A primary and secondary fusion ring network box operation state analysis method

Abstract

This application provides a method for analyzing the operating status of a primary and secondary integrated ring main unit, comprising: calculating the internal and external temperature difference based on the initial temperature dataset by subtracting the light-receiving temperature of the outer shell from the contact temperature; extracting temperature difference exceeding the limit segment to form an internal and external temperature difference sequence, which serves as the basis for subsequent solar interference identification; extracting the current data of each outgoing branch through the ring main unit's branch current acquisition unit, and combining it with the contact temperature data, the light-receiving temperature data of the outer shell, and the internal and external temperature difference sequence to form a comprehensive data set; using a preset temperature difference limit model to analyze the internal and external temperature difference sequence in the comprehensive data set; when the temperature difference exceeds the solar heating limit, activating the solar operating condition contact overheating judgment threshold; when it does not exceed the limit, using the conventional operating condition contact overheating judgment threshold.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for analyzing the operating status of a primary and secondary integrated ring network box. Background Technology

[0002] In the field of power equipment operation status analysis, ring main units (RMMs), as core nodes of urban medium-voltage distribution networks, directly affect the power supply reliability of the entire region due to their operational stability and safety. Abnormal contact temperatures inside RMMs often indicate potential faults, such as poor contact or overload. However, these abnormal signals are easily masked by external environmental factors, making accurate diagnosis difficult for maintenance personnel. Contact temperature is influenced by multiple factors, including load current, contact resistance, external solar radiation, internal airflow layout, and seasonal environmental changes. The interplay of these factors makes a single interpretation of temperature data unreliable. Traditional methods, based solely on temperature thresholds and load comparisons, ignore environmental interference and often misjudge non-faulty temperature increases as equipment defects. For example, during hot summer months, urban RMMs exposed to strong sunlight absorb radiant heat from the outer shell, which then conducts it into the interior. This causes a sharp rise in contact temperature in lightly loaded branches, resembling a fault in a heavily loaded branch, even though it is not a contact problem. This phenomenon is particularly pronounced in densely populated urban areas because reflected sunlight from buildings exacerbates the solar radiation effect, further amplifying interference in the heat conduction path of the outer shell.

[0003] Meanwhile, the air duct design within the ring main unit is uneven due to manufacturing processes or installation deviations. Ventilation openings near some contacts are blocked by partitions or cables, causing localized hot air stagnation and preventing effective exhaust. This leads to the formation of localized hot spots, resulting in abnormally high temperature readings even under normal overall load. Further complicating matters, during the early morning off-peak load period, the combination of increasing sunlight and air duct blockage can cause the temperature curve to show a false fault upward trend. Conversely, at night when there is no sunlight, increased humidity inside the unit alters heat transfer efficiency, interfering with baseline temperature judgment. Another typical contradiction is that urban ring main units are often located on sidewalks or roadsides. In summer, the road surface heat island effect transfers heat through the bottom of the unit, further distorting the contact temperature distribution. This causes low-load line temperatures to exceed the standard upper limit, which is then mistakenly interpreted as increased contact resistance. Air duct problems are exacerbated by the multi-circuit layout within the unit. Uneven heat island isolation between different branches leads to significant temperature differences between adjacent contacts, making it impossible for single sensor data to reflect the true global situation.

[0004] Such temperature anomalies caused by multiple coupled factors cannot be easily resolved by simply treating them as high-temperature power outages for maintenance. Unnecessary operations induced by light load conditions would increase costs and disturb residents. Ignoring environmental interference, on the other hand, could mask underlying contact problems and lead to sudden failures. This is particularly true in ring main unit (RNB) upgrade projects in older urban areas, where early duct design flaws coexist with modern high-density loads, resulting in stronger noise in temperature signals and further reducing the accuracy of assessments. Overall, accurately identifying the root causes of contact temperature anomalies by isolating interference from solar radiation, uneven ductwork, and the urban microenvironment has become a core challenge in improving the reliability of RNB condition assessments, directly hindering the advancement of intelligent power grid operation and maintenance. Summary of the Invention

[0005] This invention provides a method for analyzing the operating status of a primary and secondary integrated ring network enclosure, the method comprising: The contact temperature measurement unit and the outer shell temperature measurement unit of the ring network box collect contact temperature data and outer shell light-received temperature data to form an initial temperature dataset. The contact temperature data reflects the heating state of the wiring contacts, and the outer shell light-received temperature data reflects the solar heating state of the box. Based on the initial temperature dataset, the internal and external temperature difference is calculated by subtracting the light-received temperature of the outer shell from the contact temperature. The temperature difference exceeding the limit segment is extracted to form an internal and external temperature difference sequence, which serves as the basis for the criteria for identifying solar interference. The current data of each outgoing branch is extracted by the ring network box branch current acquisition unit and combined with the contact temperature data, the light-receiving temperature data of the outer shell, and the internal and external temperature difference sequence to form a comprehensive data set. The internal and external temperature difference sequence in the comprehensive data group is analyzed using a preset temperature difference limit model. When the temperature difference exceeds the solar heating limit, the solar heating condition contact overheating judgment threshold is activated. When the temperature difference does not exceed the limit, the conventional operating condition contact overheating judgment threshold is used. The contact temperature data in the comprehensive data group is evaluated based on the contact overheating judgment threshold after activation. The contact overheating record is extracted, and the suspected overheating of the light-load branch contact and the suspected overheating of the heavy-load branch contact are marked according to the rated current ratio of the branch, based on the current data of each outgoing branch, to form a classification anomaly label set. The integrated data set and the classification anomaly label set are input into the support vector machine algorithm. When the classification output shows a lightly loaded branch and the contact is overheated, the conclusion is that the air duct is blocked and the local high temperature is caused. When the classification output shows a heavily loaded branch and the contact is overheated, the conclusion is that the contact is poor. Based on the conclusion, a primary and secondary fusion ring network box operation status report is generated to form the final contact anomaly identification conclusion.

[0006] Preferably, the initial temperature dataset is formed by collecting contact temperature data and housing temperature data through the ring network box contact temperature measuring unit and the housing light-receiving temperature unit, wherein the contact temperature data reflects the heating state of the wiring contacts and the housing light-receiving temperature data reflects the solar heating state of the box, including: The contact temperature measuring unit arranged at each outgoing wire contact inside the ring network box uses an attached temperature measuring probe to contact the copper busbar connection surface to collect the heating temperature data of the wire contact. The outer shell temperature measuring unit arranged on the light-receiving surface of the top cover and side wall of the ring network box collects the light-receiving temperature data of the outer shell after the box is heated by sunlight, thus obtaining the original temperature sampling data with time stamp. The initial temperature dataset is formed by aligning and merging the heating temperature data and the outer shell light-receiving temperature data according to the same time scale, and then removing sampling segments with discontinuous time scales.

[0007] Preferably, the step of calculating the internal and external temperature difference based on the initial temperature dataset by subtracting the light-receiving temperature of the outer shell from the contact temperature, extracting temperature difference exceeding the limit segment, and forming an internal and external temperature difference sequence as the basis for identifying solar interference includes: For each outgoing branch contact position under the same sampling time scale, the internal and external temperature difference value is obtained by subtracting the corresponding shell light-receiving temperature data from the heating temperature data, and then arranged in time scale order to obtain point-by-point internal and external temperature difference time series data. The internal and external temperature difference value at each time point is compared with the preset internal and external temperature difference lower limit value. Sampling points that are lower than the internal and external temperature difference lower limit value are determined to be temperature difference exceeding the limit point. The temperature difference exceeding the limit points with adjacent time intervals lower than the preset time interval threshold are merged into the same exceeding limit segment. After removing the discrete exceeding limit points, the exceeding limit segments are arranged in the order of start and end time points to obtain the internal and external temperature difference sequence.

[0008] Preferably, the current data of each outgoing branch is extracted by the ring network box branch current acquisition unit and combined with the contact temperature data, the light-receiving temperature data of the outer casing, and the internal and external temperature difference sequence to form a comprehensive data set, including: The branch current acquisition unit is arranged at the outgoing end of each outgoing branch of the ring network box. The load current of the outgoing branch is sampled by the current transformer. The current data of each outgoing branch is read according to the sampling period consistent with the initial temperature dataset acquisition period. The current data of each outgoing branch is accompanied by the sampling time stamp and the outgoing branch number. Using the sampling time stamp and the outgoing branch number as index keys, the current data of each outgoing branch is aligned and merged with the heating temperature data, the light-receiving temperature data of the outer casing, and the internal and external temperature difference sequence to obtain the comprehensive data group.

