A ring main unit temperature testing method and device

Abstract

The present invention relates to the technical field of ring network cabinets, and specifically to a temperature testing method and device for ring network cabinets. The invention analyzes the distribution of data within the neighborhood range of each position at the real-time moment, and obtains the temperature change amount and gas characterization concentration of each position at the real-time moment; takes the temperature change amount or gas characterization concentration as the data to be analyzed, and divides the data to be analyzed at all positions into high-order data sequences and low-order data sequences according to the spatial distribution of the temperature center position; analyzes the change trend of data between the data sequences of different data to be analyzed, and obtains the degree of deviation of each data to be analyzed; and then obtains the degree of temperature anomaly in the ring network cabinet at the real-time moment; and tests the temperature of the ring network cabinet. The present invention obtains the accurate degree of temperature anomaly of the ring network cabinet by comprehensively considering the changes in temperature data and gas concentration data, thereby improving the accuracy of temperature testing.

Description

Technical Field

[0001] The present invention relates to the technical field of ring main unit, and in particular to a temperature testing method and device for a ring main unit. Background Art

[0002] As a key device in the power distribution network, the ring main unit plays an important role in the distribution and control of electric energy. However, during operation, the temperature of the ring main unit may rise abnormally due to internal faults, overload or other reasons, which not only affects the normal operation of the equipment, but may also cause safety accidents and pose a threat to the stability of the power grid. Therefore, it is necessary to test the temperature inside the ring main unit to ensure its safe operation.

[0003] Considering that when the temperature rises abnormally, the insulating medium SF6 gas in the ring network cabinet expands, causing gas leakage; in the prior art, the gas leakage is judged based on the empirical threshold of the gas concentration and the temperature of the ring network cabinet is then detected; but due to the small change in gas concentration or large fluctuations in ambient temperature, the traditional empirical threshold detection cannot accurately reflect the actual temperature conditions inside the ring network cabinet, and the temperature test of the ring network cabinet is inaccurate. Summary of the invention

[0004] In order to solve the technical problem that the empirical threshold cannot accurately reflect the actual temperature condition inside the ring main unit, the purpose of the present invention is to provide a ring main unit temperature testing method and device, and the technical solution adopted is as follows:

[0005] The present invention proposes a ring main unit temperature testing method, the method comprising:

[0006] Acquire the temperature data and gas concentration data of each position in the vertical direction of the ring main unit in time sequence;

[0007] Analyze the distribution of data within the neighborhood of each position at the real time moment to obtain the temperature change and gas characterization concentration of each position at the real time moment; obtain the temperature center position according to the change trend of the temperature data of different positions within the neighborhood of the real time moment; use the temperature change or gas characterization concentration as the data to be analyzed, and divide the data to be analyzed at all positions into a high-order data sequence and a low-order data sequence according to the spatial distribution of the temperature center position;

[0008] For any data sequence, the sequence volatility of each data sequence is obtained according to the difference between adjacent data in the data sequence; the deviation degree of each data to be analyzed is obtained according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, as well as the difference in data distribution; the temperature anomaly degree in the ring network cabinet at the real time is obtained according to the deviation degree of different data to be analyzed at the real time and the data changes corresponding to different positions;

[0009] The temperature of the ring main unit is tested according to the temperature abnormality degree in the ring main unit at real time and the temperature data of each location.

[0010] Furthermore, the analysis of the distribution of data within the neighborhood of each location at the real-time moment includes:

[0011] For the temperature data, the difference between the corresponding temperature data of the right endpoint and the left endpoint of each position in the neighborhood at the real time is obtained as the endpoint temperature difference; the fitting straight line of the endpoint data is obtained, and the average of the difference between the fitting data and the temperature data of the corresponding fitting straight line at all moments in the neighborhood is calculated as the overall data difference; according to the endpoint temperature difference and the overall data difference, the temperature change of each position at the real time is obtained, and the endpoint temperature difference and the overall data difference are both positively correlated with the temperature change;

[0012] For gas concentration data, the mean of all gas concentration data within the neighborhood of each location at the real time is obtained as the gas characterization concentration of each location at the real time.

[0013] Furthermore, the method for obtaining the temperature center position includes:

[0014] Obtain the mean of all temperature data within the neighborhood of each location at the real time as the temperature mean;

[0015] The position corresponding to the maximum temperature mean among all positions is selected as the temperature center position.

