A method and system for detecting abnormality of connection terminals of a metering junction box

Abstract

The present invention relates to the technical field of abnormality detection of junction boxes, and specifically to a method and system for detecting abnormalities in the wiring terminals of a metering junction box. The present invention first screens out suspected normal terminals from all voltage terminals based on the voltage drop deviation between different output voltages of the voltage terminals; then obtains the temperature abnormality coefficient and input abnormality coefficient of each group of suspected normal terminals at each moment, and further determines the terminals to be analyzed by combining the change correlation between the input voltage and the temperature abnormality coefficient; further evaluates the output abnormality coefficient of each group of terminals to be analyzed, and determines the normal terminals by combining the temporal change correlation between the output voltage of each output terminal and the terminal temperature and the input abnormality coefficient. Based on the circuit characteristics of the voltage terminals and combined with the change correlation between temperature and voltage, the present invention discusses the voltage terminals in different situations to analyze and evaluate the abnormal possibility of each group of voltage terminals in real time and accurately, thereby improving the abnormality detection effect of the wiring terminals of the metering junction box.

Description

Technical Field

[0001] The present invention relates to the technical field of junction box abnormality detection, and in particular to a method and system for detecting abnormalities in connection terminals of a metering junction box. Background Art

[0002] The metering junction box is a crucial device in the power metering system. Its primary function is to connect and transmit signals such as current and voltage within the power system, enabling the metering, monitoring, and control of electrical energy. Terminal blocks are key mechanical components within the junction box that connect the wires. During use, these terminals can become loose, become damaged, or age, potentially causing serious safety incidents. Therefore, abnormality detection and regular maintenance of these terminals are crucial.

[0003] Currently, real-time electrical data from the junction box terminals can be monitored to analyze whether the terminals are abnormally faulty. However, the electrical data changes before the terminal fails are small, making it difficult to distinguish them from the electrical data fluctuations during normal operation. Potential fault signs, such as small electrical data fluctuations, may also be masked by environmental factors or system noise, resulting in the inability to timely discover potential fault hazards, which in turn leads to poor detection of abnormalities in the metering junction box terminals. Summary of the Invention

[0004] In order to solve the technical problem of poor detection effect of abnormal connection terminals of metering junction boxes, the purpose of the present invention is to provide a method and system for detecting abnormal connection terminals of metering junction boxes. The technical solution adopted is as follows:

[0005] A method for detecting abnormality of a connection terminal of a metering junction box, the method comprising:

[0006] In the metering junction box, obtain the terminal temperature, input voltage of each voltage terminal group at each moment, output voltage of each output terminal, and ambient temperature;

[0007] According to the deviation of the output voltage of different output terminals of each group of voltage terminals relative to the input voltage at each moment, the suspected normal terminal is determined from all voltage terminals;

[0008] For each group of suspected normal terminals, at each moment, the temperature anomaly coefficient is obtained based on the terminal temperature and its deviation from the ambient temperature. Based on the changes in the input voltage and the temperature anomaly coefficient, the input anomaly coefficient of each group of suspected normal terminals is obtained. Combined with the temporal correlation between the input voltage and the temperature anomaly coefficient, the normal terminals and the terminals to be analyzed are determined from all suspected normal terminals.

[0009] For each group of terminals to be analyzed, the output abnormality coefficient is obtained based on the output voltage change of each output terminal in the timing, and its association with the changes in the input voltage and terminal temperature, respectively, combined with the temperature abnormality coefficient; based on the association between the output voltage of each output terminal of each group of terminals to be analyzed and the timing change of the terminal temperature, and the timing change association between the input voltage and the temperature abnormality coefficient, combined with the input abnormality coefficient and the output abnormality coefficient, the normal terminals are determined from all the terminals to be analyzed.

[0010] Furthermore, the method for determining the suspected normal terminal includes:

[0011] At each moment, according to the difference between the output voltage and the input voltage of each output end of each group of voltage terminals, a voltage drop value of each output end is obtained; according to the voltage drop value of each output end and the difference between the voltage drop values of different output ends, an abnormal parameter of each group of voltage terminals is obtained;

[0012] For each group of voltage terminals, the moment when the abnormal parameter is greater than the preset parameter threshold is taken as the abnormal moment. When the total number of the abnormal moments in the preset historical period is less than the preset number threshold, the voltage terminal is determined to be a suspected normal terminal.

[0013] Furthermore, the method for obtaining the abnormal parameters includes:

[0014] For each group of voltage terminals, the mean of the voltage drop values of all output terminals is multiplied by the range of the voltage drop values of all output terminals, and a normalized value of the product is used as an abnormality parameter.

[0015] Furthermore, the method for obtaining the temperature anomaly coefficient includes:

[0016] At each moment, the difference between the terminal temperature of each group of suspected normal terminals and the ambient temperature is taken as the temperature deviation, and the sum of the temperature deviation and the terminal temperature is taken as the temperature anomaly coefficient of the suspected normal terminals.

[0017] Furthermore, the method for obtaining the input anomaly coefficient includes:

[0018] The maximum change voltage of the input voltage of each group of suspected normal terminals between all adjacent moments is used as the first input abnormality parameter; the maximum temperature abnormality coefficient is used as the second input abnormality parameter; the first input abnormality parameter and the second input abnormality parameter are combined to obtain the input abnormality coefficient of each group of suspected normal terminals.

[0019] Furthermore, the method for determining the normal terminals and the terminals to be analyzed includes:

[0020] For each group of suspected normal terminals, construct the input voltage sequence and temperature anomaly coefficient sequence based on the input voltage and temperature anomaly coefficient at each moment, and use the Pearson correlation coefficient between the two sequences as the first confidence parameter;

[0021] When the first confidence parameter is greater than a preset confidence threshold and the input abnormality coefficient is less than a preset abnormality threshold, the suspected normal terminal is determined to be a normal terminal, and the remaining suspected normal terminals except the normal terminal are used as terminals to be analyzed.

