Intelligent monitoring method and system for operation state of power distribution cabinet

Abstract

The invention relates to the technical field of power distribution cabinet operation state monitoring, and specifically discloses a power distribution cabinet operation state intelligent monitoring method and system. The method mainly comprises the following steps: determining a specified number of time points t in a day; acquiring real-time monitoring data and historical monitoring data; determining an equipment reference temperature RT1nt, an external reference temperature RAt and a reference current load RCnt based on historical monitoring data; constructing an equipment temperature reference interval ST1nt based on the equipment reference temperature RT1nt; and judging whether the equipment temperature TEnt of the equipment En in the real-time monitoring data is within the range of the equipment temperature reference interval STnt or not. According to the invention, a dynamic reference temperature interval under similar conditions can be constructed, and the change conditions of equipment temperature and current load can be monitored in real time, so that equipment with potential problems can be rapidly detected through abnormal fluctuation even if the variable quantity is small, and the abnormal trend in equipment operation can be found in advance, thereby preventing small problems from developing into large faults, and improving the safety of the equipment. And the safety and the stability of the equipment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution cabinet operation status monitoring, and in particular to a method and system for intelligently monitoring the operation status of a power distribution cabinet. Background Art

[0002] Distribution cabinet monitoring usually involves real-time monitoring of the operating status of multiple devices in the distribution cabinet, including key parameters such as temperature, current, and voltage. Through sensors and monitoring systems, it monitors whether the temperature of the equipment is too high, whether the current load is too large, etc., to prevent equipment overheating, short circuit or failure. The monitoring system can not only collect the operating data of the equipment in real time, but also issue early warnings based on set thresholds or benchmark data to ensure that the equipment operates under safe and stable conditions, effectively avoiding power outages or safety hazards caused by equipment failure.

[0003] The traditional method of monitoring abnormalities of distribution cabinet equipment is usually to monitor whether the temperature of the equipment reaches a preset threshold. When the temperature exceeds the threshold, the system will issue an alarm. However, since the alarm is only triggered after the temperature reaches the threshold, the system often finds it difficult to detect potential failures or abnormalities of the equipment in a timely manner, especially when the temperature of the equipment is rising. Hidden dangers may have already occurred, resulting in the inability to take preventive measures in advance, further increasing the risk of failure. Summary of the invention

[0004] The present application provides a method and system for intelligently monitoring the operating status of a distribution cabinet, thereby solving the problem that when monitoring distribution cabinet equipment using traditional methods in the prior art, hidden dangers may have occurred during the process of equipment temperature rising, but it is difficult to detect potential failures or abnormalities of the equipment in time. The application realizes early detection of abnormal trends in equipment operation, thereby avoiding small problems from developing into major failures and improving the safety and stability of the equipment.

[0005] In a first aspect, the present application provides an intelligent monitoring method for the operating status of a distribution cabinet, which includes the following steps: determining a specified number of time points t within a day; obtaining real-time monitoring data and historical monitoring data, the monitoring data including: external temperature AE_t of the distribution cabinet, device temperature TE_nt of device En, and current load CE_nt of device En; wherein the external temperature AE is the air temperature of the environment in which the distribution cabinet is located, En represents the nth device in the distribution cabinet, TE_nt represents the temperature of the nth device at time point t, and CE_nt represents the current load of the nth device at time point t; determining the device reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt based on the historical monitoring data; wherein RT1_nt represents the reference temperature of the nth device at time point t, RA_t represents the external reference temperature of the distribution cabinet at time point t, and RC_nt represents the reference current load of the nth device at time point t; based on the device reference temperature R T1_nt constructs the device temperature reference interval ST1_nt; wherein ST1_nt represents the temperature reference interval of the nth device at time point t; determines whether the device temperature TE_nt of device En in the real-time monitoring data is within the range of the device temperature reference interval ST_nt; if the device temperature TE_nt is not within the range of the temperature reference interval ST_nt, then calculates the difference D_0 between the device temperature TE_nt and the device reference temperature RT1_nt, the difference D_1 between the external temperature AE_t and the external reference temperature RA_t, and the difference D_2 between the current load CE_nt and the reference current load RC_nt; determines whether the difference D_0, the difference D_1 and the difference D_3 meet one of the following two conditions a1 and b1: a1: the difference D_0 is a positive value, and the difference D_1 and the difference D_2 are not positive values; b1: the difference D_0 is a negative value, and the difference D_1 and the difference D_2 are not negative values; if one of the two conditions a and b is met, an abnormal warning is issued.