[0009] Preferably, the step of analyzing the internal and external temperature difference sequence in the comprehensive data set using a preset temperature difference limit model, and activating the overheating judgment threshold for contacts under solar heating conditions when the temperature difference exceeds the solar heating limit, and using the conventional overheating judgment threshold for contacts when the limit is not exceeded, includes: The internal and external temperature difference sequence is retrieved from the comprehensive data set to obtain the preset solar heating limit. The solar heating limit is a limit determined based on the lower quantile of the difference between the heating temperature of the wiring contacts and the light-received temperature of the outer casing under historical no-sunlight conditions, and the value is lower than the lower limit of the internal and external temperature difference. The internal and external temperature difference values ​​in each exceeding segment of the internal and external temperature difference sequence are compared with the solar heating limit segment by segment. If the internal and external temperature difference value within a certain over-limit segment is lower than the solar heating limit, it is determined that the time period to which the over-limit segment belongs is under solar heating conditions, and the determination criterion for the corresponding outgoing branch contact position is switched to the preset solar heating condition contact overheating determination threshold. For cases where the internal and external temperature difference value in the internal and external temperature difference sequence is lower than the solar heating limit without any exceeding segment, the corresponding outgoing branch contact position uses the preset overheating judgment threshold for conventional operating conditions. For each sampling time marker indexed by outgoing branch number in the comprehensive data group, based on the operating condition judgment result of the corresponding time period, the overheating judgment threshold of the contact after activation is attached one by one to obtain a comprehensive data group with operating condition mark and corresponding overheating judgment threshold.

[0010] Preferably, the contact temperature data in the comprehensive data group is evaluated based on the activated contact overheating judgment threshold, contact overheating records are extracted, and combined with the current data of each outgoing branch, suspected overheating points of light-load branches and suspected overheating points of heavy-load branches are marked according to the rated current ratio of the branches, forming a classification anomaly label set, including: For each sampling time point of each outgoing branch, the overheating judgment threshold of the connected contact is used as the judgment benchmark. Sampling points with heating temperature data higher than the overheating judgment threshold of the connected contact are recorded in the contact overheating record. The contact overheating record is accompanied by an overheating time point and the outgoing branch number and contact position number. The load ratio is defined as the ratio of the load current amplitude to the rated current of the branch. Outgoing branches with a load ratio lower than the preset load ratio threshold are determined to be light-load branches, and those with a load ratio higher than the threshold are determined to be heavy-load branches. According to the load status of the outgoing branch, the suspected overheating of the contacts in the lightly loaded branch and the suspected overheating of the contacts in the heavy-load branch are marked respectively. The suspected items are summarized according to the outgoing branch number to form the classified anomaly mark set.

[0011] Preferably, the contact overheat recording further includes: Adjacent and consecutive contact overheating points under the same contact position in the same outgoing branch are merged into the same contact overheating record. The contact overheating record is accompanied by the overheating start and end time stamp, the outgoing branch number and the contact position number. After being arranged, it forms a list of overheating events with time tags. For each overheating start and end time period corresponding to each entry in the overheating event list, the load current amplitude of each outgoing branch within that time period is retrieved from the comprehensive data group. The ratio of the average value of the load current amplitude over the time period to the rated current of the corresponding branch is taken as the load ratio. The light-load branch and the heavy-load branch are marked based on the comparison result between the load ratio and the load ratio boundary value.

[0012] Preferably, the integrated data set and the classification anomaly label set are input into a support vector machine algorithm. When the classification output shows a lightly loaded branch and overheated contacts, the conclusion is drawn that the air duct is blocked, causing local high temperature. When the classification output shows a heavily loaded branch and overheated contacts, the conclusion is drawn that there is poor contact. Based on the conclusion, a primary and secondary fusion ring network box operation status report is generated to form the final contact anomaly identification conclusion, including: The data on heating temperature, shell light temperature, internal and external temperature difference, load current amplitude, load ratio and light and heavy load attributes corresponding to each suspicious item are retrieved from the comprehensive data group and the classification anomaly mark set. The suspicious items are then concatenated into multi-dimensional feature vectors to obtain a feature vector set that corresponds one-to-one with the suspicious items. The feature vector set is used as the input sample set of the support vector machine algorithm. The pre-trained support vector machine classifier is invoked. The support vector machine classifier takes the multi-dimensional feature vector as input and the anomaly type category as output. It outputs the anomaly type category corresponding to each suspicious item on the input sample set. The anomaly type category is one of the categories of local high temperature caused by air duct obstruction or poor contact of wiring terminals. Using the outgoing branch number and overheat start and end time stamp of the suspicious item as associated fields, the anomaly type category is attached to each item, and a report on the operation status of the primary and secondary fusion ring network box containing the anomaly type, the outgoing branch, and the time of occurrence is compiled to obtain the final contact anomaly identification conclusion.

[0013] Preferably, the generation of the primary and secondary integrated ring network box operation status report further includes: From the comprehensive data set and the classified anomaly mark set, retrieve the branch load status and contact overheating suspicion attributes one by one according to the suspicious item, obtain the branch number, contact position number, overheating start and end time mark and load ratio, and obtain the suspicious item association record linked according to the suspicious item; For entries with a branch load status of light load and suspected overheating of the branch contacts, the abnormality type category is linked as "local high temperature caused by air duct obstruction"; for entries with a branch load status of heavy load and suspected overheating of the branch contacts, the abnormality type field is linked as "poor contact of the wiring terminal", resulting in associated records with the abnormality type field. Using the anomaly type, the outgoing branch number, and the overheating start and end timestamps as core fields, the primary and secondary integrated ring network box operation status report is generated by summarizing the outgoing branch numbers.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a primary and secondary integrated ring main unit (RMU) operation status analysis method. Addressing the issue of contact overheating during RMU operation, it integrates multi-dimensional data such as contact temperature, shell temperature under sunlight, internal and external temperature difference, and branch current to solve the operational challenges of misjudging contact overheating and difficulty in identifying the causes of anomalies under solar interference. This invention collects contact and shell temperature data, calculates the internal and external temperature difference, and combines it with current data to form a comprehensive data set. It uses a temperature difference limit model to dynamically adjust the overheating judgment threshold, accurately distinguishing between solar heating and actual overheating patterns. Furthermore, it combines branch load status to classify and label suspected contact overheating under light and heavy loads. Using a support vector machine algorithm, it outputs anomaly conclusions such as duct obstruction or poor contact, ultimately generating an operation status report including the anomaly type, branch, and time of occurrence. This invention achieves intelligent identification and precise location of contact overheating causes, improves the reliability of RMU operation status monitoring and the targeted nature of anomaly handling, and provides technical assurance for the safe operation of power equipment. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for analyzing the operating status of a primary and secondary integrated ring network box according to the present invention.

[0016] Figure 2 This is a schematic diagram of a method for analyzing the operating status of a primary and secondary integrated ring network box according to the present invention.

[0017] Figure 3 This is another schematic diagram of the method for analyzing the operating status of a primary and secondary integrated ring network box according to the present invention. Detailed Implementation

[0018] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0019] like Figures 1-3 This embodiment of the method for analyzing the operating status of a primary and secondary integrated ring network box may specifically include: Step S101: Collect contact temperature data and shell temperature data through the ring network box contact temperature measurement unit and shell temperature measurement unit to form an initial temperature dataset. The contact temperature data reflects the heating state of the wiring contacts, and the shell temperature data reflects the solar heating state of the box.

[0020] Temperature measurement units at the contact points of each outgoing line within the ring main unit collect the heating temperature data of the contact points using attached temperature probes that contact the copper busbar connection surfaces. Similarly, temperature measurement units on the top and side walls of the ring main unit collect the solar-heated temperature data of the enclosure after exposure to sunlight, resulting in time-stamped raw temperature sampling data. Based on this raw temperature sampling data, the heating temperature data output by the contact point measurement units and the solar-heated temperature data output by the enclosure measurement units are aligned and merged according to the same time scale. After removing sampling segments with discontinuous time scales, an initial temperature dataset is formed that includes both the contact heating component and the enclosure solar-heated component.