[0022] Furthermore, the method for obtaining the output abnormality coefficient includes:

[0023] Obtaining an abnormal reference weight for each output terminal based on a correlation between the output voltage of each output terminal of each group of terminals to be analyzed and the change in the terminal temperature in a time series, and a similarity between the fluctuation characteristics of the output voltage of each output terminal and the input voltage in a time series;

[0024] The maximum change voltage of the output voltage of each output terminal of each group of terminals to be analyzed between all adjacent moments is used as the first sub-abnormal parameter of each output terminal; the maximum temperature abnormality coefficient is used as the second sub-abnormal parameter; the first sub-abnormal parameter and the second sub-abnormal parameter are combined to obtain the sub-abnormal coefficient of each output terminal;

[0025] The abnormal reference weight of each output terminal is used to weight the corresponding sub-abnormal coefficient, and the normalized result of the weighted sum value of all output terminals is used as the output abnormal coefficient of the corresponding terminal to be analyzed.

[0026] Furthermore, the method for obtaining the abnormal reference weight includes:

[0027] For each group of terminals to be analyzed, between adjacent moments, based on the difference between the output voltage change of each output terminal and the input voltage change, a voltage difference is obtained and sorted in time sequence to construct a voltage difference sequence for each output terminal. Based on the difference between the terminal temperatures at adjacent moments, a terminal temperature difference sequence is constructed. The Pearson correlation coefficient between the voltage difference sequence and the terminal temperature difference sequence of each output terminal is used as the first normal parameter of each output terminal.

[0028] For each group of terminals to be analyzed, the mean of the output voltage variation between all adjacent moments and the standard deviation of all output voltages of each output terminal are used as vector elements to construct a fluctuation characteristic vector for each output terminal; the mean of the input voltage variation between all adjacent moments and the standard deviation of all input voltages are used as vector elements to construct an input fluctuation characteristic vector; the cosine similarity between the input fluctuation characteristic vector and the fluctuation characteristic vector of each output terminal is used as the second normal parameter of each output terminal;

[0029] The first normal parameter and the second normal parameter are fused, and a negative correlation mapping result of the fusion result is used as an abnormal reference weight of each output end.

[0030] Furthermore, the method for determining normal terminals from all terminals to be analyzed includes:

[0031] When the first confidence parameter is greater than a preset confidence threshold and the input abnormality coefficient is greater than a preset abnormality threshold, the terminal to be analyzed is determined to be an abnormal terminal, and the remaining terminals to be analyzed except the abnormal terminal are taken as target terminals;

[0032] For each group of target terminals, taking the mean of the first normal parameters of all output terminals as the second confidence parameter, fusing the second confidence parameter and the first confidence parameter, and taking the negative correlation mapping result of the fusion result as the abnormal confidence weight;

[0033] The output anomaly coefficient and the input anomaly coefficient are fused, the fusion result is weighted using the anomaly confidence weight, and the normalized value of the weighted result is used as the anomaly index of the target terminal; the target terminal whose anomaly index is less than the preset index threshold is regarded as a normal terminal.

[0034] The present invention also proposes a metering junction box terminal abnormality detection system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the metering junction box terminal abnormality detection method are implemented.

[0035] The present invention has the following beneficial effects:

[0036] The present invention first preliminarily screens out suspected normal terminals from all voltage terminals based on the voltage drop deviation between the input voltage and different output voltages of each group of voltage terminals in the metering junction box; then, for each group of suspected normal terminals, analyzes and evaluates the temperature anomaly coefficient of each group of suspected normal terminals at each moment, and obtains the input anomaly coefficient of each group of suspected normal terminals based on the characteristics that abnormalities such as poor contact at the input end of the voltage terminal may lead to similar voltage drops at different output ends and transient changes in input voltage and terminal temperature, and combines the temporal variation correlation between the input voltage and the temperature anomaly coefficient to provide a confidence reference, increase the consideration of the violent fluctuation of the input voltage itself, so as to accurately determine the normal terminals and the terminals to be analyzed from all suspected normal terminals; and then In the first step, for each group of terminals to be analyzed, based on the theoretical basis that input and output are equal voltage and temperature affects resistance and thus voltage, the output voltage change of each output terminal in the time sequence is analyzed to be associated with the change of input voltage and terminal temperature respectively. At the same time, combined with the characteristics that abnormalities such as poor contact of the voltage terminal output terminal may cause transient changes in output voltage and terminal temperature, the output abnormality coefficient of each group of terminals to be analyzed is comprehensively evaluated; then, the temporal change correlation between the output voltage of each output terminal of each group of terminals to be analyzed and the terminal temperature, and the temporal change correlation between the input voltage and the temperature abnormality coefficient are analyzed. Combined with the input abnormality coefficient and the output abnormality coefficient, the normal terminals are determined from all the terminals to be analyzed, and then all the abnormal terminals in the metering junction box are determined. Based on the circuit characteristics of the voltage terminals and combined with the change correlation between temperature and voltage, the present invention discusses the voltage terminals in different situations to analyze and evaluate the abnormal possibility of each group of voltage terminals in real time and accurately, thereby improving the abnormality detection effect of the terminal of the metering junction box. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 any creative work.

[0038] Figure 1 A flow chart of a method for detecting abnormalities in connection terminals of a metering junction box provided by one embodiment of the present invention;

[0039] Figure 2 A schematic diagram of a metering junction box provided by one embodiment of the present invention;

[0040] Figure 3 A flow chart of a method for obtaining an output anomaly coefficient provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, provides a detailed description of the specific implementation, structure, features, and effectiveness of a method and system for detecting abnormalities in connection terminals of a metering junction box according to the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0042] 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.