[0016] Furthermore, dividing the data to be analyzed at all positions into a high-order data sequence and a low-order data sequence includes:

[0017] The sequence consisting of the temperature center position and all positions above the temperature center position corresponding to the data to be analyzed is taken as the high-order data sequence;

[0018] The sequence consisting of the temperature center position and all positions below the temperature center position corresponding to the data to be analyzed is taken as the low-order data sequence.

[0019] Furthermore, the method for obtaining the sequence volatility includes:

[0020] For any data sequence, the mean of the differences between all adjacent data in the data sequence is obtained as the sequence volatility of each data sequence at the real time.

[0021] Furthermore, the method for obtaining the degree of deviation of the data to be analyzed includes:

[0022] The mean of all data in each data sequence is obtained as the sequence average value; the difference in the sequence average values ​​between the high-order data sequence and the low-order data sequence corresponding to each data sequence to be analyzed is obtained as the sequence difference;

[0023] According to the sequence volatility of the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, the deviation weight of each data to be analyzed is obtained. For the temperature change, the sequence volatility of the high-order data sequence is positively correlated with the deviation weight, and the sequence volatility of the low-order data sequence is negatively correlated with the deviation weight; for the gas characterization concentration, the sequence volatility of the low-order data sequence is positively correlated with the deviation weight, and the sequence volatility of the high-order data sequence is negatively correlated with the deviation weight;

[0024] The sequence differences are weighted according to the deviation weight of each data to be analyzed, and normalized to obtain the deviation degree of each data to be analyzed.

[0025] Furthermore, the method for obtaining the degree of temperature anomaly includes:

[0026] Obtain the data mean of all positions corresponding to each type of data to be analyzed at the real time as the overall data level of each type of data to be analyzed;

[0027] The deviation degree of all the data to be analyzed and the overall data level are integrated, and positive correlation normalization mapping is performed to obtain the temperature abnormality degree in the ring network cabinet at the real time.

[0028] Further, the temperature of the ring main unit is tested according to the temperature abnormality degree of the ring main unit at the real time, including:

[0029] If the temperature abnormality of the ring main unit at the real time is greater than the preset temperature abnormality threshold, or the temperature data of at least one location at the real time is greater than the temperature threshold, it is determined that the temperature of the ring main unit is abnormal.

[0030] Furthermore, using The function performs positive correlation normalization mapping.

[0031] The present invention also proposes a ring main unit temperature testing device, comprising a data acquisition module, a data processing module, an abnormality analysis module, and a temperature testing module:

[0032] Data acquisition module: acquires temperature data and gas concentration data at each position in the vertical direction of the ring main unit in time sequence;

[0033] Data processing module: Analyze the distribution of data within the neighborhood range of the real time moment to obtain the temperature change and gas characterization concentration of each position at the real time moment; obtain the temperature center position according to the change trend of the temperature data of different positions within the neighborhood range of the real time moment; take the temperature change or gas characterization concentration as the data to be analyzed, and divide the data to be analyzed at all positions into high-order data sequence and low-order data sequence according to the spatial distribution of the temperature center position;

[0034] Abnormal analysis module: for any data sequence, the sequence volatility of each data sequence is obtained according to the difference between adjacent data in the data sequence; the deviation degree of the data to be analyzed is obtained according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence, as well as the difference in data distribution; the temperature abnormality degree in the ring network cabinet at the real time is obtained according to the deviation degree of different data to be analyzed at the real time and the data changes at the corresponding different positions;

[0035] Temperature test module: Test the temperature of the ring main unit according to the temperature abnormality in the ring main unit at real time and the temperature data of each location.

[0036] The present invention has the following beneficial effects:

[0037] The present invention analyzes the distribution of data of each position within the neighborhood range at the real time, obtains the temperature change amount and gas characterization concentration of each position at the real time, which is helpful to understand the change law of environmental parameters such as temperature and gas concentration over time and space; obtains the temperature center position according to the change trend of temperature data of different positions within the neighborhood range at the real time; takes the temperature change amount or gas characterization concentration as the data to be analyzed, and divides the data to be analyzed at all positions into high-order data sequences and low-order data sequences according to the spatial distribution of the temperature center position, so that the change trend and difference of data at different positions can be seen more clearly; for any data sequence, according to the difference between adjacent data in the data sequence, obtains the sequence volatility of each data sequence, reflecting the dynamic process and long-term trend of the data; according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, and the difference in data distribution, obtains the deviation degree of each data to be analyzed, and by measuring the data stability and consistency, the difference and change degree between the data can be quantified; according to the deviation degree of different data to be analyzed at the real time and the data change corresponding to different positions, obtains the temperature abnormality degree in the ring network cabinet at the real time, reflecting the current operation state of the ring network cabinet; and tests the temperature of the ring network cabinet. The present invention obtains the accurate temperature anomaly degree of the ring network cabinet by comprehensively considering the changes in temperature data and gas concentration data, thereby improving the accuracy of temperature testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flow chart of a ring main unit temperature testing method provided by one embodiment of the present invention;