[0021] In one embodiment, the contact temperature measuring unit uses a patch-type platinum resistance temperature sensor, which is attached to the copper busbar connection surface of the wiring contacts in the incoming cabinet, busbar cabinet and each outgoing cabinet of the ring network box. One sensor is arranged at each wiring contact. The sensor lead wires are connected to the acquisition module via shielded cables. The heating temperature data of the wiring contact is read at a fixed sampling period. The typical sampling period is 30 seconds to 60 seconds. The collected heating temperature data are accompanied by a time stamp of the sampling time.

[0022] For example, the outer shell temperature measurement unit uses an infrared non-contact temperature probe, which is respectively arranged in the middle of the top cover of the ring network box, on the sun-facing side wall, and on the shaded side wall, corresponding to the direct sunlight area and the shaded area. It collects the light-received temperature data and the shaded temperature data of the outer shell, respectively. The light-received temperature data reflects the heating state of the box under sunlight, and the shaded temperature data reflects the temperature state of the shaded area. All collected data includes a timestamp of the sampling time. To form an initial temperature dataset, temperature data collected from multiple locations are used as input, and a weighted average algorithm is used to calculate the representative temperature value T, i.e., T = w1 × T. top +w2×T sunny +w3×T shady (The weights have been normalized, w1+w2+w3=1), where T top T represents the temperature of the top cover. sunny Temperature on the sun-facing side, T shady For the backlight side temperature, the weights w1, w2, and w3 are determined based on the exposed area at each location; for example, w1 = 0.4, w2 = 0.4, and w3 = 0.2. Example: If T... top =36℃, T sunny =38℃, T shady=30℃, then T=0.4×36+0.4×38+0.2×30=14.4+15.2+6.0=35.6℃. The representative temperature value T can be used as a measure of the overall light heating level of the chamber, which complements the logic of calculating the temperature difference according to the temperature measurement location.

[0023] Specifically, the heating temperature data output by the contact temperature measuring unit and the housing light-receiving temperature data output by the housing temperature measuring unit are merged into the same data buffer. Using the sampling time as the index key, the data is aligned and merged line by line according to the time index. If a sampling segment is encountered due to communication interruption or sampling jump causing discontinuity in the time index, it is judged as an abnormal acquisition segment based on the interval between adjacent time indexes and is discarded. The merged data is indexed by the outgoing branch number and the temperature measuring position number to form an initial temperature dataset containing the contact heating component and the housing light-receiving heating component. In the subsequent calculation of the internal and external temperature difference, for each contact position, the housing temperature of the nearest housing temperature measuring position under the same outgoing branch is selected from the initial temperature dataset as a reference value, and the temperature difference ΔT = T is calculated. contact -T shell T contact T is the contact heating temperature. shell To select the shell temperature, if ΔT is lower than the preset threshold of 5.0°C, it is determined to be an over-limit temperature difference (suspected solar interference), and enters the over-limit segment processing flow of S102; if ΔT is significantly greater than the threshold, it reflects that the heat generation level of the contact itself is far greater than that of the shell, and enters the contact overheating judgment process.

[0024] Step S102: Based on the initial temperature dataset, calculate the internal and external temperature difference by subtracting the light-receiving temperature of the outer shell from the contact temperature, extract the temperature difference exceeding the limit segment, and form the internal and external temperature difference sequence, which serves as the basis for subsequent solar interference identification.

[0025] By retrieving the time-aligned heating temperature data and the shell light-receiving temperature data from the initial temperature dataset, for each outgoing branch contact position under the same sampling time point, the internal and external temperature difference value is obtained by subtracting the corresponding shell light-receiving temperature data from the heating temperature data. The internal and external temperature difference values ​​are arranged in time-order to obtain point-by-point internal and external temperature difference time-series data. Based on the internal and external temperature difference time-series data, a preset lower limit value for internal and external temperature difference is obtained. For each time point, the internal and external temperature difference value is compared with the lower limit value. If the internal and external temperature difference value is lower than the lower limit value, the sampling point under that time point is determined to be a temperature difference violation point. For consecutive temperature difference violation points, based on whether the time interval between adjacent violation points is lower than a preset time interval threshold, adjacent violation points with a time interval lower than the time interval threshold are merged into the same violation segment. After removing discrete single violation points, each violation segment is arranged in the order of start and end time points to obtain the internal and external temperature difference sequence, which serves as the basis for subsequent solar interference identification.

[0026] In one embodiment, the physical meaning of the internal and external temperature difference refers to the difference between the temperature of the wiring contacts themselves and the temperature of the enclosure shell heated by sunlight. Under normal operating conditions without sunlight, the wiring contacts heat up due to the load current passing through the copper busbar connection surface, and their temperature is usually higher than the temperature of the enclosure shell. The internal and external temperature difference is positive and relatively large. When the enclosure shell is heated by strong solar radiation, the shell temperature rises sharply, even approaching or exceeding the temperature of the wiring contacts. The internal and external temperature difference shrinks to near zero or even becomes negative. This temperature difference trend reflects the operating condition characteristic of solar radiation heat penetrating into the enclosure through conduction from the shell.

[0027] Specifically, the heating temperature data and the light-receiving temperature data of the outer casing, aligned with the time scale, are retrieved from the initial temperature dataset. For each contact position of each outgoing branch, subtraction is performed time-by-time to obtain point-by-point internal and external temperature difference time series data. The internal and external temperature difference time series data are archived according to the outgoing branch number and the contact position number.

[0028] It should be noted that the lower limit of the internal and external temperature difference is not a fixed value, but a judgment benchmark pre-calibrated based on the historical meteorological data and historical temperature collection records of the urban area where the ring network box is located.

[0029] Preferably, the historical temperature records of the ring network box under light load conditions and no sunlight at night are statistically analyzed. The difference sequence between the heat generation temperature data and the light-received temperature data of the outer shell is calculated. An empirical distribution function is used to statistically analyze the difference distribution. After extracting the distribution, the lower quantile is taken as the calibration basis for the lower limit of the internal and external temperature difference. The lower quantile is defined as the percentage of data points in the distribution that are lower than this value. For example, when calculating the 5th quantile, the input difference sequence is such as d1=2.5, d2=3.0, d3=1.8, d4=4.2, d5=2.1. The sequence is sorted in ascending order as [1.8, 2.1, 2.5, 3.0, 4.2]. Using linear interpolation, the position p = 0.05 × (n-1) + 1 = 0.05 × 4 + 1 = 1.2, the integer part k = 1, and the fractional part f = 0.2. The output value P5 is calculated by linear interpolation as P5 = d3 + f × (d5 - d3) = 1.8 + 0.2 × (2.1 - 1.8) = 1.86.

[0030] For example, the lower limit of the internal and external temperature difference is 5 degrees Celsius, which means that under normal operating conditions, the temperature of the wiring contact should be at least 5 degrees Celsius higher than the temperature of the housing under sunlight. If the temperature of the wiring contact is less than 5 degrees Celsius higher than the temperature of the housing under sunlight at a certain time, or even lower than the temperature of the wiring contact, it indicates that the heating component of the housing under sunlight has increased abnormally, which has approached or exceeded the heating component of the contact itself, and there is a suspicion of solar radiation interference.

[0031] In one possible implementation, the internal and external temperature difference values ​​in the internal and external temperature difference time-series data are retrieved time-by-time and compared with the lower limit of the internal and external temperature difference. If the internal and external temperature difference value is lower than the lower limit, the sampling point at that time-time is marked as a temperature difference exceeding the limit point, and the exceeding time-time, the corresponding outgoing branch number, and the contact position number are noted, thus obtaining a set of temperature difference exceeding points point-by-point. Furthermore, the selection of the time-time interval threshold is matched with the sampling period.

[0032] Preferably, the time interval threshold is 2 to 3 times the sampling period. For example, when the sampling period is 30 seconds, the time interval threshold is 90 seconds.

[0033] It is understandable that a single timescale exceeding the limit may be caused by sampling jumps or transient disturbances, and does not represent a continuous solar radiation interference process. The exceeding points in the set of temperature difference exceeding points are arranged in chronological order, and the timescale interval between adjacent exceeding points is calculated pairwise. If the timescale interval is lower than the timescale interval threshold, the adjacent exceeding points are classified into the same exceeding segment; if the timescale interval is higher than the timescale interval threshold, the previous exceeding point is considered the end of an ended segment, and the subsequent exceeding point is considered the start of a new exceeding segment. Discrete segments containing only a single exceeding point are discarded, and segments containing two or more consecutive exceeding points are retained. The start timescale, end timescale, and duration of each exceeding segment are recorded.