[0043] The specific scheme of the method and system for detecting abnormality of the connection terminals of a metering junction box provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0044] See also Figure 1 , which shows a flow chart of a method for detecting abnormality of a connection terminal of a metering junction box provided by one embodiment of the present invention, specifically comprising:

[0045] Step S1: In a metering junction box, the terminal temperature, input voltage, output voltage of each output terminal, and ambient temperature of each group of voltage terminals at each moment are obtained.

[0046] It should be noted that the abnormality detection in the embodiment of the present invention is only for the voltage terminal with a single input and multiple output structure in the metering junction box. The abnormality detection method for voltage terminals with different structures is the same. Here, only the voltage terminal with a single input and three output structure is used as an example for analysis and description.

[0047] See also Figure 2 , which shows a schematic diagram of a metering junction box provided by an embodiment of the present invention, Figure 2 There are 7 groups of terminals in total, including 4 groups of voltage terminals and 3 groups of current terminals. Each small rectangle corresponds to a group of terminals, and each group of terminals contains a terminal pick. Figure 2 The terminal on the middle vertical terminal paddle is the voltage terminal, which has one input and three outputs.

[0048] To perform real-time anomaly detection on the metering junction box, one embodiment of the present invention first installs a temperature sensor at the terminal paddle of each set of voltage terminals in the metering junction box, and installs voltage sensors on the transmission lines at the input and output ends respectively. At the same time, an environmental sensor is installed in a local area of the metering junction box, such as within a radius of 5 meters. The acquisition frequency of all sensors is set to 50Hz, which can also be customized by the implementer. Then, during the operation of the metering junction box, the terminal temperature of each set of voltage terminals is collected in real time using a temperature sensor, the input voltage of each set of voltage terminals and the output voltage of each output terminal are collected in real time using a voltage sensor, and the ambient temperature is collected in real time using an environmental sensor.

[0049] It should be noted that the implementer may also deploy corresponding sensors according to the type of voltage terminal and actual application, or use an infrared thermal imager to collect the terminal temperature; the selection, deployment and application of various sensors are all existing technologies and will not be repeated here.

[0050] It should be noted that, in one embodiment of the present invention, the preset historical time period of the current detection moment, such as the collected data within the historical 3 minutes, is used as the basis for analysis, that is, the terminal temperature, input voltage and output voltage of each output terminal of each group of voltage terminals at each moment within the historical 3 minutes are obtained to analyze and evaluate the abnormal possibility of each group of voltage terminals at the current moment; each moment described subsequently is a moment within the preset historical time period and will not be repeated; in other embodiments, the implementer can also customize the preset historical time period, but it should not be too large to avoid difficulty in capturing data fluctuation characteristics.

[0051] Step S2 : determining suspected normal terminals from all voltage terminals according to the deviation of the output voltages of different output terminals of each group of voltage terminals relative to the input voltages at each moment.

[0052] Considering that the voltage signal at the input terminal of a voltage terminal is usually distributed in parallel to multiple output terminals, theoretically, the output voltage of all output terminals should be the same as the input voltage of the input terminal. However, in actual applications, factors such as the wire impedance in the voltage terminal and the contact resistance of the connecting screws may cause a slight voltage drop at the output terminal. However, since the electrical connection paths of different output terminals are similar, the differences in wire impedance and contact resistance are small, and the voltage drop should also be relatively similar.

[0053] Based on this, the embodiment of the present invention will analyze the voltage drop conditions of different output terminals according to the deviation of the output voltage of different output terminals of each group of voltage terminals relative to the input voltage at each moment within a preset historical period, so as to first screen out abnormal terminals from all voltage terminals, and then treat the remaining voltage terminals as suspected normal terminals for further abnormal analysis.

[0054] Preferably, in one embodiment of the present invention, considering that at each moment, the greater the difference in voltage drops between different output terminals in each group of voltage terminals, the greater the possibility of abnormality, and the evaluation result at a single moment may be somewhat accidental, the more abnormal moments occur within a preset historical period, the more persistent the abnormality is, and the greater the possibility of abnormality of the voltage terminal; therefore, the method for determining suspected normal terminals includes:

[0055] At each moment, according to the difference between the output voltage and the input voltage of each output terminal of each group of voltage terminals, the voltage drop value of each output terminal is obtained; according to the voltage drop value of each output terminal and the difference between the voltage drop values of different output terminals, the abnormal parameters of each group of voltage terminals are obtained;

[0056] For each group of voltage terminals, the moment when the abnormal parameter is greater than the preset parameter threshold is regarded as the abnormal moment. When the total number of abnormal moments in the preset historical period is less than the preset number threshold, the voltage terminal is determined to be a suspected normal terminal.

[0057] In a preferred embodiment of the present invention, considering that the average voltage drop value of all output terminals in the voltage terminal can reflect the voltage drop degree, and the range of the voltage drop value can reflect the difference in the voltage drop degree, when the voltage drop degree is greater and the difference in the voltage drop degree is greater, it indicates that the possibility of abnormality of the group of voltage terminals is greater; therefore, the method for obtaining abnormal parameters includes:

[0058] For each group of voltage terminals, the mean of the voltage drop values of all output terminals is multiplied by the range of the voltage drop values of all output terminals, and the normalized value of the product is used as the abnormality parameter.

[0059] As an example, for each output end of each group of voltage terminals, the output voltage is subtracted from the input voltage and then divided by the input voltage to obtain the voltage drop value of each output end, and further obtain the abnormal parameters of each group of voltage terminals; the preset parameter threshold is set to 0.5 to determine all abnormal moments within the preset historical period; then the preset quantity threshold is set to 20% of the total number of moments in the preset historical period to determine whether the voltage terminal is a suspected normal terminal; voltage terminals other than suspected normal terminals will be directly regarded as abnormal terminals in the junction box.