[0040] Figure 2 A flow chart of a method for obtaining the degree of deviation of data to be analyzed provided by one embodiment of the present invention;

[0041] Figure 3 A schematic structural diagram of a temperature testing device for a ring main unit provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a ring network cabinet temperature testing method and device proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] The specific scheme of a ring main unit temperature testing method and device provided by the present invention is described in detail below with reference to the accompanying drawings.

[0045] See also Figure 1 , which shows a flow chart of a ring main unit temperature testing method provided by an embodiment of the present invention, and the specific method includes:

[0046] Step S1: Acquire temperature data and gas concentration data at each position in the vertical direction in the ring main unit in a time sequence.

[0047] In an embodiment of the present invention, taking into account slight changes in gas concentration, abnormal situations when temperature changes are large may be ignored. The internal temperature of the ring network cabinet is monitored in real time by a high-precision temperature sensor, and combined with the real-time data of SF6 gas concentration, advanced data analysis technology is used to analyze temperature anomalies, thereby improving the accuracy of temperature testing. First, since the internal equipment and components of the ring network cabinet are closely arranged in the vertical direction, certain fault characteristics are more likely to manifest themselves in the vertical direction. When the ring network cabinet produces temperature anomalies, the temperature and gas concentration change differently in different spatial distributions. Therefore, a number of temperature sensors and SF6 gas concentration detectors are installed at the same interval in the vertical direction on the inner side of the ring network cabinet. The temperature data and gas concentration data of each position in the vertical direction of the ring network cabinet are obtained in time sequence. Among them, in an embodiment of the present invention, the number of temperature sensors and SF6 gas concentration detectors installed can be set according to the specific situation, and the setting range is 3-7, which is not limited or elaborated here.

[0048] It should be noted that, in one embodiment of the present invention, the timing interval is 0.2 seconds; in other embodiments of the present invention, the size of the timing interval can be set according to specific circumstances, and is not limited or elaborated here.

[0049] It should be noted that in order to facilitate subsequent data processing, the data is smoothed through moving mean filtering to reduce noise and data fluctuations. In one embodiment of the present invention, the mean filter window size is set to 5; in other embodiments of the present invention, the mean filter window can be set according to specific circumstances, which is not limited or elaborated here; the specific moving mean filtering is a technical personnel in this field and will not be elaborated here.

[0050] Step S2: Analyze the distribution of data in the neighborhood range of each position at the real time moment to obtain the temperature change and gas characterization concentration of each position at the real time moment; obtain the temperature center position according to the change trend of the temperature data of different positions in the neighborhood range of the real time moment; take the temperature change or gas characterization concentration as the data to be analyzed, and divide the data to be analyzed at all positions into high-order data sequences and low-order data sequences according to the spatial distribution of the temperature center position.

[0051] When the ring main unit produces abnormal temperature, a large amount of heat, smoke and possible gas leakage will occur, which will change dynamically over time. In addition, the degree of diffusion at different locations is inconsistent, resulting in differences in temperature and gas concentration at different locations. Therefore, it is necessary to analyze the changes in data within the neighborhood range at real time to obtain the temperature change and gas characterization concentration at each location at real time; and consider the distribution of locations to perform layered analysis on the data.

[0052] The data distribution within the neighborhood of real-time data reflects the changes in the environmental status of each location, which helps to understand the changes in environmental parameters such as temperature and gas concentration over time and space. Therefore, the distribution of data within the neighborhood of each location at the real-time moment is analyzed to obtain the temperature change and gas characterization concentration of each location at the real-time moment.