[0006] Furthermore, if one of the two conditions a and b is not met, an external temperature reference interval SA and a current load reference interval SC_n are constructed based on the real-time monitoring data; wherein SC_n represents the current load reference interval of the nth device; a record R that meets specific conditions is found from the historical monitoring data of the device En, and the specific conditions are: a2. The external temperature AE is within the external temperature reference interval SA; b2. The current load CE_n is within the current load reference interval SC_n; a device temperature reference interval ST2_n is constructed based on the record R; and it is determined whether the device temperature TE_nt in the real-time monitoring data is within the range of the device temperature reference interval ST2_n. If not, an abnormal warning is issued.

[0007] Furthermore, determining the device reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt of each device based on historical monitoring data includes: determining the current date; and selecting historical monitoring data of the day before the current date as the device reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt.

[0008] Furthermore, the device temperature reference interval ST1_nt is: ST1_nt=[RT1_nt-k1,RT1_nt+k1], where k1 represents a preset parameter.

[0009] Furthermore, the external temperature reference interval SA is: SA=[AE_t-k2,AE_t+k2]; wherein k2 represents a parameter; the current load reference interval SC_n is: SC_n=[CE_nt-k3,CE_nt+k3]; wherein k3 represents a parameter.

[0010] Further, k2=c*k21, k3=c*k31; wherein k21 and k31 represent preset parameters, and c represents a coefficient; searching for records R that meet specific conditions from the historical monitoring data of device En includes: Sa. Initializing the value of c to 1; Sb. Traversing the historical monitoring data of device En, and for each record in the historical monitoring data, searching for records that meet specific conditions; Sc. If no record that meets the specific conditions is found, increase the value of the coefficient c by 1, and execute Sb until a specified number of records R that meet the specific conditions are found or the coefficient c reaches a preset maximum value c_max.

[0011] Furthermore, constructing the device temperature reference interval ST2_n based on the record R includes: calculating the average temperature of a specified number of device temperatures TE_n in the record R as the device reference temperature RT2_n; constructing the device temperature reference interval ST2_n based on the device reference temperature RT2_n, ST2_n is: ST2_n=[RT2_n-k4,RT2_n+k4], where k4 represents a parameter.

[0012] Furthermore, if the device temperature TE_nt is greater than a preset device temperature threshold TE_nmax or the current load CE_nt is greater than a preset current load CE_nmax, an abnormality warning is issued.

[0013] Furthermore, starting from midnight 00:00, a time point t is recorded every half hour until 23:30.

[0014] In the second aspect, the present application provides an intelligent monitoring system for the operating status of a distribution cabinet, which adopts the intelligent monitoring method for the operating status of a distribution cabinet as described in the first aspect, and includes: a monitoring time point determination module, a monitoring data acquisition module, a benchmark parameter determination module and an abnormality judgment module.

[0015] The monitoring time point determination module is used to determine a specified number of time points t within a day; the monitoring data acquisition module is used to acquire real-time monitoring data and historical monitoring data, and the monitoring data include: external temperature AE_t of the distribution cabinet, device temperature TE_nt of the device En, and current load CE_nt of the device En; wherein, the external temperature AE is the air temperature of the environment where the distribution cabinet is located, En represents the nth device in the distribution cabinet, TE_nt represents the temperature of the nth device at time point t, and CE_nt represents the current load of the nth device at time point t; the benchmark parameter determination module is used to determine the device benchmark temperature RT1_nt, external benchmark temperature RA_t, and benchmark current load RC_nt based on historical monitoring data; wherein RT1_nt represents the benchmark temperature of the nth device at time point t, RA_t represents the external benchmark temperature of the distribution cabinet at time point t, and RC_nt represents the benchmark current load of the nth device at time point t; construct a benchmark parameter based on the device benchmark temperature RT1_nt Establish a device temperature reference interval ST1_nt; wherein ST1_nt represents the temperature reference interval of the nth device at time point t; the abnormal judgment module is used to judge whether the device temperature TE_nt of the device En in the real-time monitoring data is within the range of the device temperature reference interval ST_nt; if the device temperature TE_nt is not within the range of the temperature reference interval ST_nt, then calculate the difference D_0 between the device temperature TE_nt and the device reference temperature RT1_nt, the difference D_1 between the external temperature AE_t and the external reference temperature RA_t, and the difference D_2 between the current load CE_nt and the reference current load RC_nt; judge whether the difference D_0, the difference D_1 and the difference D_3 meet one of the following two conditions a1 and b1: a1: the difference D_0 is a positive value, and the difference D_1 and the difference D_2 are not positive values; b1: the difference D_0 is a negative value, and the difference D_1 and the difference D_2 are not negative values; if one of the two conditions a and b is met, an abnormal warning is issued.

