Temperature rise test method for low-voltage switchgear assembly and related equipment

Abstract

The invention provides a temperature rise test method of a low-voltage switchgear assembly and related equipment, and relates to the technical field of throttling devices. By receiving equipment scale data and outputting related data, a basis is provided for subsequent processing. And then, a series of sub-steps are executed circularly, so that the intelligentization of temperature monitoring is realized. When the temperature exceeds the threshold value, the system can improve the data acquisition frequency, and it is ensured that the temperature change at the critical moment is monitored more densely. Meanwhile, by analyzing the temperature rise trend and identifying the potential fault area, the system can find abnormal conditions in time. In addition, by comparing the current temperature rise trend with the historical temperature rise trend, the system can judge whether cooling measures need to be executed or not. And finally, through temperature stability analysis, the system can adjust a data acquisition strategy according to actual conditions. The dynamic adjustment method can effectively cope with the temperature change characteristics of different scales of equipment, and the detection efficiency and accuracy of temperature abnormity are improved.

Description

Technical Field

[0001] The present application relates to the technical field of complete throttling devices, and in particular to a temperature rise test method for a low-voltage complete switchgear and related equipment. Background Art

[0002] With the continuous development of power systems and the continuous growth of electricity demand, low-voltage switchgear plays an increasingly important role in power distribution systems. The reliability and safety of these devices are directly related to the stable operation of the entire power system. Therefore, comprehensive and accurate temperature rise test of low-voltage switchgear has become a key link to ensure its performance and safety.

[0003] The existing temperature rise test method for low-voltage complete switchgear usually uses a fixed time interval sampling method for temperature monitoring. Specifically, the tester will first set a fixed sampling period, such as collecting temperature data every 5 minutes or every 10 minutes. However, this setting is often difficult to adapt to the needs of equipment of different sizes. Large-scale equipment may require higher frequency sampling, while small-scale equipment may not need such frequent sampling. After the test starts, the system will automatically sample the temperature of each key part of the equipment (such as the main circuit, auxiliary circuit, casing, etc.) at the preset time point and record the collected data. The test usually continues until the temperature tends to stabilize, that is, when the temperature rise value measured continuously for multiple times does not change by more than 1K / h, it is considered to have reached a stable state. After the test is completed, the technician will use special data analysis software to process the data, calculate the temperature rise value of each part, draw the temperature rise curve, and identify potential abnormal points through statistical analysis methods.

[0004] However, as the structure of low-voltage complete switchgear becomes increasingly complex and its scale continues to expand, the working environment and load conditions become more variable, which puts higher requirements on the accuracy and real-time performance of temperature rise test. Under this new demand, the methods of related technologies are not enough to meet the demand. For example: when the equipment is in a rapid heating stage or the load changes suddenly, the sampling at fixed intervals may miss the key points of temperature change, resulting in the inability to accurately capture the trend of temperature change, thus affecting the timely discovery of potential faults. Secondly, during the period when the temperature of the equipment is relatively stable, too frequent sampling may cause a large amount of redundant data, which not only wastes storage resources, but also increases the complexity of data processing and reduces analysis efficiency. In addition, this method makes it difficult to perform differentiated sampling according to the temperature change characteristics of equipment of different sizes, which may cause temperature anomalies in certain key parts to be ignored or delayed. Summary of the invention

[0005] The present application provides a temperature rise test method and related equipment for low-voltage complete switchgear, which are used to cope with the temperature change characteristics of equipment of different sizes and improve the detection efficiency and accuracy of temperature anomalies.

[0006] In the first aspect, the present application provides a temperature rise test method for a low-voltage complete switchgear, comprising: receiving scale data of the low-voltage complete switchgear, and outputting relevant data; using the relevant data as a loop variable, and cyclically executing the following sub-steps: receiving temperature data, and determining whether the temperature data is greater than a temperature threshold in the relevant data; if the temperature data is greater than the temperature threshold, increasing the data acquisition frequency in the relevant data according to preset rules; receiving subsequent temperature data at the increased data acquisition frequency; outputting temperature rise trend analysis results and potential fault area identification results based on the temperature data and subsequent temperature data; receiving the historical temperature rise trend analysis results of the previous cycle; determining whether the temperature rise trend analysis results are continuous and increasing with the historical temperature rise trend analysis results; if the determination result is continuous and increasing, executing preset cooling measures; if the temperature data is not greater than the temperature threshold, executing temperature stability analysis; adjusting the data acquisition frequency and period in the relevant data according to the temperature stability analysis results to update the relevant data.

[0007] By adopting the above technical solution, by receiving equipment scale data and outputting relevant data, a basis for subsequent processing is provided. Then, by looping through a series of sub-steps, the intelligentization of temperature monitoring is realized. When the temperature exceeds the threshold, the system increases the frequency of data acquisition to ensure more intensive monitoring of temperature changes at critical moments. At the same time, by analyzing the temperature rise trend and identifying potential fault areas, the system can detect abnormal conditions in a timely manner. In addition, by comparing the current and historical temperature rise trends, the system can determine whether cooling measures need to be implemented. Finally, through temperature stability analysis, the system can adjust the data acquisition strategy according to actual conditions. This dynamic adjustment method can effectively cope with the temperature change characteristics of equipment of different scales and improve the detection efficiency and accuracy of temperature anomalies.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the preset rules are: calculate the temperature change rate and the temperature change acceleration based on the temperature data; obtain the short-term temperature trend based on the temperature change rate and the temperature change acceleration; when the short-term temperature trend shows that the temperature is rising, increase the data collection frequency; when the short-term temperature trend shows that the temperature is falling, reduce the data collection frequency; determine the adjustment range of the data collection frequency based on the absolute value of the temperature change rate.

[0009] By adopting the above technical solution, the short-term temperature trend is obtained by calculating the temperature change rate and the temperature change acceleration. This trend analysis can quickly capture the dynamic characteristics of temperature changes. When the short-term trend shows that the temperature is rising, the system will increase the frequency of data collection to more closely monitor possible abnormal conditions. On the contrary, when the trend shows that the temperature is falling, the system will appropriately reduce the collection frequency to save resources.

[0010] In combination with some embodiments of the first aspect, in some embodiments, if the temperature data is greater than the temperature threshold, after the step of increasing the data acquisition frequency in the relevant data according to preset rules, the method also includes: performing frequency domain analysis on the temperature data to obtain periodic characteristics; determining temperature peaks and temperature troughs based on the periodic characteristics; increasing the data acquisition density of the temperature peaks and reducing the data acquisition density of the temperature troughs.