[0053] Preferably, in one embodiment of the present invention, the distribution of data within the neighborhood of each location at the real time is analyzed, including:

[0054] For temperature data, the difference between the corresponding temperature data of the right endpoint and the left endpoint of each position in the neighborhood at the real time is obtained as the endpoint temperature difference; the fitting straight line of the endpoint data is obtained, and the average of the difference between the fitting data and the temperature data of the corresponding fitting straight line at all times in the neighborhood is calculated as the overall data difference; according to the endpoint temperature difference and the overall data difference, the temperature change of each position at the real time is obtained, and the endpoint temperature difference and the overall data difference are both positively correlated with the temperature change;

[0055] Among them, the positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases, that is, the greater the endpoint temperature difference, the greater the change in temperature data, the greater the overall data difference, and the greater the difference between the temperature data at each moment and the fitting straight line, that is, the greater the change in temperature data, the greater the temperature change.

[0056] In one embodiment of the present invention, the temperature variation is expressed as:

[0057] ;

[0058] ;

[0059] in, A serial number indicating a real time moment; Indicates real time Next The temperature change at each location; Indicates Locations at real time The difference between the corresponding temperature data between the right endpoint and the left endpoint within the neighborhood of is the endpoint temperature difference; Indicates Locations at real time The overall data difference within the neighborhood of ; Indicates that the fitted straight line is Locations at real time In the neighborhood of Fitting data at other moments; Indicates Locations at real time In the neighborhood of Other moments correspond to temperature data; represents the number of other moments in the neighborhood; represents the logistic function.

[0060] In the temperature change formula, if the end point temperature difference is larger, it means that the temperature data changes more; It represents the mean of the difference between the fitting data and the temperature data of the fitting straight line corresponding to all moments in the calculated neighborhood, that is, the overall data difference. The larger the overall data difference, the larger the difference between the fitting data and the temperature data at each moment, indicating that the temperature data changes from small to gradually accelerate, and the greater the change in temperature data. On the contrary, if the overall data difference is smaller, the difference between the fitting data and the temperature data at each moment is smaller, the temperature data change needs to be closer and closer to stability, and the temperature change is smaller.

[0061] For gas concentration data, the mean of all gas concentration data within the neighborhood of each location at the real time is obtained as the gas characterization concentration of each location at the real time.

[0062] It should be noted that the formula for characterizing gas concentration is: ;in, Indicates real time Next Gas characterization concentration at each location; Indicates real time In the neighborhood of Other moments Gas characterization concentration at each location; Represents the number of other moments in the neighborhood.

[0063] It should be noted that, in one embodiment of the present invention, the neighborhood range of the real-time moment is based on the real-time moment and is composed of a preset number of historical moments, and the preset number is 50; in other embodiments of the present invention, the size of the preset number can be set according to the specific circumstances, and is not limited or elaborated here.

[0064] It should be noted that, in some embodiments of the present invention, the method for obtaining the fitting straight line of the endpoint data is to adopt existing fitting methods such as the least squares method and polynomial fitting. The specific existing fitting methods such as the least squares method and polynomial fitting are technical means well known to those skilled in the art and will not be elaborated here.

[0065] Since the ring network cabinet generates heat during normal operation, the temperature at different locations is different. Since the heat in the ring network cabinet is mainly generated by the equipment usually located in the central area, the temperature distribution in the ring network cabinet radiates from one location to other locations. Therefore, the temperature center position is obtained based on the changing trend of temperature data within the neighborhood range of different locations at real time.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining the temperature center position includes:

[0067] The mean of all temperature data within the neighborhood of each location at the real time is obtained as the temperature mean; the location corresponding to the maximum temperature mean among all locations is selected as the temperature center location.

[0068] Using the temperature center position as the basis for division can reflect the impact of temperature on data analysis results, and facilitates differentiated analysis of data at different spatial locations; when temperature anomalies occur inside the ring network cabinet, the internal hot air and smoke float upward, causing temperature differences at different locations, and temperature anomalies cause gas leakage in the ring network cabinet, resulting in changes in gas at different locations; the division of high-order and low-order data sequences helps to reveal the spatial distribution characteristics of data, and reveal the inherent connection and change rules between data at different locations. Therefore, the temperature change or gas characterization concentration is used as the data to be analyzed, and according to the spatial distribution of the temperature center position, the data to be analyzed at all locations are divided into high-order data sequences and low-order data sequences.