[0016] In the third aspect, the present application also provides an intelligent monitoring device for the operating status of a distribution cabinet, which includes: a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the intelligent monitoring method for the operating status of a distribution cabinet described in the first aspect.

[0017] The technical solution provided by this application has at least the following technical effects or advantages: By introducing the equipment reference temperature, external reference temperature and reference current load, a dynamic reference temperature range under similar conditions can be constructed, and the changes in equipment temperature and current load can be monitored in real time. This effectively solves the problem that when the traditional method of monitoring the distribution cabinet equipment in the prior art is used, hidden dangers may have occurred during the rise in equipment temperature, but it is difficult to detect potential equipment failures or abnormal problems in time. Even if the change is small, the equipment with potential problems can be quickly detected through abnormal fluctuations, and abnormal trends in equipment operation can be discovered in advance, thereby avoiding small problems from developing into major failures and improving the safety and stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the overall process of the intelligent monitoring method for the operation status of the power distribution cabinet in this application; Figure 2 A flow chart of searching for records R that meet specific conditions from historical monitoring data of equipment En for this application; Figure 3 This is the module diagram of the intelligent monitoring system for the operating status of the distribution cabinet in this application. DETAILED DESCRIPTION

[0019] In order to solve the problems raised by the background technology, the present application introduces device reference temperature, external reference temperature and reference current load to construct a dynamic reference temperature range under similar conditions, and monitor the changes in device temperature and current load in real time, so that even if the change is small, potential problems can be quickly detected through abnormal fluctuations.

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] Embodiment 1: Figure 1-Figure 2 As shown, this embodiment provides a method for intelligently monitoring the operating status of a power distribution cabinet, which includes the following steps: S100. Determine a specified number of time points t in a day.

[0022] In S100, a time point t may be recorded every half hour starting from midnight 00:00 until 23:30. By recording the time point t every half hour in 24 hours a day, the operating status of the distribution cabinet can be monitored around the clock.

[0023] S200. Obtain real-time monitoring data and historical monitoring data, the monitoring data including: external temperature AE_t of the distribution cabinet, device temperature TE_nt of device En, and current load CE_nt of device En; wherein, external temperature AE is the air temperature of the environment where the distribution cabinet is located, En represents the nth device in the distribution cabinet, TE_nt represents the temperature of the nth device at time point t, and CE_nt represents the current load of the nth device at time point t.

[0024] In S200, the monitoring data is divided into real-time monitoring data and historical monitoring data. The sensor can collect the external temperature AE_t, the device temperature TE_nt of the device En, and the current load CE_nt of the device En at each time point t, obtain the real-time monitoring data and retrieve the historical monitoring data for subsequent benchmark comparison. Since there are generally many devices in the power distribution cabinet, the device temperature TE_nt and the current load CE_n can be monitored separately for different devices. Monitoring is performed once at each time point t, which can effectively capture the temperature changes and current load changes of the equipment within a day, and at the same time can better balance the real-time nature of the data and the load processed by the system, thereby improving the operating efficiency of the overall system.

[0025] S300. Determine the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt based on historical monitoring data; wherein RT1_nt represents the reference temperature of the nth device at time point t, RA_t represents the external reference temperature of the distribution cabinet at time point t, and RC_nt represents the reference current load of the nth device at time point t.

[0026] In S300, the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt can be determined based on recent historical monitoring data. For equipment that needs to run continuously to ensure a stable supply of electricity, the recent equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt are used as references to determine whether the real-time monitoring data is abnormal.

[0027] For example, at 10:00 am yesterday, the temperature of device En was 45°C, the current load was 90A, and the external ambient temperature of the distribution cabinet was 30°C. Therefore, the device's reference temperature RT1_nt is determined to be 45°C, the reference current load RC_nt is 90A, and the external reference temperature RA_t is 30°C.

[0028] S400. Construct a device temperature reference interval ST1_nt based on the device reference temperature RT1_nt; wherein ST1_nt represents the temperature reference interval of the nth device at time point t.

[0029] In S400, the reference interval is centered on the device reference temperature RT1_nt and fluctuates up and down by k1, so as to tolerate temperature fluctuations within a certain range, rather than relying on a fixed single value. For example, if the device reference temperature RT1_nt is 50°C and k1 is set to 5°C, the device temperature reference interval ST1_nt is [45°C, 55°C].

[0030] S500. Determine whether the device temperature TE_nt of the device En in the real-time monitoring data is within the range of the device temperature reference interval ST_nt.