[0011] By adopting the above technical solution, the periodic characteristics of temperature changes can be identified by performing frequency domain analysis on temperature data. This analysis method can reveal the inherent laws of temperature changes, rather than relying solely on time domain data. Then, based on these periodic characteristics, the system can accurately determine the peaks and troughs of temperature. This precise positioning lays the foundation for subsequent differentiated sampling. Finally, the system will increase the data acquisition density at the temperature peak and reduce the acquisition density at the trough. Not only can it capture temperature changes at critical moments, but it can also optimize resource usage while ensuring data quality and avoid unnecessary oversampling.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the steps of increasing the data collection density of temperature peaks and reducing the data collection density of temperature valleys, the method also includes: generating a time interval sequence based on the increased data collection frequency; sampling temperature data between the temperature peak and the temperature valley according to the time interval sequence to obtain additional temperature data; comparing whether the difference between the additional temperature data and the temperature data obtained by sampling at a fixed frequency exceeds a preset abnormal threshold; determining the temperature data of the sampling point that exceeds the preset abnormal threshold as an abnormal point; determining the location information of the abnormal point; identifying the temperature sensor in the adjacent area based on the location information; and sending a high-frequency sampling instruction to the temperature sensor in the adjacent area.

[0013] By adopting the above technical solution, a time interval sequence is generated according to the adjusted acquisition frequency, which provides a time reference for subsequent precise sampling. Then, additional temperature data sampling is performed between temperature peaks and troughs. This strategic sampling can capture temperature changes that may be ignored by conventional fixed-frequency sampling. Next, the system compares these additional sampled data with the fixed-frequency sampled data. If the difference exceeds the preset threshold, it will be marked as an anomaly. This method can not only detect temperature anomalies, but also accurately locate the location of the anomaly. Finally, the system identifies temperature sensors near the anomaly and sends high-frequency sampling instructions to them, realizing key monitoring of potential problem areas.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the steps of outputting temperature rise trend analysis results and potential fault area identification results based on temperature data and subsequent temperature data specifically include: calculating the temperature change rate and temperature change acceleration based on the temperature data; obtaining the short-term temperature trend based on the temperature change rate and the temperature change acceleration, and outputting the temperature rise trend analysis results; comparing the temperature data and subsequent temperature data with the temperature threshold, and identifying data points that exceed the temperature threshold; determining the potential fault area based on the location and number of data points that exceed the temperature threshold, and outputting the potential fault area identification result.

[0015] By adopting the above technical solution, the short-term temperature trend is obtained by calculating the temperature change rate and the temperature change acceleration. This dynamic analysis can capture the slight trend of temperature change in time, providing the possibility of early warning. The system then compares the temperature data with the preset threshold and identifies the data points that exceed the threshold. This method can not only detect abnormally high temperatures, but also abnormal temperature fluctuations. Finally, based on the location and number of data points that exceed the threshold, the system is able to determine potential fault areas. This comprehensive analysis not only provides the overall trend of temperature changes, but also accurately locates the specific areas where problems may occur.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the step of judging whether the temperature rise trend analysis result is continuous and increasing with the historical temperature rise trend analysis result specifically includes: extracting key parameters in the temperature rise trend analysis result, the key parameters including average temperature, maximum temperature and temperature change rate; extracting corresponding key parameters in the historical temperature rise trend analysis result; calculating the time interval between the temperature rise trend analysis result and the historical temperature rise trend analysis result; judging whether the time interval is less than a preset continuity threshold in the relevant data; if the time interval is less than the preset continuity threshold, then: comparing whether the average temperature in the temperature rise trend analysis result is higher than the average temperature in the historical temperature rise trend analysis result; comparing whether the maximum temperature in the temperature rise trend analysis result is higher than the maximum temperature in the historical temperature rise trend analysis result; comparing whether the temperature change rate in the temperature rise trend analysis result is higher than the temperature change rate in the historical temperature rise trend analysis result; if all three comparison results are yes, then judging that the temperature rise trend analysis result and the historical temperature rise trend analysis result are continuous and increasing; if the time interval is greater than or equal to the preset continuity threshold, or any of the above three comparison results is no, then judging that the temperature rise trend analysis result and the historical temperature rise trend analysis result do not meet the continuous and increasing conditions.

[0017] By adopting the above technical solution, key parameters including average temperature, maximum temperature and temperature change rate are extracted, which fully reflect the characteristics of temperature change. Then, the system calculates and determines whether the time interval between adjacent analysis results meets the continuity requirements, ensuring the temporal continuity of trend analysis. Next, the system determines whether the temperature continues to rise by comparing the key parameters in the current and historical analysis results. This multi-dimensional comparison not only considers the absolute value change of temperature, but also pays attention to the rate of change. Finally, only when all conditions are met will the system determine that the temperature rise trend is continuous and increasing, which improves the reliability of the analysis results.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the step of performing temperature stability analysis specifically includes: calculating the mean and standard deviation of temperature data; calculating the coefficient of variation of temperature data, the coefficient of variation being the standard deviation divided by the mean; determining whether the coefficient of variation is less than a preset stability threshold; calculating the first-order difference of temperature data to obtain the temperature change rate; determining whether the absolute value of the temperature change rate is less than a preset change rate threshold; calculating the second-order difference of temperature data to obtain the temperature change acceleration; determining whether the absolute value of the temperature change acceleration is less than a preset acceleration threshold; if the coefficient of variation is less than the preset stability threshold, and the absolute value of the temperature change rate is less than the preset change rate threshold, and the absolute value of the temperature change acceleration is less than the preset acceleration threshold, then the temperature is determined to be in a stable state; if any condition is not met, then the temperature is determined to be in an unstable state; based on the duration of the stable state, the degree of temperature stability is evaluated; and a temperature stability analysis result including a stable state judgment result and a stability degree evaluation is generated.

[0019] By adopting the above technical solution, the average value and standard deviation of the temperature data are calculated, and the overall fluctuation of the temperature is evaluated by the coefficient of variation. This statistical method can effectively measure the degree of discreteness of the temperature distribution. Then, the system calculates the first-order difference and the second-order difference to obtain the temperature change rate and the acceleration of change. These indicators can reflect the dynamic characteristics of temperature changes. Next, the system compares these indicators with the preset thresholds to determine whether the temperature is in a stable state from multiple dimensions. Finally, the system will also evaluate the duration of the stable state to further quantify the degree of temperature stability. This multi-angle and multi-level analysis method can accurately determine the stability of the temperature.