[0060] It should be noted that, in other examples, the implementer may also define the preset parameter threshold and the preset quantity threshold by himself; in other embodiments, the implementer may also use variance instead of range to obtain abnormal parameters.

[0061] Step S3: For each group of suspected normal terminals, at each moment, the temperature anomaly coefficient is obtained based on the terminal temperature and its deviation from the ambient temperature; the input anomaly coefficient of each group of suspected normal terminals is obtained based on the changes in the input voltage and the temperature anomaly coefficient, and the normal terminals and terminals to be analyzed are determined from all suspected normal terminals in combination with the temporal change correlation between the input voltage and the temperature anomaly coefficient.

[0062] Considering that if there is an abnormality such as poor contact at the input end of the voltage terminal, the voltage drop degree at different output ends may still remain highly similar, the abnormality assessment method in step S2 may have misjudgments, that is, there is a possibility that abnormal terminals may still exist among the suspected normal terminals, and further analysis and evaluation is needed.

[0063] Poor contact at the voltage input terminal can cause a significant increase in terminal temperature. However, the ambient temperature fluctuation in a short period of time is relatively low, which can provide a reference for analyzing the terminal temperature fluctuation and help accurately assess whether the terminal temperature has increased abnormally.

[0064] Therefore, the embodiment of the present invention first obtains the temperature anomaly coefficient of each group of suspected normal terminals at each moment based on the terminal temperature of each group of suspected normal terminals and its deviation from the ambient temperature; the temperature anomaly coefficient evaluates whether each group of voltage terminals is abnormal from the perspective of thermal effect changes, preparing for the subsequent analysis of the possibility of abnormal terminals among the suspected normal terminals.

[0065] Preferably, in one embodiment of the present invention, the method for obtaining the temperature anomaly coefficient includes:

[0066] At each moment, the difference between the terminal temperature of each group of suspected normal terminals and the ambient temperature is taken as the temperature deviation, and the sum of the temperature deviation and the terminal temperature is taken as the temperature anomaly coefficient of the suspected normal terminals.

[0067] It should be noted that the difference is measured in the form of the absolute value of the difference. In the voltage terminal working process, the terminal temperature is usually higher than the ambient temperature, and the temperature deviation can also be directly measured in the form of the difference.

[0068] Considering that poor contact at the input end of the voltage terminal may significantly increase the contact resistance of the terminal, thereby causing large instantaneous fluctuations in the input voltage, and Joule heat will continue to accumulate after poor contact, causing the terminal temperature to rise significantly within a certain period of time; considering that the temperature anomaly coefficient not only reflects the possibility of abnormality of a suspected normal terminal, but its changes in time can also reflect the changes in terminal temperature;

[0069] Therefore, the embodiment of the present invention will obtain the input abnormality coefficient of each group of suspected normal terminals based on the changes in the input voltage and temperature abnormality coefficient; the input abnormality coefficient comprehensively evaluates the possibility of poor contact at the input end of the suspected normal terminal from two perspectives: the instantaneous fluctuation of the input voltage and the increase in the terminal temperature, preparing for subsequent further screening and evaluation.

[0070] Preferably, in one embodiment of the present invention, the difference in input voltage between adjacent moments of each group of suspected normal terminals within a preset historical period is taken into account to evaluate the instantaneous change degree thereof. The greater the instantaneous change in the input voltage, the greater the possibility of poor contact of the input terminal. Furthermore, considering that the higher the temperature anomaly coefficient, the higher the corresponding terminal temperature, the greater the possibility of poor contact of the input terminal. Therefore, the method for obtaining the input anomaly coefficient includes:

[0071] The maximum change voltage of the input voltage of each group of suspected normal terminals between all adjacent moments is used as the first input anomaly parameter; the maximum temperature anomaly coefficient is used as the second input anomaly parameter; the first input anomaly parameter and the second input anomaly parameter are combined to obtain the input anomaly coefficient of each group of suspected normal terminals.

[0072] It should be noted that the change voltage is the difference between the input voltage at each moment in the preset historical period and the input voltage at the previous adjacent moment. The maximum change voltage reflects the maximum instantaneous change in the input voltage; the maximum temperature anomaly coefficient is the maximum value of the temperature anomaly coefficients at all moments in the preset historical period.

[0073] As an example, the first input abnormality parameter and the second input abnormality parameter are multiplied and combined, and the product is linearly normalized to obtain the input abnormality coefficient of each group of suspected normal terminals; in other examples, the implementer can also use basic mathematical operations such as addition or weighted summation to fuse the two.

[0074] Considering that the input voltage may fluctuate significantly before being input to the voltage terminal, such fluctuations not caused by terminal abnormalities may also cause the input abnormality coefficient of the suspected normal terminal to be large. Therefore, the accuracy of assessing the abnormality possibility of the suspected normal terminal based solely on the input abnormality coefficient is not high;

[0075] Considering that there is a certain linear approximate relationship between temperature and resistance, changes in terminal temperature may cause changes in the contact resistance in the terminal, thereby causing changes in the input voltage. At the same time, changes in the temperature anomaly coefficient indirectly reflect changes in terminal temperature. Therefore, there is a certain change correlation between the temperature anomaly coefficient and the input voltage. In addition, sharp fluctuations in the input voltage itself may further increase the terminal temperature, thereby causing a relative deviation in the change correlation between the terminal temperature and the temperature anomaly coefficient and the input voltage.

[0076] Therefore, after obtaining the input anomaly coefficient of each group of suspected normal terminals, the embodiment of the present invention will further combine the temporal change correlation between the input voltage and the temperature anomaly coefficient to determine the normal terminals and the terminals to be analyzed from all suspected normal terminals; the temporal change correlation between the input voltage and the temperature anomaly coefficient can provide a certain confidence reference, increase the consideration of the violent fluctuation of the input voltage itself, so as to accurately analyze the abnormal possibility of the suspected normal terminals.