[0069] Preferably, in one embodiment of the present invention, the data to be analyzed at all positions are divided into a high-order data sequence and a low-order data sequence, including:

[0070] The sequence consisting of the temperature center position and all positions above the temperature center position corresponding to the data to be analyzed is taken as the high-order data sequence;

[0071] The sequence consisting of the temperature center position and all positions below the temperature center position corresponding to the data to be analyzed is taken as the low-order data sequence.

[0072] As an example, if the data to be analyzed at all locations is , the temperature center position corresponds to , that is, the high-digit drama sequence is , the low-order data sequence is ;in, Indicates real time Next The data to be analyzed at each location.

[0073] Step S3: For any data sequence, the sequence volatility of each data sequence is obtained according to the difference between adjacent data in the data sequence; the degree of deviation of each data to be analyzed is obtained according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, as well as the difference in data distribution; the degree of temperature anomaly in the ring network cabinet at real time is obtained according to the degree of deviation of different data to be analyzed at real time and the data changes corresponding to different positions.

[0074] By analyzing the differences between adjacent data in a data sequence, the degree of change of the data sequence can be identified; sequence volatility can help evaluate the stability of the data sequence. A sequence with high volatility indicates that the data has greater uncertainty or rapid changes, while a sequence with low volatility may be more stable; so for any data sequence, the sequence volatility of each data sequence is obtained based on the differences between adjacent data in the data sequence.

[0075] Preferably, in one embodiment of the present invention, the method for obtaining the sequence volatility includes:

[0076] For any data sequence, the mean of the differences between all adjacent data in the data sequence is obtained as the sequence volatility corresponding to each data sequence at the real time moment.

[0077] In one embodiment of the present invention, taking any data sequence as an example, the formula for sequence volatility is expressed as:

[0078] ;

[0079] in, Indicates real time Corresponding data series The serial volatility of Indicates real time Corresponding data series No. individual data; Indicates real time Corresponding data series No. individual data; Represents a data sequence The amount of data above.

[0080] In the formula for series volatility, Indicates the calculation real time Corresponding data series No. Data and The difference between the data It means calculating the mean of the differences between all adjacent data. The larger the mean, the greater the difference between adjacent data, that is, the greater the rate of data change.

[0081] Since high-order and low-order data sequences have different diffusion rates or propagation characteristics, the difference in sequence volatility helps to reveal the diffusion characteristics of the data in space. The difference in data distribution reflects the uniformity of data changes. Therefore, the degree of deviation of each data to be analyzed is obtained based on the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, as well as the difference in data distribution.

[0082] Preferably, in one embodiment of the present invention, the method for obtaining the degree of deviation of the data to be analyzed is as follows: Figure 2 , which shows a flow chart of a method for obtaining the degree of deviation of data to be analyzed, including:

[0083] Step S201: Obtain the mean of all data in each data sequence as the sequence average; obtain the difference in the sequence averages between the high-order data sequence and the low-order data sequence corresponding to each data sequence to be analyzed as the sequence difference.

[0084] In order to facilitate data comparison, all data in each data sequence are quantitatively counted by averaging to reflect the general level of data in each data sequence, and compared by the sequence average value in the subsequent calculation process.

[0085] Step S202: According to the sequence volatility of the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, the deviation weight of each data to be analyzed is obtained. For the temperature change, the sequence volatility of the high-order data sequence is positively correlated with the deviation weight, and the sequence volatility of the low-order data sequence is negatively correlated with the deviation weight; for the gas characterization concentration, the sequence volatility of the low-order data sequence is positively correlated with the deviation weight, and the sequence volatility of the high-order data sequence is negatively correlated with the deviation weight.

[0086] As the hot air and smoke inside float upward, the data changes above the temperature center position are faster than the temperature changes below. Therefore, for the temperature change, the larger the deviation weight, the greater the data change in the high-order data sequence, and the more likely there is a temperature anomaly; since the gas density is higher than the air density, about 5 times the air density, the leaked gas will tend to diffuse to the position below the ring network cabinet, resulting in a greater change in the gas concentration below the temperature center position. Therefore, for the gas characterization concentration, the larger the deviation weight, the greater the data change in the low-order data sequence, the more likely there is a large gas leak and the more abnormal the temperature.

[0087] Step S203: weighting the sequence differences according to the deviation weight of each type of data to be analyzed, and normalizing them to obtain the deviation degree of each type of data to be analyzed.