[0020] In the second aspect, the present application provides a temperature rise test for a low-voltage complete switchgear, which comprises: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and one or more processors call computer instructions to make the temperature rise test of the low-voltage complete switchgear execute the method described in the first aspect and any possible implementation method in the first aspect.

[0021] In a third aspect, the present application provides a computer program product comprising instructions, which, when run on a temperature rise test of a low-voltage complete switchgear, enables the temperature rise test of the low-voltage complete switchgear to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a temperature rise test of a low-voltage complete switchgear, enables the temperature rise test of the low-voltage complete switchgear to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By receiving equipment scale data and outputting relevant data, it provides a basis for subsequent processing. Then, by looping through a series of sub-steps, the intelligentization of temperature monitoring is realized. When the temperature exceeds the threshold, the system increases the frequency of data collection to ensure more intensive monitoring of temperature changes at critical moments. At the same time, by analyzing the temperature rise trend and identifying potential fault areas, the system can detect abnormal conditions in a timely manner. In addition, by comparing the current and historical temperature rise trends, the system can determine whether cooling measures need to be implemented. Finally, through temperature stability analysis, the system can adjust the data collection strategy according to actual conditions. This dynamic adjustment method can effectively cope with the temperature change characteristics of equipment of different scales and improve the detection efficiency and accuracy of temperature anomalies.

[0024] 2. By calculating the temperature change rate and the acceleration of temperature change, the short-term temperature trend is obtained. This trend analysis can quickly capture the dynamic characteristics of temperature changes. When the short-term trend shows that the temperature is rising, the system will increase the frequency of data collection to more closely monitor possible abnormal conditions. On the contrary, when the trend shows that the temperature is falling, the system will appropriately reduce the collection frequency to save resources.

[0025] 3. By performing frequency domain analysis on temperature data, the periodic characteristics of temperature changes can be identified. This analysis method can reveal the inherent laws of temperature changes, rather than relying solely on time domain data. Then, based on these periodic characteristics, the system can accurately determine the peaks and troughs of temperature. This precise positioning lays the foundation for subsequent differentiated sampling. Finally, the system will increase the data collection density at the temperature peak and reduce the collection density at the trough. Not only can it capture temperature changes at critical moments, but it can also optimize resource usage while ensuring data quality and avoid unnecessary oversampling. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a temperature rise test method for low-voltage complete switchgear in an embodiment of the present application; Figure 2 This is a flow chart of step S106 in the embodiment of the present application; Figure 3 This is a flow chart of step S108 in the embodiment of the present application; Figure 4 This is a flow chart of step S110 in the embodiment of the present application; Figure 5 This is another flow chart of the temperature rise test method for low-voltage complete switchgear in the embodiment of the present application; Figure 6 It is an exemplary hardware structure diagram of a temperature rise test system for a low-voltage complete switchgear in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] See also Figure 1 , Figure 1 It is a flow chart of a temperature rise test method for low-voltage complete switchgear in an embodiment of the present application.

[0030] S101, receiving scale data of low-voltage complete switchgear, and outputting relevant data; Scale data refers to information related to the scale of the device, such as the physical size of the device, rated current, power capacity, etc. Related data refers to the data set generated based on the scale data for subsequent processing, including parameters such as temperature thresholds and initial data collection frequency.

[0031] Before the test begins, it is necessary to first obtain basic information about the device being tested. In some embodiments, the system first receives the scale data of the low-voltage complete switchgear, which may come from the technical specifications of the device or field measurement results. Then, based on these scale data, the system generates a series of related data, such as setting the initial temperature threshold according to the device scale, determining the initial data collection frequency, etc. These related data will serve as the basic parameters for the subsequent test process.

[0032] In some specific embodiments, the equipment scale data is manually input through the human-computer interaction interface; the system processes the input scale data according to preset rules; and generates a relevant data set including initial parameters, which is not limited here.

[0033] S102, taking the relevant data as a loop variable, and looping through the following sub-steps: S103, receiving temperature data, and determining whether the temperature data is greater than a temperature threshold in the relevant data; Temperature data refers to the real-time temperature measurement values ​​obtained from the temperature sensors of low-voltage complete switchgear.

[0034] At the beginning of each cycle, the system needs to obtain the latest temperature data and make a preliminary judgment. In some embodiments, the system first receives the latest temperature data from the temperature sensor. Then, the system compares this temperature data with the temperature threshold value pre-set in the relevant data. This comparison operation is to determine whether the current temperature has exceeded the safety limit, so as to determine whether special measures need to be taken later.

[0035] S104, if the temperature data is greater than the temperature threshold, increasing the data collection frequency in the relevant data according to a preset rule; The preset rule refers to a predefined strategy or algorithm for adjusting the data collection frequency. The data collection frequency refers to the number of temperature measurements performed per unit time.

[0036] When the temperature exceeds the threshold, the system needs to take measures to monitor the temperature change more closely. In some embodiments, if the system determines that the current temperature data is greater than the preset temperature threshold, it will trigger the adjustment mechanism of the data collection frequency. The system will increase the frequency of data collection according to the pre-set rules. This adjustment is intended to obtain more temperature data points in order to more accurately analyze the temperature change trend and find potential problems in time.

[0037] In some embodiments, the preset rules are: S1041. Calculate the temperature change rate and the temperature change acceleration according to the temperature data; Among them, temperature data refers to a series of temperature values ​​measured within a certain period of time. The temperature change rate refers to the amount of temperature change per unit time, which is used to indicate how fast the temperature changes. The temperature change acceleration refers to the rate of change of the temperature change rate, which is used to indicate the change in the speed of temperature change.

[0038] In some specific embodiments, the system may use a sliding window method to select temperature data within a certain time window, calculate the temperature difference between the start and end of the window, divide it by the time interval to obtain the temperature change rate, and then perform similar calculations on continuous temperature change rates to obtain the temperature change acceleration; this is not limited here.

[0039] In some specific embodiments, the system may use the least squares method to fit the temperature data curve, and obtain the temperature change rate and the temperature change acceleration through the first-order derivative and the second-order derivative of the fitting curve, respectively; this is not limited here.

[0040] S1042, obtaining a short-term temperature trend according to the temperature change rate and the temperature change acceleration; Among them, the short-term temperature trend refers to the direction and characteristics of temperature change within a shorter time frame. The temperature change rate indicates the speed of temperature change, and the temperature change acceleration indicates the change in the temperature change speed.