[0077] Preferably, in one embodiment of the present invention, considering that the Pearson correlation coefficient can reflect the change correlation between time series data, the first confidence parameter of the input abnormality coefficient of each group of suspected normal terminals can be obtained, and then the abnormal possibility of each suspected normal terminal can be evaluated in combination with the input abnormality coefficient for screening; therefore, the method for determining normal terminals and terminals to be analyzed includes:

[0078] For each group of suspected normal terminals, construct the input voltage sequence and temperature anomaly coefficient sequence based on the input voltage and temperature anomaly coefficient at each moment, and use the Pearson correlation coefficient between the two sequences as the first confidence parameter;

[0079] When the first confidence parameter is greater than the preset confidence threshold and the input abnormality coefficient is less than the preset abnormality threshold, the suspected normal terminal is determined to be a normal terminal, and the remaining suspected normal terminals except the normal terminal are used as terminals to be analyzed.

[0080] As an example, for each group of abnormal and normal terminals, the input voltages at each moment in the preset historical period are sorted in chronological order to construct an input voltage sequence, and similarly, a temperature anomaly coefficient sequence is constructed to obtain the first confidence parameter; then the preset confidence threshold is set to 0.8, and the preset abnormal threshold is set to 0.8, so as to determine all normal terminals and terminals to be analyzed in the suspected normal terminals.

[0081] It should be noted that the implementer may also use DTW similarity or other correlation measurement parameters instead of the Pearson correlation coefficient. Both DTW similarity and the Pearson correlation coefficient are already existing technologies and will not be described in detail.

[0082] It should be noted that when the preset signal threshold is greater than 0.8, the possibility of drastic fluctuations in the input voltage itself is excluded. On this basis, the normal terminals and abnormal terminals among the suspected normal terminals can be accurately screened out according to the input abnormality coefficient, among which the suspected normal terminals with a preset abnormality threshold less than 0.8 are normal terminals, and those greater than or equal to 0.8 are abnormal terminals; and when the preset signal threshold is less than or equal to 0.8, the fluctuation of the input voltage itself will interfere with the accuracy of the abnormality analysis and evaluation, and further analysis and evaluation are required, that is, the terminals to be analyzed include voltage terminals that are difficult to accurately analyze and evaluate the possibility of abnormality due to the drastic fluctuations in the input voltage itself, namely the target terminals described later. The embodiment of the present invention will further analyze and evaluate in step S4.

[0083] Step S4: For each group of terminals to be analyzed, the output abnormality coefficient is obtained based on the output voltage change of each output terminal in the timing, and the association with the changes of the input voltage and the terminal temperature respectively, combined with the temperature abnormality coefficient; based on the association between the output voltage of each output terminal of each group of terminals to be analyzed and the timing change of the terminal temperature, and the association between the input voltage and the timing change of the temperature abnormality coefficient, combined with the input abnormality coefficient and the output abnormality coefficient, the normal terminal is determined from all the terminals to be analyzed.

[0084] Considering that even if the input voltage fluctuates dramatically at the terminals to be analyzed, if the input voltage and the output voltage at each output terminal show similar fluctuations, the likelihood of an abnormality is lower. Furthermore, considering that changes in terminal temperature can also cause changes in the output voltages of different output terminals, the greater the correlation between terminal temperature and output voltage, the lower the likelihood of an abnormality at the terminals to be analyzed. Furthermore, considering that within each group of terminals to be analyzed, if each output terminal also has an abnormality such as poor contact, the output voltage will also experience large instantaneous fluctuations, and its temperature anomaly coefficient will also be larger, and the likelihood of an output abnormality at the output terminal will also be greater.

[0085] Therefore, in an embodiment of the present invention, for each group of terminals to be analyzed, the output voltage change of each output terminal in the time sequence, and the association with the change of the input voltage and the terminal temperature, respectively, are combined with the temperature anomaly coefficient to obtain the output anomaly coefficient; the output anomaly coefficient reflects the possibility of the output terminal of the terminal to be analyzed being abnormal, and prepares for the subsequent further screening of normal terminals among the terminals to be analyzed.

[0086] Preferably, in one embodiment of the present invention, the method for obtaining the output anomaly coefficient includes:

[0087] See also Figure 3 , which shows a flow chart of a method for obtaining an output anomaly coefficient provided by an embodiment of the present invention, specifically comprising:

[0088] Step S401 , obtaining an abnormal reference weight of each output terminal based on the correlation between the output voltage of each output terminal of each group of terminals to be analyzed and the terminal temperature in time series, and the similarity between the fluctuation characteristics of the output voltage of each output terminal and the input voltage in time series.

[0089] Taking into account the temporal correlation between the voltage change value and the terminal temperature change value at each output terminal of the terminal to be analyzed at adjacent moments, it can effectively reveal the dynamic interaction between voltage change and temperature change, providing a more accurate dynamic assessment of the temporal correlation between the output voltage and terminal temperature, thereby more accurately assessing the possibility of abnormality at the terminal to be analyzed. The Pearson correlation coefficient can reflect the correlation between the changes in time series data, so it can obtain the first normal parameter of each output terminal of the terminal to be analyzed.

[0090] Taking into account the mean and standard deviation of the voltage change values of the input voltage and output voltage at adjacent moments, their fluctuation characteristics can be reflected to a certain extent. After constructing the fluctuation characteristic vector using the mean and variance as vector elements, the cosine similarity between the vectors can be evaluated to reflect the similarity of the fluctuation characteristics of the output voltage and input voltage at each output terminal in the time series, thereby obtaining the second normal parameter.