[0034] For example, the internal and external temperature difference sequence is arranged in the order of the start time of the over-limit segment. Each over-limit segment is accompanied by the branch number of its corresponding outgoing line, the contact position number, the start and end time, and the internal and external temperature difference value of each point in the segment. The internal and external temperature difference sequence serves as the basis for subsequent solar interference identification.

[0035] Step S103: Extract the current data of each outgoing branch through the ring network box branch current acquisition unit, and combine it with the contact temperature data, the light-receiving temperature data of the outer shell, and the internal and external temperature difference sequence to form a comprehensive data set.

[0036] By using branch current acquisition units located at the outgoing terminals of each branch of the ring main unit, the load current of the outgoing branch is sampled by a current transformer. The current data of each outgoing branch is read at a sampling period consistent with the initial temperature dataset acquisition period (e.g., 30 seconds or 60 seconds). The initial temperature dataset refers to the initial temperature data of the ring main unit previously acquired by a temperature sensor. Each outgoing branch current data is accompanied by a sampling timestamp and the outgoing branch number. Based on the outgoing branch current data, using the sampling timestamp and the outgoing branch number as index keys, the outgoing branch current data is aligned and merged with the heating temperature data, the outer shell light-receiving temperature data, and the internal and external temperature difference sequence to obtain a comprehensive data set covering the outgoing branch current data, the heating temperature data, the outer shell light-receiving temperature data, and the internal and external temperature difference sequence.

[0037] In one embodiment, the branch current acquisition unit uses a through-core low-voltage current transformer, which is respectively installed on the outgoing cable core of each outgoing branch in the ring network box. The secondary side lead of the current transformer is connected to the acquisition module shared with the contact temperature measurement unit and the shell temperature measurement unit via a shielded cable. The load current amplitude of the outgoing branch is sampled. The acquired current data of each outgoing branch is archived according to the outgoing branch number and accompanied by a time stamp of the sampling time.

[0038] Specifically, the sampling period of the branch current acquisition unit is the same as the acquisition period of the initial temperature dataset, for example, 30 seconds or 60 seconds. The sampling clock is uniformly synchronized by the acquisition module, so that the current data of each outgoing branch, the heating temperature data, and the light-receiving temperature data of the outer casing correspond one-to-one at the same sampling time. Further, the current data of each outgoing branch, the heating temperature data, the light-receiving temperature data of the outer casing, and the internal and external temperature difference sequence obtained in the previous step are merged into the same data merging area, using the sampling time stamp as the primary index key and the outgoing branch number as the secondary index key, and are aligned and merged one by one. After merging, each outgoing branch is associated with a record under each sampling time point. The record contains the load current amplitude of the branch under that time point, the heating temperature data of the corresponding contact position, and the housing light-receiving temperature data of the corresponding housing light-receiving surface position. The start and end time points of each exceeding segment in the internal and external temperature difference sequence to which the branch belongs are associated using the following rules: The input is a list of exceeding segments, and each segment contains start and end time points. For each sampling time point t, a loop algorithm is used to check if start≤t≤end. If so, the start and end time points of the segment are associated and the flag bit isexceed=1 is set; otherwise, is exceed=0.

[0039] For example, if the segment is [10:00, 10:30] and t = 10:15, then associate it and mark it as is exceed = 1; the output is the associated segment and flag bit of each record, resulting in a comprehensive data group indexed by the outgoing branch number.

[0040] Step S104: Use a preset temperature difference limit model to analyze the internal and external temperature difference sequence in the comprehensive data set. When the temperature difference exceeds the solar heating limit, activate the solar heating condition contact overheating judgment threshold. When it does not exceed the limit, use the conventional operating condition contact overheating judgment threshold.

[0041] By retrieving the internal and external temperature difference sequence from the comprehensive data set, a preset solar heating limit is obtained. The solar heating limit is a limit value determined in advance based on the lower quantile of the difference between the heating temperature of the wiring contacts and the light-received temperature of the casing under historical no-sunlight conditions, and the value is lower than the lower limit of the internal and external temperature difference. The internal and external temperature difference values ​​in each exceeding-limit segment of the internal and external temperature difference sequence are compared with the solar heating limit segment by segment. According to the results of the segment-by-segment comparison, if the internal and external temperature difference value in a certain exceeding-limit segment is lower than the solar heating limit, it is determined that the time period to which the exceeding-limit segment belongs is under solar light conditions, and the determination criterion for the corresponding outgoing branch contact position under this time period is switched to the preset solar light condition contact overheating determination threshold; if the internal and external temperature difference value in the internal and external temperature difference sequence without exceeding-limit segments is lower than the solar heating limit, the preset normal condition contact overheating determination threshold is used for the corresponding outgoing branch contact position under this time period. For each sampling time marker indexed by outgoing branch number in the comprehensive data group, based on the operating condition judgment result of the corresponding time period, the overheating judgment threshold of the contact after activation is attached one by one to obtain a comprehensive data group with operating condition mark and corresponding overheating judgment threshold, which serves as the judgment basis for extracting contact overheating records.

[0042] In one embodiment, the physical meaning of the solar heating limit refers to the characteristic lower boundary of the difference between the heating temperature data and the light-received temperature data of the outer casing when the degree of heating of the casing by solar radiation exceeds a certain critical level. When the light-received temperature data of the outer casing rises sharply until it approaches or exceeds the heating temperature data of the wiring contacts themselves, the internal and external temperature difference value will not only fall below the lower limit of the internal and external temperature difference under normal operating conditions, but will further fall to a lower lower level. This lower lower level is the solar heating limit.

[0043] It should be noted that the calibration process of the solar heating limit relies on historical temperature data collection and recording.

[0044] Preferably, temperature data of the ring mains enclosure during historical periods of strong summer sunlight, with all outgoing branches under light load, are retrieved. The difference between the heat generation temperature and the light-received temperature of the outer shell at each point within this period is statistically analyzed using an equal-width histogram with a bin size of 1 degree Celsius to obtain the internal and external temperature difference distribution during this period. The internal and external temperature difference distribution data are then sorted in ascending order, and the 5th quantile is calculated using the following formula: , where X is the sorted temperature difference array (index starts from 1), k=(n-1)×0.05+1, and n is the number of data points.

[0045] For example, the lower limit of the internal and external temperature difference under normal operating conditions is 5 degrees Celsius, and the solar heating limit is 0 degrees Celsius or even -2 degrees Celsius. This means that when the internal and external temperature difference drops to 0 degrees Celsius or even becomes negative, it indicates that the solar heating component of the outer shell has exceeded the heating component of the wiring contacts themselves, and the characteristics of strong solar radiation interference are significant during this period.

[0046] Specifically, each exceeding-limit segment in the internal and external temperature difference sequence is retrieved from the comprehensive data set, and the internal and external temperature difference value of each point in the segment is extracted and compared with the solar heating limit. The sampling point in the segment with an internal and external temperature difference value lower than the solar heating limit is marked.

[0047] It is understandable that the setting logic for the overheating judgment threshold for contacts under sunlight conditions differs from that for contacts under normal conditions. The overheating judgment threshold for contacts under normal conditions is set based on the allowable temperature rise boundary of the wiring contacts themselves, for example, 70 degrees Celsius, reflecting the upper limit of the heating temperature data of the copper busbar connection surface of the wiring contacts under no sunlight radiation interference. The overheating judgment threshold for contacts under sunlight conditions is based on the overheating judgment threshold for contacts under normal conditions, plus the temperature rise margin caused by sunlight radiation penetrating into the box through the outer shell, for example, 85 degrees Celsius, reflecting the upper limit of the heating temperature data of the copper busbar connection surface of the wiring contacts under strong sunlight radiation interference. The two sets of thresholds correspond to the judgment criteria for whether the wiring contacts have actual contact abnormalities under different operating conditions.

[0048] In one possible implementation, for any segments in the internal and external temperature difference sequence that exceed the solar heating limit, the start and end timestamps of these segments are extracted as solar operating conditions periods; the remaining periods are classified as normal operating conditions periods. Further, each sampling timestamp indexed by outgoing branch number in the integrated data set is traversed. If the sampling timestamp falls within the solar operating conditions period, the solar operating conditions contact overheating threshold is applied to that record; if the sampling timestamp falls within the normal operating conditions period, the normal operating conditions contact overheating threshold is applied to that record. Each completed record is accompanied by an operating condition marker and a corresponding contact overheating threshold, resulting in an integrated data set with the operating condition marker and corresponding overheating threshold, which serves as the criterion for subsequent contact overheating record extraction.