[0041] In some embodiments, the system will comprehensively analyze the values ​​and signs of the temperature change rate and the temperature change acceleration to determine the temperature change trend in the short term. For example, if the temperature change rate is positive and the temperature change acceleration is also positive, it may indicate that the temperature is rising faster; if the temperature change rate is positive but the temperature change acceleration is negative, it may indicate that the temperature rise rate is slowing down.

[0042] S1043. When the short-term temperature trend shows a temperature rise, increase the frequency of data collection; S1044. When the short-term temperature trend shows a temperature drop, reduce the frequency of data collection; S1045. Determine the adjustment range of the data acquisition frequency according to the absolute value of the temperature change rate.

[0043] This step is performed when the data acquisition frequency needs to be adjusted, usually immediately after step S1043 or S1044. In some embodiments, the system will decide how to adjust the data acquisition frequency based on the calculated absolute value of the temperature change rate. The larger the absolute value of the temperature change rate, the more drastic the temperature change, and the system will increase the adjustment amplitude of the data acquisition frequency accordingly. This method can make the system respond more sensitively to temperature changes, provide more intensive data acquisition when the temperature changes drastically, and save resources when the temperature changes slowly.

[0044] It can be seen that by calculating the temperature change rate and the temperature change acceleration, the short-term temperature trend is obtained. This trend analysis can quickly capture the dynamic characteristics of temperature changes. When the short-term trend shows that the temperature is rising, the system will increase the frequency of data collection to more closely monitor possible abnormal conditions. On the contrary, when the trend shows that the temperature is falling, the system will appropriately reduce the collection frequency to save resources.

[0045] S105, receiving subsequent temperature data at the increased data collection frequency; The increased data collection frequency represents a higher temperature data collection rate after adjustment in step S104.

[0046] After adjusting the data collection frequency, the system needs to obtain new temperature data to evaluate the temperature change. In some embodiments, the system uses a new, higher data collection frequency to obtain a series of temperature data. These data reflect the temperature changes of the device in a more intensive time interval, which helps the system to more accurately analyze temperature change trends and identify potential abnormalities.

[0047] S106, outputting a temperature rise trend analysis result and a potential fault area identification result according to the temperature data and the subsequent temperature data; See also Figure 2 , Figure 2 This is a flow chart of step S106 in the embodiment of the present application; In some embodiments, step S106 specifically includes: S1061. Calculate the temperature change rate and the temperature change acceleration according to the temperature data; It should be noted that the principle and process of this step are similar to those of step S1041. The relevant principles and processes can be referred to S1041 and will not be repeated here.

[0048] S1062. Obtain the short-term temperature trend according to the temperature change rate and the temperature change acceleration, and output the temperature rise trend analysis result; It should be noted that the principle and process of this step are similar to those of step S1042. The relevant principles and processes can be referred to S1042 and will not be repeated here.

[0049] S1063, comparing the temperature data and subsequent temperature data with a temperature threshold, and identifying data points exceeding the temperature threshold; In some embodiments, the system continuously compares newly acquired temperature data with a preset temperature threshold. This process not only includes an evaluation of the current data, but also considers subsequent data collection to detect temperature anomalies in a timely manner. The system marks all data points that exceed the threshold, which may represent potential failures or abnormal conditions that require further analysis and processing.

[0050] S1064. Determine a potential fault area based on the location and number of data points exceeding the temperature threshold, and output a potential fault area identification result.

[0051] This step is performed after identifying data points that exceed the temperature threshold. In some embodiments, the system analyzes the spatial distribution and density of the abnormal data points identified in S1063. By considering the location and number of these abnormal points, the system can infer which areas are more likely to have potential failures or abnormal conditions. For example, if a particular area has multiple over-temperature points concentrated, this area is likely to be a potential failure area. The system generates a report that details the location, range, and abnormality assessment of these potential failure areas.

[0052] In some specific embodiments, all data points exceeding a threshold are mapped in a two-dimensional or three-dimensional space; these points are clustered using a density clustering algorithm (such as DBSCAN); the formed clusters are regarded as potential fault areas; the center point and coverage of each cluster are calculated; and a report containing the location, size, and severity of the fault area is generated; this is not limited here.

[0053] In some specific embodiments, the monitoring area is divided into uniform grids; the number of data points exceeding a threshold in each grid is counted; a threshold is set and grids exceeding the threshold are marked as potential fault areas; adjacent fault grids are merged to form a larger fault area; and an analysis report containing a fault area map and detailed statistical information is output, which is not limited here.

[0054] It can be seen that by calculating the temperature change rate and the acceleration of temperature change, the short-term temperature trend is obtained. This dynamic analysis can capture the slight trend of temperature change in time, providing the possibility of early warning. The system then compares the temperature data with the preset threshold and identifies the data points that exceed the threshold. This method can not only detect abnormally high temperatures, but also abnormal temperature fluctuations. Finally, based on the location and number of data points that exceed the threshold, the system is able to determine potential fault areas. This comprehensive analysis not only provides the overall trend of temperature changes, but also accurately locates specific areas where problems may occur.

[0055] S107, receiving the historical temperature rise trend analysis result of the previous cycle; S108, determining whether the temperature rise trend analysis result is continuous and increasing with the historical temperature rise trend analysis result; In some embodiments, the system compares the current temperature rise trend analysis results with the historical results of the previous cycle. The system first checks whether the two results are continuous in time, that is, whether there is no obvious data gap. Then, the system compares key parameters such as average temperature, maximum temperature, temperature change rate, etc. to determine whether there is a continuous upward trend. Only when the temperature trend is both continuous and increasing, the system will determine it as a "continuous and increasing" state.

[0056] See also Figure 3, Figure 3 This is a flow chart of step S108 in the embodiment of the present application; In some embodiments, step S108 specifically includes: S1081. Extract key parameters from the temperature rise trend analysis results, where the key parameters include average temperature, maximum temperature, and temperature change rate; Among them, the temperature rise trend analysis result refers to the comprehensive evaluation of the temperature change over a period of time. Key parameters represent important indicators that can reflect the characteristics of temperature change. Average temperature refers to the arithmetic mean of all temperature data within the analysis period. Maximum temperature refers to the highest temperature value observed during the analysis period. Temperature change rate refers to the amount of temperature change per unit time, usually expressed in degrees of temperature change per hour or day.

