data content credibility are obtained based on the 9-scaling method and meet general consistency verification requirements; at the same time, the weights used in weighted summation of the confidence of the data mutation, recent statistical availability and historical statistical availability are also obtained based on the 9-scaling method and meet general consistency verification requirements.

[0038] In this embodiment, in order to determine the weight of each indicator, it is necessary to construct a relative importance matrix as shown below:

[0039]

[0040] The relative importance matrix A has the following properties:

[0041] (1)a ij >1;

[0042] (2)

[0043] (3)a ii =1;

[0044] Among them, a ij Represents element a i Element a j This embodiment uses the 9-scale method to compare the importance of two elements, and the scales between the indicators are shown in Table 1.

[0045] Table 1 Meaning of scales 1 to 9

[0046] Scale meaning 1 Indicates that two elements are equally important. 3 Indicates that compared with two elements, the former is slightly more important than the latter 5 Indicates that compared with two elements, the former is obviously more important than the latter 7 Indicates that compared with two elements, the former is more important than the latter 9 Indicates that compared with two elements, the former is extremely important than the latter 2，4，6，8 Indicates the middle value of the above adjacent judgments reciprocal <![CDATA[If the importance ratio of element i to j is a ij , the importance ratio of j to i is 1 / a ij >

[0047] For the online remote data of the SF6 density meter, the statistical characteristics of the data characterize the data stability during the continuous measurement process of the sensor, which is an important indicator for evaluating the data credibility. Since equipment calibration plays an important role in data credibility, the sampling credibility weight can be appropriately enhanced. Based on expert opinions and literature, the initial values ​​of the relative importance matrix are shown in Tables 2 to 5.

[0048] Table 2 Data credibility judgment matrix A

[0049] Statistical credibility Sampling Confidence Content Credibility Statistical credibility 1 2 5 Sampling Confidence 1 / 2 1 3 Content Credibility 1 / 5 1 / 3 1

[0050] Table 3 Statistical credibility judgment matrix A1

[0051] Data mutation Recent statistics availability Historical statistics availability Data mutation 1 1 / 2 2 Recent statistics availability 2 1 3 Historical statistics availability 1 / 2 1 / 3 1

[0052] Table 4 Sampling credibility judgment matrix A2

[0053] Verification Verification 1

[0054] Table 5 Content credibility judgment matrix A3

[0055]

[0056]

[0057] According to the relative importance matrix, find the maximum eigenvalue λmax The corresponding eigenvector ω is:

[0058] Aω=λ max ω#(2)

[0059] The required eigenvector is normalized to obtain the importance ranking of each evaluation factor, that is, weight matching.

[0060] The final weight distribution of the data credibility evaluation indicators after calculation is shown in Table 6.

[0061] Table 6 Weight distribution of data credibility evaluation indicators

[0062]

[0063] The one-sidedness of human thinking may make the constructed judgment matrix inconsistent. Whether the weight distribution obtained above is reasonable requires testing the general consistency of the relative importance matrix:

[0064]

[0065] Where CR is the random consistency ratio of the relative importance matrix; CI is the general consistency index of the relative importance matrix, and the calculation method is:

[0066]

[0067] RI is the average random consistency index of the relative importance matrix. For the relative importance matrices of order 1 to 9, the RI values ​​refer to the specified values, as shown in Table 7.

[0068] Table 7 Average random consistency index RI

[0069] n 1 2 3 4 5 6 7 8 9 RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45

[0070] When the CR of the relative importance matrix is ​​less than 0.1, or λ max When =n and CI=0, the matrix A is considered to have satisfactory consistency, otherwise the elements in A need to be adjusted until the consistency requirements are met.

[0071] The consistency check results are as follows, which meet the consistency requirements. The consistency check results are shown in Table 8.

[0072] Table 8 Consistency check results

[0073]

[0074] After confirming the weight of each indicator, it is necessary to perform confidence scoring on each indicator, where the confidence of data mutation, recent statistical availability and historical statistical availability, and the confidence of historical false alarm rate are obtained based on the pre-set state confidence scoring table. Specifically:

[0075] (1) Statistical credibility:

[0076] a) Data mutation:

[0077] Table 9 Data mutation confidence score

[0078] Status description Confidence score The data is stable and there is no sudden change 0.9 Data suddenly changes and has obvious "data anomaly" characteristics (i.e., a primary device leaks) 0.7 Data mutation, unable to identify the cause of the mutation 0.3 No historical data 0.5

[0079] b) Recent statistical availability:

[0080] Check the availability of the previous set of data of the current evaluation data (if the credibility is higher than the threshold, the data is available, and if the credibility is lower than the threshold, the data is not available);