[0049] Step S105: Based on the contact temperature data in the comprehensive data group after the contact overheating judgment threshold is activated, extract the contact overheating record, and mark the suspected overheating of light-load branch contacts and suspected overheating of heavy-load branch contacts according to the rated current ratio of each branch, in combination with the current data of each outgoing branch, to form a classification anomaly label set.

[0050] By retrieving the heating temperature data from the comprehensive data set containing operating condition markers and corresponding overheating judgment thresholds, for each sampling time point of each outgoing branch, the overheating judgment threshold of the contact attached to that record is used as the judgment benchmark. If the heating temperature data is higher than the attached contact overheating judgment threshold, the sampling point under that sampling time point is recorded in the contact overheating record. The contact overheating record includes an overheating time point, the outgoing branch number, and the contact position number, forming a time-stamped list of overheating events. Based on the time-stamped list of overheating events, the load current amplitude of each outgoing branch in the comprehensive data set under the overheating time point is obtained. The ratio of the load current amplitude to the preset rated current of the outgoing branch is used as the load ratio, and a preset load ratio boundary value is obtained. If the load ratio is lower than the load ratio boundary value, the outgoing branch to which the overheating event belongs is determined to be a light-load branch; otherwise, it is determined to be a heavy-load branch. For each overheating event in the overheating event list, based on the load status of the outgoing branch to which it belongs, overheating events belonging to lightly loaded outgoing branches are marked as suspected overheating of contacts in lightly loaded branches, and overheating events belonging to heavily loaded outgoing branches are marked as suspected overheating of contacts in heavily loaded branches. Each suspected event entry is summarized according to the outgoing branch number to form a set of classified anomaly markers.

[0051] In one implementation, the extraction of contact overheating records is based on the contact overheating judgment threshold attached to each record in the previous step. Records are retrieved sequentially from the integrated data set according to the outgoing branch number and sampling time stamp. For each record, the heating temperature data in that record is compared with the attached contact overheating judgment threshold. If the heating temperature data is higher than the attached contact overheating judgment threshold, the sampling point corresponding to that record is determined to be a contact overheating point.

[0052] Specifically, the contact overheating points are summarized according to their respective outgoing branch numbers and contact position numbers. For adjacent consecutive overheating points at the same contact position within the same outgoing branch, the start and end timestamps of the consecutive overheating points are used as timestamps for each contact overheating record. Each contact overheating record includes the overheating start and end timestamps, the corresponding outgoing branch number, the contact position number, and the corresponding operating condition flag. The contact overheating records are arranged in chronological order according to their timestamps to form a time-stamped list of overheating events.

[0053] It should be noted that the load ratio is defined as the ratio between the average load current amplitude of the outgoing branch during the start and end period of an overheating event and the preset rated current of that outgoing branch. The specific calculation process is as follows: first, input the point-by-point load current amplitude I. t Where t is a time point, and then the average value I over the time period is calculated. avg =(sumI t ) / n, where n is the number of time points, and then the load ratio R = I avg / I rated , where I rated This refers to the rated current of the branch circuit. The rated current of the branch circuit is pre-entered on the nameplate of the distribution cabinet or in the operation and maintenance file, and is individually marked for each outgoing branch circuit.

[0054] Specifically, the point-by-point load current amplitude within the start and end timeframes corresponding to the overheating event is retrieved from the comprehensive data set. The average value of these point-by-point load current amplitudes over a given period is then calculated. This average value is divided by the rated current of the outgoing branch to obtain the load ratio corresponding to the overheating event. This load ratio is expressed as a percentage, reflecting the load occupancy of the outgoing branch relative to its rated capacity during the duration of the overheating event. Furthermore, the load ratio threshold is calibrated based on the historical operating data of the outgoing branch.

[0055] Preferably, the historical load current amplitude of each outgoing branch of the ring main unit is statistically analyzed according to its distribution, and the median of the load ratio distribution is taken as the load ratio boundary value.

[0056] For example, the load ratio threshold value is 30%, which means that when the load ratio of a certain outgoing branch is less than 30% during the duration of the overheating event, the outgoing branch is considered to be in a light load state, and vice versa.

[0057] In one possible implementation, each contact overheating record in the overheating event list is traversed, and the load ratio corresponding to the record is retrieved and compared with the load ratio threshold value. If the load ratio corresponding to the record is lower than the load ratio threshold value, the outgoing branch to which the contact overheating record belongs is marked as a light-load branch; otherwise, it is marked as a heavy-load branch.

[0058] It is understandable that the suspected overheating of the light-load branch contacts and the suspected overheating of the heavy-load branch contacts reflect two very different causes of overheating. The suspected overheating of the light-load branch contacts refers to a situation where the contact temperature exceeds the judgment threshold despite a low load ratio. In this case, the Joule heat generated by the load current through the contact itself is limited, and the copper busbar connection surface should maintain a low temperature. However, the temperature rises abnormally, indicating that there is an external heat source accumulating near the contact of this branch or that the heat dissipation channel is blocked. The suspected overheating of the heavy-load branch contacts refers to a situation where the load ratio is high and the contact temperature exceeds the judgment threshold. Under heavy load, a large load current passes through the copper busbar connection surface, resulting in higher Joule heat. The temperature rise is closely related to whether the contact resistance increases. The suspected overheating points of the light-load branch contacts and the suspected overheating points of the heavy-load branch contacts are summarized into entries according to the outgoing branch number, contact position number, start and end time markers and corresponding load ratios to form the classification anomaly mark set. Each entry in the classification anomaly mark set carries both the branch load status label and the contact overheating attribute.

[0059] Retrieve contact temperature data and overheating judgment threshold from the integrated data group, extract contact overheating records that exceed the overheating judgment threshold, obtain a list of overheating events with time tags, retrieve current data of each outgoing branch, classify light load and heavy load states according to the rated current of the branch, and mark the overheating events according to the branch load state as light load branch contact overheating suspected points and heavy load branch contact overheating suspected points, forming a classification anomaly label set.

[0060] By retrieving the heating temperature data connected to the outgoing branch number in the comprehensive data group and the overheating judgment threshold of the contact after activation, for the heating temperature data under each sampling time mark, the overheating judgment threshold of the contact is used as the judgment benchmark. If the heating temperature data is higher than the overheating judgment threshold of the contact, the sampling time mark is marked as a contact overheating point. Adjacent and consecutive contact overheating points under the same contact position in the same outgoing branch are merged into the same contact overheating record. The contact overheating record is accompanied by the overheating start and end time mark, the outgoing branch number and the contact position number. After being arranged, an overheating event list with time tags is formed. Based on the overheating start and end time periods corresponding to each entry in the overheating event list, the load current amplitude of each outgoing branch within that time period is retrieved from the comprehensive data group. The ratio of the time-average load current amplitude to the preset rated current of the corresponding outgoing branch is taken as the load ratio. If the load ratio is lower than the pre-defined load ratio threshold, the outgoing branch to which the overheating event belongs is marked as a light-load branch; otherwise, it is marked as a heavy-load branch. For each entry in the overheating event list, based on the load status of the outgoing branch to which it belongs, entries belonging to light-load branches are marked as suspected light-load branch contact overheating points, and entries belonging to heavy-load branches are marked as suspected heavy-load branch contact overheating points. The suspected point entries are summarized according to the outgoing branch number to form a classification anomaly label set.

[0061] In one implementation, the point-by-point extraction process for contact overheating points is as follows: Based on the comparison between the heating temperature data at each sampling time point and the contact overheating judgment threshold, if the heating temperature data is higher than the threshold, the sampling point at that sampling time point is marked as a contact overheating point, and the corresponding outgoing branch number and contact position number are noted. Subsequently, the marked contact overheating points are input into a classification anomaly marker set, and the anomaly markers are associated through a time series matching algorithm to output an integrated anomaly event sequence for subsequent fault diagnosis.

[0062] It should be noted that a contact overheating point under a single time scale may be caused by sampling jumps or instantaneous current surges, and does not constitute a complete overheating event.

[0063] Specifically, for two adjacent overheating points at the same contact position in the same outgoing branch, their time interval is compared with the pre-calibrated overheating event merging interval, which is matched with the sampling period.