[0091] Considering that both the first normal parameter and the second normal parameter reflect the normal possibility of the terminal to be analyzed, a negative correlation mapping adjustment logic is required after fusion to reflect the abnormal possibility of each output terminal, that is, to obtain the abnormal reference weight. The abnormal reference weight provides a reference basis for the subsequent analysis of the output abnormality coefficient of the terminal to be analyzed;

[0092] Based on this, in a preferred embodiment of the present invention, the method for obtaining an abnormal reference weight includes:

[0093] For each group of terminals to be analyzed, between adjacent moments, based on the difference between the output voltage change of each output terminal and the input voltage change, a voltage difference is obtained and sorted in time sequence to construct a voltage difference sequence for each output terminal. Based on the difference between the terminal temperatures at adjacent moments, a terminal temperature difference sequence is constructed. The normalized value of the Pearson correlation coefficient between the voltage difference sequence and the terminal temperature difference sequence of each output terminal is used as the first normal parameter of each output terminal.

[0094] For each group of terminals to be analyzed, the mean of the output voltage variation between all adjacent moments and the standard deviation of all output voltages are used as vector elements to construct a fluctuation feature vector for each output terminal. The mean of the input voltage variation between all adjacent moments and the standard deviation of all input voltages are used as vector elements to construct an input fluctuation feature vector. The normalized value of the cosine similarity between the input fluctuation feature vector and the fluctuation feature vector of each output terminal is used as the second normal parameter of each output terminal.

[0095] The first normal parameter and the second normal parameter are fused, and the negative correlation mapping result of the fusion result is used as the abnormal reference weight of each output end.

[0096] As an example, for each output terminal of each group of terminals to be analyzed, first, the output voltage at each moment in the preset historical period is subtracted from the output voltage at the previous adjacent moment to obtain the output change voltage at each moment; similarly, the input change voltage and terminal temperature difference at each moment are obtained; the difference between the output change voltage and the input change voltage at the same moment is taken as the voltage difference, and the voltage difference is sorted in chronological order to construct a voltage difference sequence; the terminal temperature difference is sorted in chronological order to construct a terminal temperature difference sequence; and then the Pearson correlation coefficient is obtained, and the Pearson correlation coefficient is used as The x in is normalized to be between 0 and 1. The closer x is to 1, the larger the first normal parameter of each output terminal is.

[0097] Similarly, the cosine similarity between the input fluctuation feature vector and the fluctuation feature vector of each output terminal is used as The y in the equation is normalized to a value between 0 and 1. The closer y is to 1, the larger the second normal parameter of each output terminal is. The first normal parameter is then multiplied and combined with the second normal parameter. The product is then added with a preset non-zero positive constant 0.001 and the reciprocal operation is performed to avoid the denominator being 0. The logic is adjusted and normalized to obtain the abnormal reference weight of the corresponding output terminal.

[0098] In other examples, implementers may also adopt other normalization means and negative correlation mapping means, or may combine the two by adopting basic mathematical operations such as addition or weighted summation.

[0099] In step S402, the maximum change voltage of the output voltage of each output terminal of each group of terminals to be analyzed between all adjacent moments is used as the first sub-abnormal parameter of each output terminal; the maximum temperature abnormality coefficient is used as the second sub-abnormal parameter; the first sub-abnormal parameter and the second sub-abnormal parameter are combined to obtain the sub-abnormal coefficient of each output terminal.

[0100] Based on the same acquisition logic and method as the input abnormality parameters in step S3, the sub-abnormality coefficient of each output terminal in each group of terminals to be analyzed is obtained. The sub-abnormality coefficient reflects the possibility of abnormalities such as poor contact at each output terminal, preparing for the subsequent comprehensive evaluation of the output abnormality coefficients of the terminals to be analyzed.

[0101] As an example, taking any output terminal as an example, the difference between the output voltage at each moment in the preset historical period and the output voltage at the previous adjacent moment is taken as the change voltage, and then the maximum change voltage is obtained to obtain the first sub-abnormality parameter; the maximum temperature abnormality coefficient of the terminal to be analyzed in the preset historical period is taken as the second sub-abnormality parameter; the first sub-abnormality parameter and the second sub-abnormality parameter are multiplied and combined, and the product is linearly normalized to obtain the sub-abnormality coefficient of the output terminal corresponding to the terminal to be analyzed; in other examples, the implementer can also use basic mathematical operations such as addition or weighted summation to merge the two.

[0102] Step S403 : weighting the corresponding sub-abnormality coefficient using the abnormality reference weight of each output terminal, and taking the normalized result of the weighted sum of all output terminals as the output abnormality coefficient of the corresponding terminal to be analyzed.

[0103] It should be noted that weighted summation is already an existing technology and will not be described in detail; linear normalization is specifically used, and implementers may also use other normalization methods.

[0104] Considering the temporal variation correlation between the output voltage of each output terminal of the terminal to be analyzed and the terminal temperature, as well as the temporal variation correlation between the input voltage and the temperature anomaly coefficient, both can provide a confidence reference for the normal voltage fluctuation in the terminal to be analyzed, and also provide a reference for the abnormality of the terminal to be analyzed; the input anomaly coefficient and the output anomaly coefficient both reflect the possibility of fault of the voltage terminal;

[0105] Therefore, the embodiment of the present invention will determine the normal terminals from all the terminals to be analyzed based on the correlation between the output voltage of each output terminal of each group of terminals to be analyzed and the time series change of the terminal temperature, and the correlation between the input voltage and the time series change of the temperature anomaly coefficient, combined with the input anomaly coefficient and the output anomaly coefficient.