[0088] In one embodiment of the present invention, the formula for the degree of deviation of the data to be analyzed is expressed as:

[0089] ;

[0090] ;

[0091] in, Indicates real time The degree of deviation of the temperature change; Indicates real time The degree of deviation of the concentration of the gas characterization; Indicates real time The lower temperature change corresponds to the sequence average of the high-order data sequence; Indicates real time The lower temperature change corresponds to the sequence average of the low-order data sequence; Indicates real time The lower temperature change corresponds to the sequence volatility of the high-order data sequence; Indicates real time The lower temperature change corresponds to the sequence volatility of the high-order data sequence; Indicates real time The lower gas characterization concentration corresponds to the sequence average of the high-order data sequence; Indicates real time The lower gas characterization concentration corresponds to the sequence average of the low-order data sequence; Indicates real time The sequence volatility of the lower gas characterization concentration corresponding to the low-order data sequence; Indicates real time The sequence volatility of the lower gas characterization concentration corresponding to the low-order data sequence; Indicates taking the absolute value; Represents the normalization function.

[0092] In the formula for the degree of deviation of the data to be analyzed, represents the difference in the series of temperature changes, Indicates the sequence difference of gas characterization concentration. The larger the sequence difference, the greater the difference in the sequence average between the high-order data sequence and the low-order data sequence, and the more likely there is a change difference; and Middle Pair and Add 0.01 to avoid the denominator of the formula being 0; represents the deviation weight of the temperature change, It represents the deviation weight of gas characterization concentration. The larger the deviation weight, the greater the deviation of the data to be analyzed.

[0093] The degree of deviation reflects the difference in changes between the data to be analyzed, and the data at different locations reflects the overall change of the data to be analyzed. The degree of temperature anomaly in the ring network cabinet is evaluated by comprehensively considering the degree of deviation and the change of position data; therefore, the degree of temperature anomaly in the ring network cabinet at real time is obtained according to the degree of deviation of different data to be analyzed at real time and the change of data at corresponding different locations.

[0094] Preferably, in one embodiment of the present invention, the method for obtaining the degree of temperature anomaly includes:

[0095] Obtain the data mean of all positions corresponding to each type of data to be analyzed at the real time as the overall data level of each type of data to be analyzed;

[0096] The deviation degree of all the data to be analyzed and the overall data level are integrated, and positive correlation normalization mapping is performed to obtain the temperature abnormality degree in the ring network cabinet at the real time.

[0097] It should be noted that, in some embodiments of the present invention, the deviation degrees of all the data to be analyzed and the overall data level can be integrated by addition or multiplication. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0098] In one embodiment of the present invention, the formula for the degree of temperature anomaly is expressed as:

[0099] ;

[0100] ;

[0101] ;

[0102] in, Indicates real time The abnormal temperature level in the lower ring main unit; Indicates real time The overall data level corresponding to the temperature change of the lower ring main unit; Indicates real time The degree of deviation of the temperature change; Indicates real time The overall data level corresponding to the gas characterization concentration of the lower ring main unit; Indicates real time The degree of deviation of the concentration of the gas characterization; Indicates real time Next The temperature change at each location; Indicates real time Next Gas characterization concentration at each location; Indicates the number of positions; represents the logistic function.

[0103] In the formula for temperature anomaly, the real time The greater the overall data level corresponding to the temperature change of the lower ring network cabinet, the greater the real-time The greater the deviation of the temperature change; Indicates real time The greater the overall data level corresponding to the gas characterization concentration of the lower ring main unit, Indicates real time The greater the deviation of the gas characterization concentration, the greater the abnormality and the greater the temperature abnormality.

[0104] Step S4: testing the temperature of the ring main unit according to the temperature abnormality degree in the ring main unit at the real time and the temperature data of each position.

[0105] The changes in temperature data and gas concentration data are comprehensively considered, the abnormal degree of temperature in the ring network cabinet is comprehensively evaluated, and the accuracy of temperature testing is improved; the temperature of the ring network cabinet is tested according to the abnormal degree of temperature in the ring network cabinet at real time and the temperature data of each location.

[0106] Preferably, in one embodiment of the present invention, the temperature of the ring main unit is tested according to the temperature abnormality degree in the ring main unit and the temperature data of each position at the real time, including:

[0107] If the temperature abnormality of the ring main unit at the real time is greater than the preset temperature abnormality threshold, or the temperature data of at least one location at the real time is greater than the temperature threshold, it is determined that the temperature of the ring main unit is abnormal.