[0064] Preferably, the sampling period is 2 to 3 times the sampling period. If the time interval between two contact overheating points does not exceed the overheating event merging interval, they are considered as consecutive sampling points of the same overheating event. Consecutive contact overheating points are merged pair by pair along the time interval to obtain a contact overheating record consisting of the starting contact overheating point to the ending contact overheating point. The time interval of the starting contact overheating point in the contact overheating record is used as the overheating start time interval, and the time interval of the ending contact overheating point in the contact overheating record is used as the overheating end time interval. Each contact overheating record includes an overheating start time interval, an overheating end time interval, the branch number to which it belongs, the contact position number, and the corresponding operating condition mark. All contact overheating records are arranged in chronological order according to the overheating start time interval to obtain a list of overheating events with time tags. Furthermore, for each contact overheating record in the overheating event list, the load current amplitude of the corresponding outgoing branch in the integrated data set is retrieved at each sampling time point between the overheating start time point and the overheating end time point of that record. The arithmetic mean of the load current amplitude is then used to calculate the time-period average. The time-period average reflects the overall load level of the outgoing branch during the duration of the overheating event. The integrated data set is derived from the real-time monitoring database and includes the load current amplitude, temperature data, and time stamp information of each outgoing branch.

[0065] Understandably, a load ratio threshold is introduced to assess the risk of outgoing branches from ring main units (RMUs) in overheating event handling. A RMU refers to an enclosed enclosure used for power distribution in a power system. The load ratio threshold is calibrated based on the ratio of historical load current amplitude to the branch's rated current. The specific process involves inputting the current amplitude data I for each branch over the past 12 months and the corresponding branch rated current I0. rated Calculate the point-to-point load ratio R = I / I rated Frequency statistics are performed on all R values, and the median of the distribution is taken as the load ratio cutoff value.

[0066] For example, if the median of the historical load ratio distribution of a certain outgoing branch is 0.35, then the load ratio cutoff value for that branch is 0.35. This cutoff value is dimensionless and is used to determine whether the load state of the outgoing branch is light during the duration of the overheating event.

[0067] In one possible implementation, for each contact overheating record in the overheating event list, the load ratio of that contact overheating record is obtained by dividing the average value of the corresponding time period by the rated current of the outgoing branch. The load ratio is compared with a load ratio threshold. If the load ratio is lower than the threshold, the outgoing branch to which the contact overheating record belongs is marked as a light-load branch, and the identifier attribute of that contact overheating record is set to "light-load branch contact overheating suspected point." If the load ratio is not lower than the threshold, the outgoing branch to which the contact overheating record belongs is marked as a heavy-load branch, and the identifier attribute of that contact overheating record is set to "heavy-load branch contact overheating suspected point." Each contact overheating record with an identifier attribute is summarized into entries according to the outgoing branch number, contact position number, overheating start and end time, load ratio, and light / heavy load attribute. These entries are then archived centrally by outgoing branch number, forming the classification anomaly marker set.

[0068] Step S106: Input the integrated data set and the classification anomaly label set into the support vector machine algorithm. When the classification output shows that the lightly loaded branch and the contact is overheated, the conclusion is that the air duct is blocked and the local high temperature is caused. When the classification output shows that the heavy-loaded branch and the contact is overheated, the conclusion is that the contact is poor. Based on the conclusion, generate the primary and secondary fusion ring network box operation status report to form the final contact anomaly identification conclusion.

[0069] By retrieving the heating temperature data, shell light-receiving temperature data, internal and external temperature difference value, load current amplitude, load ratio, and light / heavy load attributes corresponding to each suspicious item from the comprehensive data set and the classification anomaly marker set, and concatenating them one by one into a multi-dimensional feature vector according to the suspicious item, a feature vector set corresponding to each suspicious item is obtained. This feature vector set serves as the input sample set for the support vector machine algorithm. Based on the input sample set, a pre-trained support vector machine classifier is invoked. The support vector machine classifier takes the multi-dimensional feature vector as input and the anomaly type category as output, and outputs the anomaly type category corresponding to each suspicious item on the input sample set. The anomaly type category is one of the categories of local high temperature caused by air duct obstruction or poor contact of wiring terminals. For the output results of the anomaly type category, the outgoing branch number and overheating start and end time stamp of the suspicious item are used as association fields to attach the anomaly type category to each item, and a primary and secondary fusion ring network box operation status report containing the anomaly type, the outgoing branch, and the time of occurrence is summarized to obtain the final contact anomaly identification conclusion.

[0070] In one implementation, the process of constructing the multidimensional feature vector is as follows: data fields are retrieved one by one from the comprehensive data group and the classification anomaly mark set according to the suspicious item number, including the average value of the heating temperature data during the overheating period, the average value of the shell light-receiving temperature data during the period, the average value of the internal and external temperature difference value during the period, the average value of the load current amplitude during the period, the load ratio, the light and heavy load attributes, and the operating condition mark to which the overheating period belongs.

[0071] Specifically, the above data fields are arranged in a fixed order into a one-dimensional array, forming a multi-dimensional feature vector that corresponds one-to-one with each suspicious item.

[0072] For example, the multidimensional feature vector contains seven components: the average heating temperature over a given period, the average temperature of the outer casing exposed to sunlight over a given period, the average temperature difference between the inside and outside over a given period, the average load current over a given period, the load ratio, binary encoding of light and heavy load attributes, and binary encoding of operating condition labels. The light and heavy load attributes are represented by 0 for light load branches and 1 for heavy load branches, and the operating condition labels are represented by 0 for normal operating conditions and 1 for sunshine operating conditions. The numerical components in the multidimensional feature vector are normalized to zero mean according to their respective dimensions to obtain a regularized multidimensional feature vector. The regularized multidimensional feature vectors corresponding to all suspicious entries are then summarized to form the input sample set for the support vector machine algorithm.

[0073] Understandably, this implementation uses the Support Vector Machine (SVM) algorithm to find the hyperplane w×x+b=0 that maximizes the classification margin in the feature space, separating samples of different classes to both sides of the hyperplane, where w is the weight vector and b is the bias. For the non-linearly separable case, a radial basis function K(x) is introduced. i ,x j )=exp(-γ||x i -x j ||²) maps the original feature space to a high-dimensional space. The kernel width parameter γ and the regularization parameter C are obtained by combining grid search with 5-fold cross-validation. The search range of γ is [0.1, 10], and the search range of C is [1, 100]. The optimal parameter combination is selected based on the classification accuracy on the validation subset to obtain the pre-trained support vector machine classifier.

[0074] It should be noted that the training samples for the support vector machine classifier are derived from historical operation and maintenance records.

[0075] Specifically, the operation records of the ring main unit over the past one to three years are retrieved. Overheating events confirmed by on-site inspection and repair as localized high temperatures caused by duct obstruction are selected as the first type of sample, and overheating events confirmed by on-site inspection and repair as poor contact of wiring terminals are selected as the second type of sample. The multidimensional feature vector is derived from the real-time monitoring data of the ring main unit and arranged in this order. For each event in the first and second types of samples, data fields are extracted in the same field order as the input sample set, namely, the average value of heating temperature over time, the average value of shell temperature under light over time, the average value of internal and external temperature difference over time, the average value of load current over time, the load ratio, the binary encoding of light and heavy load attributes, and the binary encoding of operating condition label, totaling seven components, to form a training sample set with category labels. The category label is 0 to represent the category of localized high temperatures caused by duct obstruction and 1 to represent the category of poor contact of wiring terminals.

[0076] In one possible implementation, the training sample set with category labels is divided into a training subset and a validation subset in a 7:3 ratio. A support vector machine (SVM) algorithm is called on the training subset to optimize parameters. A grid search method combined with 5-fold cross-validation is used to search for the radial basis function kernel function with a kernel width parameter gamma ranging from 0.1 to 10 and a regularization parameter C ranging from 1 to 100. The optimal parameter combination is selected based on the classification accuracy on the validation subset, resulting in a pre-trained SVM classifier. Further, the input sample set is fed into the pre-trained SVM classifier line by line. The SVM classifier takes the normalized multidimensional feature vector as input and outputs the anomaly type category label corresponding to each suspicious entry line by line. Subsequently, in the subsequent anomaly diagnosis process, this category label, combined with the light / heavy load attribute, is used to determine the specific anomaly type. For example, when the light / heavy load attribute of the suspected item is light and the category label output by the classifier is 0, the anomaly type is determined to be localized high temperature caused by duct obstruction; when the light / heavy load attribute of the suspected item is heavy and the category label output by the classifier is 1, the anomaly type is determined to be poor contact of the wiring terminal. Similarly, when the light / heavy load attribute of the suspected item is light and the category label output by the classifier is 0, the anomaly type is determined to be localized high temperature caused by duct obstruction; when the light / heavy load attribute of the suspected item is heavy and the category label output by the classifier is 1, the anomaly type is determined to be poor contact of the wiring terminal.