[0106] Preferably, in one embodiment of the present invention, considering that the larger the first confidence parameter is, the more likely it is that there is a certain fluctuation correlation between the input voltage and the temperature anomaly parameter, and the higher the confidence of the input anomaly coefficient analyzed and evaluated on this basis, all abnormal terminals in the terminals to be analyzed can be preliminarily screened out based on the input anomaly coefficient, and the remaining terminals can be used as target terminals for further analysis;

[0107] Furthermore, considering that the first normal parameter reflects the temporal variation correlation between the output voltage of each output terminal of the terminal to be analyzed and the terminal temperature, and the first confidence coefficient reflects the temporal variation correlation between the input voltage and the temperature anomaly coefficient, both reflect the normal possibility of the terminal to be analyzed. Therefore, after fusing them and performing negative correlation mapping adjustment logic, they can serve as a reference for evaluating anomalies. Furthermore, the output anomaly coefficient and the input anomaly coefficient can be combined to comprehensively evaluate the abnormal possibility of the target terminal and screen normal terminals.

[0108] Based on this, the methods for determining normal terminals from all terminals to be analyzed include:

[0109] When the first confidence parameter is greater than a preset confidence threshold and the input abnormality coefficient is greater than a preset abnormality threshold, the terminal to be analyzed is determined to be an abnormal terminal, and the remaining terminals to be analyzed except the abnormal terminal are regarded as target terminals;

[0110] For each group of target terminals, the mean of the first normal parameters of all output terminals is used as the second confidence parameter, the second confidence parameter and the first confidence parameter are fused, and the negative correlation mapping result of the fusion result is used as the abnormal confidence weight;

[0111] The output anomaly coefficient and the input anomaly coefficient are fused, the fusion result is weighted using the anomaly confidence weight, and the normalized value of the weighted result is used as the anomaly index of the target terminal; the target terminal whose anomaly index is less than the preset index threshold is regarded as a normal terminal.

[0112] As an example, first set the preset confidence threshold to 0.8, and set the preset abnormal threshold to 0.8, and obtain the abnormal terminals and target terminals in the terminals to be analyzed; then calculate the average of the second confidence parameter and the first confidence parameter of the target terminal, and use the average as the x in the exponential function exp(-x) with the natural constant e as the base, and use the negative correlation mapping to obtain the abnormal confidence weight; then add the output abnormal coefficient and the input abnormal coefficient, multiply the sum by the abnormal confidence weight, and perform linear normalization to obtain the abnormal index of the target terminal; further set the preset index threshold to 0.8 to screen out normal terminals in the target terminals, and the remaining terminals in the target terminals except the normal terminals are abnormal terminals.

[0113] In other examples, implementers may also use other fusion methods such as multiplication or weighted summation, customize various thresholds, or use other negative correlation mapping methods such as inverse operations, which will not be repeated here.

[0114] In one embodiment of the present invention, based on the continuous screening in steps S2-S4, abnormal terminals among all voltage terminals can be identified. If any abnormal terminal is found, the metering junction box is abnormal, and the number of the voltage terminal with the abnormal fault can be returned, for example, V1, V2, V3, and V4 represent voltage terminals. When a voltage terminal abnormality is detected, a corresponding warning light is illuminated based on the severity of the abnormality. The abnormality number information is transmitted to the maintenance personnel's terminal device via the power grid communication system, such as power line carrier communication or wireless communication.

[0115] The present invention also proposes a metering junction box terminal abnormality detection system, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the above-mentioned metering junction box terminal abnormality detection method are implemented.

[0116] In summary, the present invention first preliminarily screens out suspected normal terminals from all voltage terminals based on the voltage drop deviation between the input voltage and different output voltages of each group of voltage terminals in the metering junction box; then, for each group of suspected normal terminals, analyzes and evaluates the temperature anomaly coefficient of each group of suspected normal terminals at each moment, and obtains the input anomaly coefficient of each group of suspected normal terminals. Further, combined with the temporal variation correlation between the input voltage and the temperature anomaly coefficient, the normal terminals and the terminals to be analyzed are accurately determined from all suspected normal terminals; for each group of terminals to be analyzed, the output anomaly coefficient of each group of terminals to be analyzed is first evaluated, and then the temporal variation correlation between the output voltage of each output terminal of each group of terminals to be analyzed and the terminal temperature, as well as the temporal variation correlation between the input voltage and the temperature anomaly coefficient, are analyzed. Combined with the input anomaly coefficient and the output anomaly coefficient, the normal terminals are determined from all terminals to be analyzed. Based on the circuit characteristics of the voltage terminals and combined with the variation correlation between temperature and voltage, the present invention discusses the voltage terminals in different situations to analyze and evaluate the abnormal possibility of each group of voltage terminals in real time and accurately, thereby improving the detection effect of abnormality of the metering junction box terminals.

[0117] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain 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 referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for detecting abnormality of a terminal block of a metering junction box, characterized in that: The method comprises: In the metering junction box, obtain the terminal temperature, input voltage of each voltage terminal group at each moment, output voltage of each output terminal, and ambient temperature; According to the deviation of the output voltage of different output terminals of each group of voltage terminals relative to the input voltage at each moment, the suspected normal terminal is determined from all voltage terminals; For each group of suspected normal terminals, at each moment, the temperature anomaly coefficient is obtained based on the terminal temperature and its deviation from the ambient temperature. Based on the changes in the input voltage and the temperature anomaly coefficient, the input anomaly coefficient of each group of suspected normal terminals is obtained. Combined with the temporal correlation between the input voltage and the temperature anomaly coefficient, the normal terminals and the terminals to be analyzed are determined from all suspected normal terminals. For each group of terminals to be analyzed, the output abnormality coefficient is obtained based on the output voltage change of each output terminal in the timing, and its association with the changes in the input voltage and terminal temperature, respectively, combined with the temperature abnormality coefficient; based on the association between the output voltage of each output terminal of each group of terminals to be analyzed and the timing change of the terminal temperature, and the timing change association between the input voltage and the temperature abnormality coefficient, combined with the input abnormality coefficient and the output abnormality coefficient, the normal terminals are determined from all the terminals to be analyzed.