[0057] S1082. Extract corresponding key parameters from the historical temperature rise trend analysis results; S1083. Calculate the time interval between the temperature rise trend analysis result and the historical temperature rise trend analysis result; In some embodiments, the system compares the timestamp or date of the current analysis result with the historical analysis result. The difference between the two time points is calculated to obtain a specific time interval. The calculation of this time interval is very important for determining whether the two analysis results are continuous, because a long time interval may mean that there is a lack of direct correlation between the two analysis results.

[0058] S1084, determining whether the time interval is less than a preset continuity threshold in the relevant data; In some embodiments, the system first reads a preset continuity threshold from a configuration file or database. Then, the system compares the time interval calculated in S1083 with the threshold. The purpose of this comparison is to determine whether the current analysis result is close enough in time to the historical result so that it can be regarded as a continuous data point. If the time interval is less than the threshold, the two analysis results are considered to be continuous in time; otherwise, they may be regarded as independent, discontinuous analysis results.

[0059] S1085. If the time interval is less than the preset continuity threshold, then: S1086, comparing whether the average temperature in the temperature rise trend analysis result is higher than the average temperature in the historical temperature rise trend analysis result; In some embodiments, the system will take out the current average temperature extracted in S1081 and the historical average temperature extracted in S1082 for direct numerical comparison. This comparison is intended to determine whether the temperature is showing an upward trend. If the current average temperature is higher than the historical average temperature, it may mean that the overall temperature level is rising, which is an important indicator for judging the temperature rise trend.

[0060] S1087, comparing whether the highest temperature in the temperature rise trend analysis result is higher than the highest temperature in the historical temperature rise trend analysis result; In some embodiments, the system obtains the maximum temperature values ​​of the current analysis period and the historical analysis period from the data extracted in S1081 and S1082, respectively. Then a direct numerical comparison is performed. The purpose of this comparison is to determine whether the temperature peak has an upward trend. If the current maximum temperature exceeds the historical maximum temperature, it may mean that the system is experiencing a higher temperature extreme than before, which is an important indicator for determining temperature anomalies or potential problems.

[0061] S1088, comparing whether the temperature change rate in the temperature rise trend analysis result is higher than the temperature change rate in the historical temperature rise trend analysis result; This step is usually performed after the highest temperature comparison is completed. In some embodiments, the system obtains the temperature change rate of the current analysis cycle and the historical analysis cycle from the data extracted in S1081 and S1082. Then a direct numerical comparison is performed. The purpose of this comparison is to determine whether the speed of temperature change is accelerating. If the current temperature change rate is higher than the historical temperature change rate, it may mean that the system temperature is rising at a faster rate, which is an important indicator for predicting potential problems and evaluating system stability.

[0062] S1089, if the three comparison results are all yes, then determine that the temperature rise trend analysis result and the historical temperature rise trend analysis result are continuous and increasing; Among them, the "three comparison results" refer to the comparison results of the average temperature, the maximum temperature and the temperature change rate performed in S1086, S1087 and S1088. This step is performed after the first three comparison steps are completed. In some embodiments, the system checks the comparison results of S1086, S1087 and S1088. Only when all three comparisons result in a "yes" result will the judgment of "continuous and increasing" be made. This strict judgment standard ensures the comprehensiveness of the temperature rising trend, that is, the average level, extreme value and change rate all show an upward trend. This judgment result is very important for early warning systems and decision support, and may trigger further investigation or preventive measures.

[0063] S10810: If the time interval is greater than or equal to the preset continuity threshold, or any of the above three comparison results is negative, it is determined that the temperature rise trend analysis result and the historical temperature rise trend analysis result do not meet the continuous and increasing conditions.

[0064] It can be seen that by extracting key parameters including average temperature, maximum temperature and temperature change rate, these parameters fully reflect the characteristics of temperature change. Then, the system calculates and determines whether the time interval between adjacent analysis results meets the continuity requirements, ensuring the temporal continuity of trend analysis. Next, the system determines whether the temperature continues to rise by comparing the key parameters in the current and historical analysis results. This multi-dimensional comparison not only considers the absolute value change of temperature, but also pays attention to the rate of change. Finally, only when all conditions are met will the system determine that the temperature rise trend is continuous and increasing, which improves the reliability of the analysis results.

[0065] S109, if the judgment result is continuous and increasing, executing the preset cooling measures; The preset cooling measures refer to predefined operations or strategies for reducing the temperature of the device. Execution means that the system automatically starts or issues instructions to implement these cooling measures.

[0066] When the system detects that the temperature continues to rise, measures need to be taken to prevent the device from overheating. In some embodiments, if the system determines that the temperature rise trend is continuous and increasing, it will trigger pre-set cooling measures. These measures may include increasing the power of the heat dissipation device, adjusting the device load, starting the backup cooling system, etc. The purpose of executing these measures is to reduce the device temperature in time and prevent potential failures or damage caused by overheating.

[0067] S110, if the temperature data is not greater than the temperature threshold, performing temperature stability analysis; In some embodiments, if the system detects that the temperature data does not exceed the preset temperature threshold, a temperature stability analysis will be performed. This analysis process may include calculating the temperature fluctuation range and change rate over a period of time. Through these analyses, the system can determine whether the temperature is in a relatively stable state or whether there is a potential fluctuation trend. This analysis helps predict future temperature changes and provides a basis for adjusting monitoring strategies.

[0068] See also Figure 4 , Figure 4 This is a flow chart of step S110 in the embodiment of the present application; In some embodiments, step S110 specifically includes: S1101, calculating the mean value and standard deviation of temperature data; In some embodiments, temperature data is first collected over a period of time, which may be a few minutes, hours, or days, depending on the application scenario. Then, statistical methods are used to calculate the mean and standard deviation of these data. The mean gives an estimate of the overall temperature level, while the standard deviation reflects the magnitude of temperature fluctuations. These two indicators lay the foundation for subsequent stability analysis.

[0069] S1102, calculating the coefficient of variation of the temperature data, where the coefficient of variation is the standard deviation divided by the mean; Among them, the coefficient of variation is a dimensionless statistic used to indicate the degree of dispersion of the standard deviation relative to the mean. This step is performed after the mean and standard deviation are calculated. In some embodiments, the calculation of the coefficient of variation provides an important indicator for subsequent judgment of temperature stability. By calculating the coefficient of variation, we can compare the degree of dispersion of temperature data of different orders of magnitude or units, which is particularly useful when comparing the temperature stability of different environments or systems. The smaller the coefficient of variation, the smaller the fluctuation of the temperature data relative to its mean, and the better the stability.