[0077] Specifically, the category of localized high temperature caused by air duct obstruction reflects that the air vent in the cabinet near the wiring contact is blocked by a partition or cable, causing hot air to stagnate and the local temperature to rise by more than 10°C compared to the normal temperature of similar locations in the surrounding area; the category of poor contact of the wiring terminal reflects that the contact resistance between the wiring contact and the copper busbar connection surface increases, for example, from the normal 0.01 milliohms to 0.1 milliohms, and the Joule heating rises abnormally when the load current passes through.

[0078] It is understood that the "primary fusion" in the aforementioned integrated primary and secondary ring main unit operation status report refers to the fusion of the wiring contact position information, outgoing branch rated current, numbering, and internal air duct layout information of the primary equipment side of the ring main unit. "Secondary fusion" refers to the fusion of secondary monitoring data collected by the contact temperature measurement unit, the outer casing temperature measurement unit, and the branch current acquisition unit. Both are combined in terms of anomaly type category to form a fusion report covering both the primary equipment operation status and secondary monitoring anomalies.

[0079] Specifically, for each suspected item, the anomaly type category is associated with the outgoing branch number, contact position number, overheating start time, and overheating end time as related fields. The anomaly type category is then linked to the same record. The record fields simultaneously include the anomaly type, outgoing branch, contact position, occurrence time, and corresponding load ratio. All records are summarized by outgoing branch number to form a primary and secondary integrated ring network box operation status report. Each anomaly item in this report represents the final contact anomaly identification conclusion, which supports maintenance personnel in performing targeted duct cleaning or contact surface maintenance.

[0080] Extract the branch load status and contact overheating suspicions from the comprehensive data set and the classification anomaly mark set. Identify the light-load branch contact overheating suspicions as local high temperature caused by air duct obstruction. Determine the heavy-load branch contact overheating suspicions with poor terminal contact. Generate a primary and secondary fusion ring network box operation status report containing the anomaly type, the branch it belongs to, and the time of occurrence. The final contact anomaly identification conclusion is obtained.

[0081] By retrieving the branch load status and contact overheating identification attributes of each suspected item from the comprehensive data set and the classified anomaly mark set, the branch number, contact position number, overheating start and end time stamps, and load ratio are obtained, resulting in a suspected item association record linked to the suspected item. Based on this association record, for items with a light-load branch contact overheating suspected issue, the anomaly type field is linked as "localized high temperature caused by duct obstruction"; for items with a heavy-load branch contact overheating suspected issue, the anomaly type field is linked as "poor terminal contact," resulting in an association record with an anomaly type field. For these association records with an anomaly type field, using the anomaly type, branch number, and overheating start and end time stamps as core fields, each item is summarized by branch number to form a primary and secondary fusion ring network box operation status report containing the anomaly type, branch number, and occurrence time, thus obtaining the final contact anomaly identification conclusion.

[0082] In one implementation, the field retrieval process for the suspicious item association record is as follows: each suspicious item is retrieved one by one from the classified anomaly marker set according to the suspicious item number; for each suspicious item, its corresponding outgoing branch number, contact position number, overheat start and end time stamp, load ratio, and branch load status fields are read; and the average value of the heating temperature data and the average value of the casing light-receiving temperature data during the same period are retrieved from the comprehensive data group according to the corresponding outgoing branch number and the overheat start and end time stamp, and linked as the same suspicious item association record.

[0083] Specifically, each suspicious item is associated with a record stored in a structured field format. The field order is fixed as follows: outgoing branch number, contact position number, overheat start and end time stamp, load ratio, branch load status, average heating temperature over time period, and average temperature of the outer casing exposed to light over time period. This constitutes a complete associated record for each suspicious item in the classification anomaly marker set.

[0084] It should be noted that the abnormal type of local high temperature caused by the duct obstruction is determined based on the branch load status.

[0085] Specifically, for each suspicious entry associated with a record, if the branch load status field of that record indicates a suspected overheating of the light-load branch contact, then the Joule heat level generated by the connection contact itself due to the load current is limited, and the copper busbar connection surface should not show a high temperature reading. However, if the average value of the heating temperature over a certain period exceeds the corresponding contact overheating judgment threshold, it reflects that the ventilation opening in the cabinet near the connection contact is blocked by the cabinet partition or cable, and hot air stagnates there, causing a local temperature rise. Therefore, the abnormality type field of that record is linked to local high temperature caused by air duct obstruction.

[0086] Specifically, the determination of the abnormal type of poor terminal contact is also based on the branch load status. For each suspected entry associated with a record, if the branch load status field of that record indicates a suspected overheating of the contact in a heavy-load branch, then the average value of the heating temperature data of the copper busbar connection surface exceeds the corresponding contact overheating judgment threshold when the contact in that record is carrying a large load current. This reflects that the contact resistance between the contact and the copper busbar connection surface has increased, and the Joule heat increases significantly with the increase in contact resistance. Therefore, the abnormal type field of that record is associated with poor terminal contact.

[0087] In one possible implementation, the records associated with the suspicious entries are traversed, and the abnormality type field is attached to each record according to the above judgment logic to obtain the associated records with the abnormality type field. The abnormality type field of each record is one of two categories: local high temperature caused by air duct obstruction and poor contact of wiring terminals.

[0088] It is understood that in the primary and secondary integrated ring main unit operation status report, primary integration refers to the integration of the wiring contact positions, outgoing branch numbers, and internal air duct layout information of the primary equipment side of the ring main unit, while secondary integration refers to the integration of secondary monitoring data collected by the contact temperature measurement unit, the outer shell temperature measurement unit, and the branch current acquisition unit. Both are aggregated into a single report in the anomaly type field. Furthermore, for each associated record with an anomaly type field, the anomaly type, the corresponding outgoing branch number, and the overheating start and end timestamps are used as core fields, while the contact position number, load ratio, and average heating temperature over a given period are used as additional fields. These are then summarized into report entries according to the outgoing branch number to obtain the primary and secondary integrated ring main unit operation status report. Each entry in this report represents the final contact anomaly identification conclusion.

[0089] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for analyzing the operating status of a primary and secondary integrated ring network box, characterized in that, The method includes: The contact temperature measurement unit and the outer shell temperature measurement unit of the ring network box collect contact temperature data and outer shell light-received temperature data to form an initial temperature dataset. The contact temperature data reflects the heating state of the wiring contacts, and the outer shell light-received temperature data reflects the solar heating state of the box. Based on the initial temperature dataset, the internal and external temperature difference is calculated by subtracting the light-received temperature of the outer shell from the contact temperature. The temperature difference exceeding the limit segment is extracted to form an internal and external temperature difference sequence, which serves as the basis for the criteria for identifying solar interference. The current data of each outgoing branch is extracted by the ring network box branch current acquisition unit and combined with the contact temperature data, the light-receiving temperature data of the outer shell, and the internal and external temperature difference sequence to form a comprehensive data set. The internal and external temperature difference sequence in the comprehensive data group is analyzed using a preset temperature difference limit model. When the temperature difference exceeds the solar heating limit, the solar heating condition contact overheating judgment threshold is activated. When the temperature difference does not exceed the limit, the conventional operating condition contact overheating judgment threshold is used. The contact temperature data in the comprehensive data group is evaluated based on the contact overheating judgment threshold after activation. The contact overheating record is extracted, and the suspected overheating of the light-load branch contact and the suspected overheating of the heavy-load branch contact are marked according to the rated current ratio of the branch, based on the current data of each outgoing branch, to form a classification anomaly label set. The integrated data set and the classification anomaly label set are input into the support vector machine algorithm. When the classification output shows a lightly loaded branch and the contact is overheated, the conclusion is that the air duct is blocked and the local high temperature is caused. When the classification output shows a heavily loaded branch and the contact is overheated, the conclusion is that the contact is poor. Based on the conclusion, a primary and secondary fusion ring network box operation status report is generated to form the final contact anomaly identification conclusion.

2. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 1, characterized in that, The ring network box contact temperature measuring unit and the outer shell temperature measuring unit collect contact temperature data and outer shell sunlight-exposed temperature data to form an initial temperature dataset. The contact temperature data reflects the heating state of the wiring contacts, and the outer shell sunlight-exposed temperature data reflects the solar heating state of the box. This includes: The contact temperature measuring unit arranged at each outgoing wire contact inside the ring network box uses an attached temperature measuring probe to contact the copper busbar connection surface to collect the heating temperature data of the wire contact. The outer shell temperature measuring unit arranged on the light-receiving surface of the top cover and side wall of the ring network box collects the light-receiving temperature data of the outer shell after the box is heated by sunlight, thus obtaining the original temperature sampling data with time stamp. The initial temperature dataset is formed by aligning and merging the heating temperature data and the outer shell light-receiving temperature data according to the same time scale, and then removing sampling segments with discontinuous time scales.

3. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 1, characterized in that, The step of calculating the internal and external temperature difference based on the initial temperature dataset by subtracting the light-received temperature of the outer shell from the contact temperature, extracting temperature difference exceeding the limit segment, and forming an internal and external temperature difference sequence as the basis for identifying solar interference includes: For each outgoing branch contact position under the same sampling time scale, the internal and external temperature difference value is obtained by subtracting the corresponding shell light-receiving temperature data from the heating temperature data, and then arranged in time scale order to obtain point-by-point internal and external temperature difference time series data. The internal and external temperature difference value at each time point is compared with the preset internal and external temperature difference lower limit value. Sampling points that are lower than the internal and external temperature difference lower limit value are determined to be temperature difference exceeding the limit point. The temperature difference exceeding the limit points with adjacent time intervals lower than the preset time interval threshold are merged into the same exceeding limit segment. After removing the discrete exceeding limit points, the exceeding limit segments are arranged in the order of start and end time points to obtain the internal and external temperature difference sequence.

4. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 1, characterized in that, The current acquisition unit of the ring network box branch extracts the current data of each outgoing branch, and combines it with the contact temperature data, the light-receiving temperature data of the outer shell, and the internal and external temperature difference sequence to form a comprehensive data set, including: The branch current acquisition unit is arranged at the outgoing end of each outgoing branch of the ring network box. The load current of the outgoing branch is sampled by the current transformer. The current data of each outgoing branch is read according to the sampling period consistent with the initial temperature dataset acquisition period. The current data of each outgoing branch is accompanied by the sampling time stamp and the outgoing branch number. Using the sampling time stamp and the outgoing branch number as index keys, the current data of each outgoing branch is aligned and merged with the heating temperature data, the light-receiving temperature data of the outer casing, and the internal and external temperature difference sequence to obtain the comprehensive data group.

5. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 1, characterized in that, The method employs a preset temperature difference limit model to analyze the internal and external temperature difference sequences in the comprehensive data set. When the temperature difference exceeds the solar heating limit, the solar heating condition contact overheating judgment threshold is activated; otherwise, the conventional operating condition contact overheating judgment threshold is used. The internal and external temperature difference sequence is retrieved from the comprehensive data set to obtain the preset solar heating limit. The solar heating limit is a limit determined based on the lower quantile of the difference between the heating temperature of the wiring contacts and the light-received temperature of the outer casing under historical no-sunlight conditions, and the value is lower than the lower limit of the internal and external temperature difference. The internal and external temperature difference values ​​in each exceeding segment of the internal and external temperature difference sequence are compared with the solar heating limit segment by segment. If the internal and external temperature difference value within a certain over-limit segment is lower than the solar heating limit, it is determined that the time period to which the over-limit segment belongs is under solar heating conditions, and the determination criterion for the corresponding outgoing branch contact position is switched to the preset solar heating condition contact overheating determination threshold. For cases where the internal and external temperature difference value in the internal and external temperature difference sequence is lower than the solar heating limit without any exceeding segment, the corresponding outgoing branch contact position uses the preset overheating judgment threshold for conventional operating conditions. For each sampling time marker indexed by outgoing branch number in the comprehensive data group, based on the operating condition judgment result of the corresponding time period, the overheating judgment threshold of the contact after activation is attached one by one to obtain a comprehensive data group with operating condition mark and corresponding overheating judgment threshold.

6. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 1, characterized in that, The method involves evaluating the contact temperature data in the comprehensive data set based on the activated contact overheating judgment threshold, extracting contact overheating records, and combining the current data of each outgoing branch with the data to mark suspected overheating of light-load branch contacts and suspected overheating of heavy-load branch contacts according to the rated current ratio of the branch, forming a classification anomaly label set, including: For each sampling time point of each outgoing branch, the overheating judgment threshold of the connected contact is used as the judgment benchmark. Sampling points with heating temperature data higher than the overheating judgment threshold of the connected contact are recorded in the contact overheating record. The contact overheating record is accompanied by an overheating time point and the outgoing branch number and contact position number. The load ratio is defined as the ratio of the load current amplitude to the rated current of the branch. Outgoing branches with a load ratio lower than the preset load ratio threshold are determined to be light-load branches, and those with a load ratio higher than the threshold are determined to be heavy-load branches. According to the load status of the outgoing branch, the suspected overheating of the contacts in the lightly loaded branch and the suspected overheating of the contacts in the heavy-load branch are marked respectively. The suspected items are summarized according to the outgoing branch number to form the classified anomaly mark set.

7. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 6, characterized in that, The contact overheating record also includes: Adjacent and consecutive contact overheating points under the same contact position in the same outgoing branch are merged into the same contact overheating record. The contact overheating record is accompanied by the overheating start and end time stamp, the outgoing branch number and the contact position number. After being arranged, it forms a list of overheating events with time tags. For each overheating start and end time period corresponding to each entry in the overheating event list, the load current amplitude of each outgoing branch within that time period is retrieved from the comprehensive data group. The ratio of the average value of the load current amplitude over the time period to the rated current of the corresponding branch is taken as the load ratio. The light-load branch and the heavy-load branch are marked based on the comparison result between the load ratio and the load ratio boundary value.

8. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 1, characterized in that, The integrated data set and the classification anomaly label set are input into a support vector machine algorithm. When the classification output shows a lightly loaded branch and overheated contacts, the algorithm concludes that duct obstruction is causing localized high temperatures. When the classification output shows a heavily loaded branch and overheated contacts, the algorithm concludes that there is poor contact. Based on these conclusions, a primary and secondary fusion ring network box operation status report is generated, forming the final contact anomaly identification conclusion, including: The data on heating temperature, shell light temperature, internal and external temperature difference, load current amplitude, load ratio and light and heavy load attributes corresponding to each suspicious item are retrieved from the comprehensive data group and the classification anomaly mark set. The suspicious items are then concatenated into multi-dimensional feature vectors to obtain a feature vector set that corresponds one-to-one with the suspicious items. The feature vector set is used as the input sample set of the support vector machine algorithm. The pre-trained support vector machine classifier is invoked. The support vector machine classifier takes the multi-dimensional feature vector as input and the anomaly type category as output. It outputs the anomaly type category corresponding to each suspicious item on the input sample set. The anomaly type category is one of the categories of local high temperature caused by air duct obstruction or poor contact of wiring terminals. Using the outgoing branch number and overheat start and end time stamp of the suspicious item as associated fields, the anomaly type category is attached to each item, and a report on the operation status of the primary and secondary fusion ring network box containing the anomaly type, the outgoing branch, and the time of occurrence is compiled to obtain the final contact anomaly identification conclusion.

9. The method for analyzing the operating status of a primary and secondary integrated ring network box according to claim 8, characterized in that, The generation of the primary and secondary integrated ring network box operation status report also includes: From the comprehensive data set and the classified anomaly mark set, retrieve the branch load status and contact overheating suspicion attributes one by one according to the suspicious item, obtain the branch number, contact position number, overheating start and end time mark and load ratio, and obtain the suspicious item association record linked according to the suspicious item; For entries with a branch load status of light load and suspected overheating of the branch contacts, the abnormality type category is linked as "local high temperature caused by air duct obstruction"; for entries with a branch load status of heavy load and suspected overheating of the branch contacts, the abnormality type field is linked as "poor contact of the wiring terminal", resulting in associated records with the abnormality type field. Using the anomaly type, the outgoing branch number, and the overheating start and end timestamps as core fields, the primary and secondary integrated ring network box operation status report is generated by summarizing the outgoing branch numbers.

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