2. A method for detecting abnormalities in connection terminals of a metering junction box according to claim 1, characterized in that: The method for determining the suspected normal terminal includes: At each moment, according to the difference between the output voltage and the input voltage of each output end of each group of voltage terminals, a voltage drop value of each output end is obtained; according to the voltage drop value of each output end and the difference between the voltage drop values of different output ends, an abnormal parameter of each group of voltage terminals is obtained; For each group of voltage terminals, the moment when the abnormal parameter is greater than the preset parameter threshold is taken as the abnormal moment. When the total number of the abnormal moments in the preset historical period is less than the preset number threshold, the voltage terminal is determined to be a suspected normal terminal.

3. A method for detecting abnormality of a connection terminal of a metering junction box according to claim 2, characterized in that: The method for obtaining the abnormal parameters includes: For each group of voltage terminals, the mean of the voltage drop values of all output terminals is multiplied by the range of the voltage drop values of all output terminals, and a normalized value of the product is used as an abnormality parameter.

4. The method for detecting abnormality of a terminal block of a metering junction box according to claim 1, wherein: The method for obtaining the temperature anomaly coefficient includes: At each moment, the difference between the terminal temperature of each group of suspected normal terminals and the ambient temperature is taken as the temperature deviation, and the sum of the temperature deviation and the terminal temperature is taken as the temperature anomaly coefficient of the suspected normal terminals.

5. The method for detecting abnormality of a terminal block of a metering junction box according to claim 1, wherein: The method for obtaining the input anomaly coefficient includes: The maximum change voltage of the input voltage of each group of suspected normal terminals between all adjacent moments is used as the first input abnormality parameter; the maximum temperature abnormality coefficient is used as the second input abnormality parameter; the first input abnormality parameter and the second input abnormality parameter are combined to obtain the input abnormality coefficient of each group of suspected normal terminals.

6. A method for detecting abnormality of a terminal block of a metering junction box according to claim 1, characterized in that: The method for determining the normal terminals and the terminals to be analyzed includes: For each group of suspected normal terminals, the input voltage sequence and temperature anomaly coefficient sequence are constructed based on the input voltage and temperature anomaly coefficient at each moment, and the Pearson correlation coefficient between the two sequences is used as the first confidence parameter. When the first confidence parameter is greater than a preset confidence threshold and the input abnormality coefficient is less than a preset abnormality threshold, the suspected normal terminal is determined to be a normal terminal, and the remaining suspected normal terminals except the normal terminal are used as terminals to be analyzed.

7. A method for detecting abnormality of a connection terminal of a metering junction box according to claim 6, characterized in that: The method for obtaining the output abnormality coefficient includes: Obtaining an abnormal reference weight for each output terminal based on a correlation between the output voltage of each output terminal of each group of terminals to be analyzed and the change in the terminal temperature in a time series, and a similarity between the fluctuation characteristics of the output voltage of each output terminal and the input voltage in a time series; The maximum change voltage of the output voltage of each output terminal of each group of terminals to be analyzed between all adjacent moments is used as the first sub-abnormal parameter of each output terminal; the maximum temperature abnormality coefficient is used as the second sub-abnormal parameter; the first sub-abnormal parameter and the second sub-abnormal parameter are combined to obtain the sub-abnormal coefficient of each output terminal; The abnormal reference weight of each output terminal is used to weight the corresponding sub-abnormal coefficient, and the normalized result of the weighted sum value of all output terminals is used as the output abnormal coefficient of the corresponding terminal to be analyzed.

8. A method for detecting abnormality of a connection terminal of a metering junction box according to claim 7, characterized in that: The method for obtaining the abnormal reference weight includes: For each group of terminals to be analyzed, between adjacent moments, based on the difference between the output voltage change of each output terminal and the input voltage change, a voltage difference is obtained and sorted in time sequence to construct a voltage difference sequence for each output terminal. Based on the difference between the terminal temperatures at adjacent moments, a terminal temperature difference sequence is constructed. The Pearson correlation coefficient between the voltage difference sequence and the terminal temperature difference sequence of each output terminal is used as the first normal parameter of each output terminal. For each group of terminals to be analyzed, the mean of the output voltage variation between all adjacent moments and the standard deviation of all output voltages of each output terminal are used as vector elements to construct a fluctuation characteristic vector for each output terminal; the mean of the input voltage variation between all adjacent moments and the standard deviation of all input voltages are used as vector elements to construct an input fluctuation characteristic vector; the cosine similarity between the input fluctuation characteristic vector and the fluctuation characteristic vector of each output terminal is used as the second normal parameter of each output terminal; The first normal parameter and the second normal parameter are fused, and a negative correlation mapping result of the fusion result is used as an abnormal reference weight of each output end.

9. A method for detecting abnormality of a connection terminal of a metering junction box according to claim 8, characterized in that: The method for determining normal terminals from all terminals to be analyzed comprises: When the first confidence parameter is greater than a preset confidence threshold and the input abnormality coefficient is greater than a preset abnormality threshold, the terminal to be analyzed is determined to be an abnormal terminal, and the remaining terminals to be analyzed except the abnormal terminal are taken as target terminals; For each group of target terminals, taking the mean of the first normal parameters of all output terminals as the second confidence parameter, fusing the second confidence parameter and the first confidence parameter, and taking the negative correlation mapping result of the fusion result as the abnormal confidence weight; The output anomaly coefficient and the input anomaly coefficient are fused, the fusion result is weighted using the anomaly confidence weight, and the normalized value of the weighted result is used as the anomaly index of the target terminal; the target terminal whose anomaly index is less than the preset index threshold is regarded as a normal terminal.

10. A metering junction box terminal abnormality detection system, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method implements the steps of the method for detecting abnormality of a connection terminal of a metering junction box according to any one of claims 1 to 9.

Images