[0081] Table 10 Recent statistical availability confidence scores

[0082] Status description Confidence score Statistics available 0.9 Statistics not available 0 No historical data 0.5

[0083] c) Historical statistics availability:

[0084] Check the availability of data for the previous period of time (select the time range based on sensor characteristics and sampling frequency, such as 1 day, 1 week, etc.) of the current evaluation data;

[0085] Table 11 Historical statistics available credibility confidence score

[0086] Status description Confidence score Statistics available 0.9 The overall statistics of data are available (the available data is higher than a certain percentage) 0.6 The overall statistics of the data are not available (the available data is below a certain percentage) 0 No historical data 0.5

[0087] (2) Sampling credibility:

[0088] Verification status:

[0089] like Figure 3 As shown, assuming that the specified calibration cycle is T, the time of the most recent maintenance / calibration is t1, and the credibility evaluation time is t2, the confidence score is calculated according to the following formula:

[0090]

[0091] (3) Content credibility:

[0092] Historical false positive rate:

[0093] Table 12 Historical false alarm rate confidence score

[0094] Status description Confidence score There are historical false positive statistics (false positive rate is a) 1-a No historical statistics 0.8

[0095] Finally, the credibility of the SF6 density table data is evaluated. The specific implementation method is as follows:

[0096] According to the constructed data credibility evaluation model, each indicator is scored for confidence according to the meter's measurement data, calibration status, and historical false alarm rate, and then the data credibility is calculated according to the weights of different indicators. If the credibility is higher than the set threshold, the data is judged to be available. If the credibility is lower than the set threshold, the data is judged to be unavailable and there is a problem with the meter.

[0097] If the above method 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 storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0098] Example 2

[0099] This embodiment provides an electronic device, including one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the SF6 density table data credibility assessment method as described in Example 1.

[0100] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0103] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0104] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating the credibility of SF6 density table data, characterized in that: The following steps are involved: Obtain a group of uploaded data to be evaluated of the SF6 density table, determine whether the group of uploaded data to be evaluated is invalid data, if so, set the credibility evaluation value to 0, if not, perform credibility evaluation on the uploaded data to be evaluated based on the constructed data credibility evaluation model, wherein: The data credibility evaluation model includes data statistical credibility, data collection credibility and data content credibility, and the credibility evaluation value of the uploaded data to be evaluated is obtained based on the weighted sum of the data statistical credibility, data collection credibility and data content credibility.

2. The SF6 density table data credibility assessment method according to claim 1, characterized in that: The invalid data includes one or more of a null value, a value out of range, and a value that does not conform to the SF6 gas state equation.

3. The SF6 density table data credibility assessment method according to claim 1, characterized in that: The statistical credibility of the data is determined based on the weighted sum of the confidence of data mutation, recent statistical availability and historical statistical availability; The data collection credibility is determined based on the confidence of the verification situation; The data content credibility is determined based on the confidence of the historical false alarm rate.

4. The method for evaluating the credibility of SF6 density table data according to claim 3, characterized in that: The weights used for weighted summation of the confidence of the data mutation, recent statistical availability and historical statistical availability are obtained based on the 9-scaling method and meet general consistency verification requirements.

5. The method for evaluating the credibility of SF6 density table data according to claim 3, characterized in that: The data mutation, the confidence of recent statistical availability and historical statistical availability, and the confidence of historical false alarm rate are obtained based on a preset state confidence score table.

6. The method for evaluating the credibility of SF6 density table data according to claim 3, characterized in that: The confidence score of the verification situation is obtained based on the following formula: Where T is the calibration cycle, t1 is the time of the most recent maintenance / calibration, and t2 is the credibility evaluation time.

7. The method for evaluating the credibility of SF6 density table data according to claim 1, characterized in that: The weights used in weighted summation of the data statistical credibility, data collection credibility and data content credibility are obtained based on the 9-scaling method and meet general consistency verification requirements.

8. The method for evaluating the credibility of SF6 density table data according to claim 1, characterized in that: The method further includes: When the credibility evaluation value is lower than a set threshold, an alarm signal indicating that the data is unavailable is generated.

9. A computer-readable storage medium, characterized in that: The method comprises one or more programs executed by one or more processors of an electronic device, wherein the one or more programs comprise instructions for executing the method for evaluating the credibility of SF6 density table data as claimed in any one of claims 1 to 8.

10. An electronic device, characterized in that: The system comprises one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method for evaluating the credibility of SF6 density table data as claimed in any one of claims 1 to 8.

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