[0070] S1103, determining whether the coefficient of variation is less than a preset stability threshold; The preset stability threshold refers to a predetermined standard value for judging temperature stability.

[0071] In some embodiments, the stability of the temperature data can be preliminarily determined by comparing the coefficient of variation with a preset threshold. If the coefficient of variation is less than the preset threshold, it indicates that the relative fluctuation of the temperature data is small and may be in a stable state. This judgment provides an important basis for subsequent comprehensive evaluation. The selection of the preset threshold is usually based on the specific application scenario and requirements, and may need to be determined through experience or experiments.

[0072] S1104, calculating the first-order difference of the temperature data to obtain the temperature change rate; The first-order difference refers to the difference between two adjacent temperature data points.

[0073] In some embodiments, by calculating the first order difference of the temperature data, we can obtain the temperature change rate at each time point. This information is very important for understanding the trend and speed of temperature change. The temperature change rate can reflect the instantaneous change of temperature and help identify sudden changes or rapid changes in temperature.

[0074] S1105, determining whether the absolute value of the temperature change rate is less than a preset change rate threshold; The preset change rate threshold refers to a predetermined standard value for judging whether the temperature change is significant.

[0075] In some embodiments, the significance of the temperature change can be evaluated by comparing the absolute value of the temperature change rate with a preset threshold. If the absolute value of the change rate is less than the preset threshold, it indicates that the temperature change is relatively slow and may be in a stable state. This judgment takes into account the dynamic characteristics of temperature and provides another important dimension for comprehensively evaluating temperature stability. The selection of the preset change rate threshold is usually based on specific application requirements and system characteristics.

[0076] S1106, calculating the second-order difference of the temperature data to obtain the temperature change acceleration; The second-order difference refers to the result obtained by performing a difference operation on the first-order difference sequence again.

[0077] In some embodiments, by calculating the second order difference of the temperature data, we can get the acceleration of the temperature change. This information is very important for understanding the change in the temperature change trend, and can help identify abrupt changes in the temperature change pattern or rapid changes in the temperature change rate. The acceleration of temperature change can reflect whether the temperature change is accelerating or slowing down, providing an additional dimension for a deeper analysis of temperature stability.

[0078] S1107, determining whether the absolute value of the temperature change acceleration is less than a preset acceleration threshold; S1108, if the coefficient of variation is less than the preset stability threshold, and the absolute value of the temperature change rate is less than the preset change rate threshold, and the absolute value of the temperature change acceleration is less than the preset acceleration threshold, then the temperature is determined to be in a stable state; This step is performed after all previous calculations and single judgments are completed. In some embodiments, this step comprehensively considers the statistical characteristics (through the coefficient of variation) and dynamic characteristics (through the rate of change and acceleration) of the temperature data to comprehensively evaluate the temperature stability. Only when all three conditions are met at the same time, the temperature is judged to be in a stable state. This multi-dimensional judgment standard can more accurately identify the true stable state and avoid misjudgment due to fluctuations in a single indicator.

[0079] S1109, if any of the conditions is not met, the temperature is determined to be in an unstable state; S11010. Evaluate the degree of temperature stability based on the duration of the stable state; This step is performed after determining that the temperature is in a stable state. In some embodiments, the reliability of temperature stability can be further evaluated by analyzing the duration of the stable state. The longer the duration, the higher the temperature stability. This evaluation can help distinguish short-term fluctuations from long-term stability, providing more valuable information for subsequent temperature control or system operation.

[0080] In some specific embodiments, the time point of entering the stable state each time is recorded; the duration of the continuous stable state is calculated; different time thresholds are set corresponding to different stability levels; and the stability level is determined based on the duration, which is not limited here.

[0081] S11011. Generate temperature stability analysis results including stable state judgment results and stability degree assessment.

[0082] In some embodiments, this step combines the results of the previous steps to form a comprehensive temperature stability analysis report. This report not only includes the judgment of whether the temperature is stable, but also includes the degree of stability assessment, providing users with rich and detailed information to help make further decisions or take corresponding actions.

[0083] It can be seen that by calculating the mean and standard deviation of the temperature data, and evaluating the overall temperature fluctuation through the coefficient of variation. This statistical method can effectively measure the degree of discreteness of the temperature distribution. Then, the system calculates the first-order difference and the second-order difference to obtain the temperature change rate and the acceleration of change. These indicators can reflect the dynamic characteristics of temperature changes. Next, the system compares these indicators with the preset thresholds to determine whether the temperature is in a stable state from multiple dimensions. Finally, the system will also evaluate the duration of the stable state to further quantify the degree of temperature stability. This multi-angle and multi-level analysis method can accurately determine the stability of temperature.

[0084] S111. According to the temperature stability analysis result, the data collection frequency and period in the relevant data are adjusted to update the relevant data.

[0085] In some embodiments, the system adjusts the frequency and period of data collection based on the results of the temperature stability analysis. If the temperature is relatively stable, the system may reduce the collection frequency to save resources; if the temperature is found to be fluctuating, the collection frequency may be increased to monitor the changes more closely. These adjustments will be updated to the relevant data and affect the execution of the next cycle. Through this dynamic adjustment, the system can optimize resource usage while ensuring monitoring results.

[0086] It can be seen that by receiving equipment scale data and outputting relevant data, a basis is provided for subsequent processing. Then, by looping through a series of sub-steps, the intelligentization of temperature monitoring is realized. When the temperature exceeds the threshold, the system increases the frequency of data acquisition to ensure more intensive monitoring of temperature changes at critical moments. At the same time, by analyzing the temperature rise trend and identifying potential fault areas, the system can detect abnormal conditions in a timely manner. In addition, by comparing the current and historical temperature rise trends, the system can determine whether cooling measures need to be implemented. Finally, through temperature stability analysis, the system can adjust the data acquisition strategy according to actual conditions. This dynamic adjustment method can effectively cope with the temperature change characteristics of equipment of different scales and improve the detection efficiency and accuracy of temperature anomalies.

[0087] See also Figure 5 , Figure 5 This is another flow chart of the temperature rise test method for low-voltage complete switchgear in the embodiment of the present application; In some embodiments, after step S104, the method further includes: S201, performing frequency domain analysis on the temperature data to obtain periodic characteristics; In some embodiments, the system performs frequency domain analysis on temperature data within a certain time range to identify possible periodic patterns in temperature variation. This analysis can help the system understand the inherent laws of temperature variation, such as possible daily variation, seasonal variation, etc. By identifying these periodic features, the system can better predict future temperature changes and optimize data collection strategies accordingly.