[0108] It should be noted that, in one embodiment of the present invention, the size of the preset temperature anomaly threshold is 0.7, and the temperature threshold is 80°C; in one embodiment of the present invention, the size of the preset temperature anomaly threshold and the temperature threshold are technical means well known to those skilled in the art, and are not limited or elaborated here.

[0109] Based on the same application concept as the ring main unit temperature testing method provided in the embodiment of the present application, the present embodiment also proposes a ring main unit temperature testing device, such as Figure 3 As shown, the device includes: a data acquisition module 301, a data processing module 302, an abnormality analysis module 303 and a temperature testing module 304:

[0110] Data acquisition module 301: acquiring temperature data and gas concentration data at each position in the vertical direction of the ring main unit in a time sequence;

[0111] Data processing module 302: Analyze the distribution of data in the neighborhood of each position at the real time, obtain the temperature change and gas characterization concentration of each position at the real time; obtain the temperature center position according to the change trend of the temperature data of different positions in the neighborhood of the real time; take the temperature change or gas characterization concentration as the data to be analyzed, and divide the data to be analyzed at all positions into a high-order data sequence and a low-order data sequence according to the spatial distribution of the temperature center position;

[0112] Abnormal analysis module 303: for any data sequence, according to the difference between adjacent data in the data sequence, the sequence volatility of each data sequence is obtained; according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, and the difference in data distribution, the deviation degree of each data to be analyzed is obtained; according to the deviation degree of different data to be analyzed at real time and the data change corresponding to different positions, the temperature abnormality degree in the ring network cabinet at real time is obtained;

[0113] Temperature testing module 304: tests the temperature of the ring main unit according to the temperature abnormality degree in the ring main unit at the real time and the temperature data of each position.

[0114] It should be noted that the device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0115] It should be understood that the system provided in this embodiment is used to execute the above-mentioned ring main unit temperature testing method, and therefore has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0116] In summary, the present invention analyzes the distribution of data within the neighborhood range of each position at the real-time moment, obtains the temperature change amount and gas characterization concentration of each position at the real-time moment; takes the temperature change amount or gas characterization concentration as the data to be analyzed, and divides the data to be analyzed at all positions into high-order data sequences and low-order data sequences according to the spatial distribution of the temperature center position; for any data sequence, obtains the sequence volatility of each data sequence according to the difference between adjacent data in the data sequence; obtains the degree of deviation of each data to be analyzed according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, and the difference in data distribution; obtains the degree of temperature anomaly in the ring network cabinet at the real-time moment; and tests the temperature of the ring network cabinet. The present invention obtains the accurate degree of temperature anomaly of the ring network cabinet by comprehensively considering the changes in temperature data and gas concentration data, thereby improving the accuracy of temperature testing.

[0117] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A ring main unit temperature testing method, characterized in that: The method comprises: Acquire the temperature data and gas concentration data of each position in the vertical direction of the ring main unit in time sequence; Analyze the distribution of data within the neighborhood of each position at the real time moment to obtain the temperature change and gas characterization concentration of each position at the real time moment; obtain the temperature center position according to the change trend of the temperature data of different positions within the neighborhood of the real time moment; use the temperature change or gas characterization concentration as the data to be analyzed, and divide the data to be analyzed at all positions into a high-order data sequence and a low-order data sequence according to the spatial distribution of the temperature center position; For any data sequence, the sequence volatility of each data sequence is obtained according to the difference between adjacent data in the data sequence; the deviation degree of each data to be analyzed is obtained according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, as well as the difference in data distribution; the temperature anomaly degree in the ring network cabinet at the real time is obtained according to the deviation degree of different data to be analyzed at the real time and the data changes corresponding to different positions; The temperature of the ring main unit is tested according to the temperature abnormality degree in the ring main unit at real time and the temperature data of each location.

2. A ring main unit temperature testing method according to claim 1, characterized in that: The analysis of the distribution of data within the neighborhood of each location at the real-time moment includes: For the temperature data, the difference between the corresponding temperature data of the right endpoint and the left endpoint of each position in the neighborhood at the real time is obtained as the endpoint temperature difference; the fitting straight line of the endpoint data is obtained, and the average of the difference between the fitting data and the temperature data of the corresponding fitting straight line at all moments in the neighborhood is calculated as the overall data difference; according to the endpoint temperature difference and the overall data difference, the temperature change of each position at the real time is obtained, and the endpoint temperature difference and the overall data difference are both positively correlated with the temperature change; For gas concentration data, the mean of all gas concentration data within the neighborhood of each location at the real time is obtained as the gas characterization concentration of each location at the real time.

3. A ring main unit temperature testing method according to claim 1, characterized in that: The method for obtaining the temperature center position includes: Obtain the mean of all temperature data within the neighborhood of each location at the real time as the temperature mean; The position corresponding to the maximum temperature mean among all positions is selected as the temperature center position.

4. A ring main unit temperature testing method according to claim 1, characterized in that: The method of dividing the data to be analyzed at all positions into a high-order data sequence and a low-order data sequence includes: The sequence consisting of the temperature center position and all positions above the temperature center position corresponding to the data to be analyzed is taken as the high-order data sequence; The sequence consisting of the temperature center position and all positions below the temperature center position corresponding to the data to be analyzed is taken as the low-order data sequence.

5. A ring main unit temperature testing method according to claim 1, characterized in that: The method for obtaining the sequence volatility includes: For any data sequence, the mean of the differences between all adjacent data in the data sequence is obtained as the sequence volatility of each data sequence at the real time.

6. A ring main unit temperature testing method according to claim 1, characterized in that: The method for obtaining the degree of deviation of the data to be analyzed includes: The mean of all data in each data sequence is obtained as the sequence average value; the difference in the sequence average values ​​between the high-order data sequence and the low-order data sequence corresponding to each data sequence to be analyzed is obtained as the sequence difference; According to the sequence volatility of the high-order data sequence and the low-order data sequence corresponding to each data to be analyzed, the deviation weight of each data to be analyzed is obtained. For the temperature change, the sequence volatility of the high-order data sequence is positively correlated with the deviation weight, and the sequence volatility of the low-order data sequence is negatively correlated with the deviation weight; for the gas characterization concentration, the sequence volatility of the low-order data sequence is positively correlated with the deviation weight, and the sequence volatility of the high-order data sequence is negatively correlated with the deviation weight; The sequence differences are weighted according to the deviation weight of each data to be analyzed, and normalized to obtain the deviation degree of each data to be analyzed.

7. A ring main unit temperature testing method according to claim 1, characterized in that: The method for obtaining the temperature anomaly degree includes: Obtain the data mean of all positions corresponding to each type of data to be analyzed at the real time as the overall data level of each type of data to be analyzed; The deviation degree of all the data to be analyzed and the overall data level are integrated, and positive correlation normalization mapping is performed to obtain the temperature abnormality degree in the ring network cabinet at the real time.

8. A ring main unit temperature testing method according to claim 1, characterized in that: The testing of the temperature of the ring main unit according to the temperature abnormality of the ring main unit at the real time comprises: If the temperature abnormality of the ring main unit at the real time is greater than the preset temperature abnormality threshold, or the temperature data of at least one location at the real time is greater than the temperature threshold, it is determined that the temperature of the ring main unit is abnormal.

9. A ring main unit temperature testing method according to claim 7, characterized in that: use The function performs positive correlation normalization mapping.

10. A ring main unit temperature testing device, characterized in that: Including data acquisition module, data processing module, abnormal analysis module, temperature test module: Data acquisition module: acquires temperature data and gas concentration data at each position in the vertical direction of the ring main unit in time sequence; Data processing module: Analyze the distribution of data within the neighborhood range of the real time moment to obtain the temperature change and gas characterization concentration of each position at the real time moment; obtain the temperature center position according to the change trend of the temperature data of different positions within the neighborhood range of the real time moment; take the temperature change or gas characterization concentration as the data to be analyzed, and divide the data to be analyzed at all positions into high-order data sequence and low-order data sequence according to the spatial distribution of the temperature center position; Abnormal analysis module: for any data sequence, the sequence volatility of each data sequence is obtained according to the difference between adjacent data in the data sequence; the deviation degree of the data to be analyzed is obtained according to the difference in sequence volatility between the high-order data sequence and the low-order data sequence, as well as the difference in data distribution; the temperature abnormality degree in the ring network cabinet at the real time is obtained according to the deviation degree of different data to be analyzed at the real time and the data changes at the corresponding different positions; Temperature test module: Test the temperature of the ring main unit according to the temperature abnormality in the ring main unit at real time and the temperature data of each location.

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