[0088] S202, determining a temperature peak and a temperature trough according to periodic characteristics; Among them, the temperature peak refers to the highest point reached by the temperature in a cycle. The temperature trough refers to the lowest point reached by the temperature in a cycle. In some embodiments, the system uses the periodic characteristics obtained in S201 to predict the pattern of temperature changes, thereby determining the time points and temperature values ​​at which the temperature peaks and troughs may occur. This prediction can help the system prepare in advance, increase the frequency of data collection at critical moments (such as when the temperature is about to reach a peak), and reduce the frequency of data collection in less critical periods (such as when the temperature is at a trough), thereby optimizing resource utilization.

[0089] S203. Increase the data collection density of the temperature peak and reduce the data collection density of the temperature valley.

[0090] In some embodiments, the system dynamically adjusts the frequency of data collection based on the predicted temperature peak and valley times. In the time period close to the temperature peak, the system will increase the data collection density to more accurately capture the highest temperature point and the changes around it. Conversely, during the temperature valley, the system will reduce the data collection density because the temperature changes during this period are usually small and do not require too frequent monitoring. This strategy can optimize the use of system resources while ensuring the acquisition of key data.

[0091] It can be seen that by performing frequency domain analysis on temperature data, the periodic characteristics of temperature changes can be identified. This analysis method can reveal the inherent laws of temperature changes, rather than relying solely on time domain data. Then, based on these periodic characteristics, the system can accurately determine the peaks and valleys of temperature. This precise positioning lays the foundation for subsequent differentiated sampling. Finally, the system will increase the data acquisition density at the temperature peak and reduce the acquisition density at the valley. Not only can it capture temperature changes at critical moments, but it can also optimize resource usage while ensuring data quality and avoid unnecessary oversampling.

[0092] In some embodiments, after step S203, the method further includes: S204, generating a time interval sequence according to the increased data collection frequency; In some embodiments, the system generates a new time interval sequence according to the increased data collection frequency determined in S203. This sequence includes a series of time points indicating when the system should collect temperature data. In particular, near the temperature peak, the time interval becomes shorter to achieve more intensive data collection. This approach ensures that more data points are obtained during the period when the temperature changes most dramatically, thereby more accurately capturing the details of the temperature changes.

[0093] In some specific embodiments, a high-frequency sampling time window around a peak is determined; a sampling interval within the window is calculated, which is equal to the window length divided by the increased number of sampling times; starting from the start time of the window, time points are added sequentially according to the calculated intervals; for areas outside the window, the original sampling intervals are used; all time points are arranged in chronological order to form a final time interval sequence, which is not limited here.

[0094] In some specific embodiments, a baseline sampling interval is set according to the temperature change rate; in areas with high temperature change rates, the sampling interval is gradually shortened; in areas with low temperature change rates, the sampling interval is gradually increased; the minimum and maximum sampling intervals are set to ensure that the sampling frequency is within a reasonable range; and a time point sequence that meets the above conditions is generated, which is not limited here.

[0095] S205, sampling temperature data between a temperature peak and a temperature trough according to the time interval sequence to obtain additional temperature data; The additional temperature data refers to the temperature measurement values ​​obtained through the new sampling strategy, which are additionally increased compared to the original fixed frequency sampling.

[0096] In some embodiments, the system performs more intensive temperature data sampling in the area between the temperature peak and the temperature trough according to the time interval sequence generated in S204. This sampling strategy pays special attention to the interval with the most significant temperature change, and can capture more details of the temperature change. The additional temperature data obtained in this way can provide richer and more detailed temperature change information than the original fixed frequency sampling, which helps to more accurately analyze and predict temperature trends.

[0097] S206, comparing whether the difference between the additional temperature data and the temperature data obtained by fixed frequency sampling exceeds a preset abnormal threshold; In some embodiments, the system compares the newly acquired high-frequency temperature data with the original fixed-frequency sampled data. This comparison usually involves finding the closest points in time between the two sets of data, calculating the temperature difference between them, and then comparing this difference with a pre-set anomaly threshold. The purpose of this step is to identify significant temperature changes that may be missed by fixed-frequency sampling, thereby discovering potential anomalies or important temperature fluctuations.

[0098] S207, determining the temperature data of the sampling point exceeding the preset abnormal threshold as an abnormal point; S208, determining the location information of the abnormal point; The system determines the physical location information for each data point marked as an anomaly. This process may involve querying the installation location database of temperature sensors, or using pre-established spatial mapping relationships to infer the specific location of the anomaly. Determining the location information of the anomaly is crucial to understanding the environmental context in which the anomaly occurred, assessing the potential impact range, and formulating targeted response measures.

[0099] S209, identifying temperature sensors in the adjacent area according to the location information; In some embodiments, the system uses the location information of the anomaly to identify other temperature sensors in the surrounding area. This process usually involves searching in a pre-established sensor network topology or calculating spatial distances to determine which sensors are located in the vicinity of the anomaly. Identifying sensors in the vicinity is important for further verifying the anomaly, assessing the impact of the anomaly, and developing a more comprehensive monitoring strategy.

[0100] S210: Send a high-frequency sampling instruction to the temperature sensor in the adjacent area.

[0101] In some embodiments, the system sends instructions to the neighboring sensors determined in S209, requiring them to increase the frequency of data collection. The purpose of this practice is to obtain more data in order to better understand and analyze the abnormal temperature changes. By increasing the sampling frequency of the neighboring area, the system can more accurately track the development trend of temperature anomalies, assess their impact range, and provide more timely and detailed information support for possible intervention measures.

[0102] It can be seen that by generating a time interval sequence based on the adjusted acquisition frequency, this provides a time reference for subsequent precise sampling. Then, additional temperature data sampling is performed between temperature peaks and troughs. This strategic sampling can capture temperature changes that may be ignored by conventional fixed-frequency sampling. Next, the system compares these additional sampled data with the fixed-frequency sampled data. If the difference exceeds the preset threshold, it will be marked as an anomaly. This method can not only detect temperature anomalies, but also accurately locate the location of the anomaly. Finally, the system identifies temperature sensors near the anomaly and sends high-frequency sampling instructions to them, achieving focused monitoring of potential problem areas.

[0103] The following introduces a temperature rise test system 600 for an exemplary low-voltage complete switchgear provided in an embodiment of the present application. Figure 6 It is a schematic diagram of an exemplary hardware structure of a temperature rise test system 600 for a low-voltage complete switchgear provided in an embodiment of the present application.

[0104] In some embodiments, the temperature rise test system 600 of the low-voltage complete switchgear is a computer device or the temperature rise test system 600 of the low-voltage complete switchgear includes a computer device. The computer device includes a processor, a memory and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0105] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0106] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0107] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0108] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0109] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A temperature rise test method for low voltage complete switchgear, characterized in that: include: Receive scale data of low-voltage complete switchgear and output relevant data; The relevant data is used as a loop variable to loop through the following sub-steps: receiving temperature data, and determining whether the temperature data is greater than a temperature threshold in the relevant data; If the temperature data is greater than the temperature threshold, increasing the data collection frequency in the relevant data according to a preset rule; receiving subsequent temperature data at the increased data collection frequency; Outputting a temperature rise trend analysis result and a potential fault area identification result according to the temperature data and the subsequent temperature data; Receive the historical temperature rise trend analysis results of the previous cycle; Determine whether the temperature rise trend analysis result is continuous and increasing with the historical temperature rise trend analysis result; If the judgment result is continuous and increasing, the preset cooling measures are executed; If the temperature data is not greater than the temperature threshold, performing temperature stability analysis; According to the temperature stability analysis result, the data collection frequency and period in the relevant data are adjusted to achieve the updating of the relevant data.

2. The method according to claim 1, characterized in that The preset rules are: Calculate the temperature change rate and the temperature change acceleration according to the temperature data; Obtaining a short-term temperature trend according to the temperature change rate and the temperature change acceleration; In the event that the short-term temperature trend indicates a temperature increase, increasing the data collection frequency; In the event that the short-term temperature trend shows a temperature drop, reducing the data collection frequency; The adjustment range of the data acquisition frequency is determined according to the absolute value of the temperature change rate.

3. The method according to claim 1, characterized in that After the step of increasing the data collection frequency in the relevant data according to a preset rule if the temperature data is greater than the temperature threshold, the method further includes: Performing frequency domain analysis on the temperature data to obtain periodic characteristics; Determining a temperature peak and a temperature trough according to the periodic characteristics; The data collection density of the temperature peak is increased, and the data collection density of the temperature valley is reduced.

4. The method according to claim 3, characterized in that After the step of increasing the data acquisition density of the temperature peak and reducing the data acquisition density of the temperature valley, the method further includes: generating a time interval sequence according to the increased data collection frequency; According to the time interval sequence, sampling temperature data between the temperature peak and the temperature valley to obtain additional temperature data; Comparing whether the difference between the additional temperature data and the temperature data obtained by fixed frequency sampling exceeds a preset abnormal threshold; Determine the temperature data of the sampling point exceeding the preset abnormal threshold as an abnormal point; Determining the location information of the abnormal point; identifying temperature sensors in a neighboring area based on the location information; A high-frequency sampling instruction is sent to the temperature sensor in the adjacent area.

5. The method according to claim 1, characterized in that The step of outputting the temperature rise trend analysis result and the potential fault area identification result according to the temperature data and the subsequent temperature data specifically includes: Calculate the temperature change rate and the temperature change acceleration according to the temperature data; Obtaining a short-term temperature trend according to the temperature change rate and the temperature change acceleration, and outputting the temperature rise trend analysis result; comparing the temperature data and the subsequent temperature data to the temperature threshold and identifying data points that exceed the temperature threshold; Based on the location and number of the data points exceeding the temperature threshold, a potential fault area is determined, and the potential fault area identification result is output.

6. The method according to claim 1, characterized in that The step of judging whether the temperature rise trend analysis result is continuous and increasing with the historical temperature rise trend analysis result specifically includes: Extracting key parameters from the temperature rise trend analysis result, wherein the key parameters include average temperature, maximum temperature and temperature change rate; Extracting corresponding key parameters from the historical temperature rise trend analysis results; Calculating the time interval between the temperature rise trend analysis result and the historical temperature rise trend analysis result; Determining whether the time interval is less than a preset continuity threshold in the relevant data; If the time interval is less than the preset continuity threshold, then: Comparing whether the average temperature in the temperature rise trend analysis result is higher than the average temperature in the historical temperature rise trend analysis result; Comparing whether the maximum temperature in the temperature rise trend analysis result is higher than the maximum temperature in the historical temperature rise trend analysis result; comparing whether the temperature change rate in the temperature rise trend analysis result is higher than the temperature change rate in the historical temperature rise trend analysis result; If all three comparison results are yes, it is determined that the temperature rise trend analysis result and the historical temperature rise trend analysis result are continuous and increasing; If the time interval is greater than or equal to the preset continuity threshold, or any of the three comparison results is negative, it is determined that the temperature rise trend analysis result and the historical temperature rise trend analysis result do not meet the continuous and increasing conditions.

7. The method according to claim 1, characterized in that The step of performing temperature stability analysis specifically includes: Calculate the mean and standard deviation of the temperature data; Calculating a coefficient of variation of the temperature data, the coefficient of variation being the standard deviation divided by the mean; Determining whether the coefficient of variation is less than a preset stability threshold; Calculating the first-order difference of the temperature data to obtain the temperature change rate; Determining whether the absolute value of the temperature change rate is less than a preset change rate threshold; Calculating the second-order difference of the temperature data to obtain the temperature change acceleration; Determining whether the absolute value of the temperature change acceleration is less than a preset acceleration threshold; If the coefficient of variation is less than the preset stability threshold, the absolute value of the temperature change rate is less than the preset change rate threshold, and the absolute value of the temperature change acceleration is less than the preset acceleration threshold, then the temperature is determined to be in a stable state; If any of the conditions is not met, the temperature is judged to be in an unstable state; assessing the degree of temperature stability based on the duration of said stable state; Generates temperature stability analysis results including stable state judgment results and stability degree assessment.

8. A temperature rise test system for low voltage complete switchgear, characterized in that: The temperature rise test system of the low-voltage complete switchgear comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions to enable the temperature rise test system of the low-voltage complete switchgear to execute the method as described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product is run on a temperature rise test system for a low-voltage complete switchgear assembly, the temperature rise test system for the low-voltage complete switchgear assembly executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a temperature rise test system for a low-voltage complete switchgear assembly, the temperature rise test system for the low-voltage complete switchgear assembly executes the method according to any one of claims 1 to 7.

Images