[0031] In S500 , by comparing the real-time temperature TE_nt of the device with the reference temperature interval ST1_nt, it is determined whether the temperature of the device is normal.

[0032] S600. If the device temperature TE_nt is not within the temperature reference interval ST_nt, calculate the difference D_0 between the device temperature TE_nt and the device reference temperature RT1_nt, the difference D_1 between the external temperature AE_t and the external reference temperature RA_t, and the difference D_2 between the current load CE_nt and the reference current load RC_nt; determine whether the difference D_0, the difference D_1, and the difference D_3 satisfy one of the following two conditions a1 and b1; if one of the two conditions a and b is satisfied, issue an abnormal warning.

[0033] a1: The difference D_0 is a positive value, and the difference D_1 and the difference D_2 are not positive values.

[0034] b1: The difference D_0 is a negative value, and both the difference D_1 and the difference D_2 are not negative values.

[0035] When the system detects that the device temperature changes too much, it enters the abnormal judgment process. If the difference D_0, difference D_1 and difference D_3 meet one of the two conditions a1 and b1, it means that the change in external ambient temperature and the change in current load current cannot explain the change in device temperature, and thus an abnormal warning is issued.

[0036] For example, at 10:00 am, the device's reference temperature RT1_nt is 45°C, the reference current load RC_nt is 90A, the external reference temperature RA_t is 30°C, the reference range ST1_nt is [40°C, 50°C], and the device temperature TE_nt in the real-time monitoring data is 60°C. The system detects that the device temperature is too high and enters the abnormal judgment process.

[0037] During the abnormal judgment process, the current load CE_nt of the device in the real-time monitoring data is 88A, the external reference temperature RA_t is 28°C, the difference D_0, difference D_1 and difference D_3 are 15, -2, and -2 respectively. The changes in the external ambient temperature and the current load current are difficult to explain the changes in the device temperature, and an abnormal warning is issued.

[0038] In S100-S600, a reference interval ST1_nt of the device temperature is constructed based on historical data to determine whether the device temperature TE_nt is within the reference interval. If the real-time temperature TE_nt of the device is not within the reference interval ST_nt, the system will further calculate the difference D_0 between the device temperature and the reference temperature, the difference D_1 between the external temperature and the reference temperature, and the difference D_2 between the current load and the reference current. By judging whether the difference D_0, the difference D_1, and the difference D_2 meet the conditions a1 or b1, it is determined whether the device is abnormal and a warning is issued. By constructing a dynamic reference temperature interval under similar conditions and monitoring the changes in the device temperature and current load in real time, potential problems can be quickly detected through abnormal fluctuations even if the changes are small, thereby improving the safety and reliability of the distribution cabinet. In addition, it can effectively filter short-term fluctuations and reduce the possibility of false alarms.

[0039] For example, in a low-temperature environment in winter, although the temperature of a device in the distribution cabinet has not yet reached the preset threshold, its working status has deviated from the normal baseline range. Through this technical solution, this abnormal situation can be discovered in time, an early warning can be issued, and equipment shutdown or damage can be avoided.

[0040] S700. If one of the two conditions a and b is not met, then an external temperature reference interval SA and a current load reference interval SC_n are constructed based on the real-time monitoring data; wherein SC_n represents the current load reference interval of the nth device; and a record R that meets a specific condition is found from the historical monitoring data of the device En, and the specific condition is: a2. The outside temperature AE is within the outside temperature reference interval SA.

[0041] b2. The current load CE_n is within the current load reference interval SC_n.

[0042] If one of the two conditions a and b is not met, it is necessary to further determine whether the changes in the external ambient temperature and the changes in the current load current can explain the changes in the device temperature. By constructing the external temperature reference interval SA and the current load reference interval SC_n, find the record R of the device in a similar environment from the historical monitoring data, so as to compare it with the real-time monitoring data to determine whether to issue an abnormal warning.

[0043] For example, the current external temperature is 25°C, the constructed external temperature reference interval SA is [23°C, 27°C], and the current load reference interval SC_n of the device is [80A, 100A]. Find the record R that meets both conditions from the historical monitoring data for subsequent judgment.

[0044] S800. Construct a device temperature reference interval ST2_n based on the record R; determine whether the device temperature TE_nt in the real-time monitoring data is within the device temperature reference interval ST2_n. If not, issue an abnormal warning.

[0045] After obtaining the record R, the temperature of the device in a similar environment can be obtained from the record R to determine whether the device temperature TE_nt in the real-time monitoring data is still similar to the data in the historical monitoring record. This is specifically achieved by constructing a device temperature reference interval ST2_n. If the device temperature TE_nt in the real-time monitoring data is within the device temperature reference interval ST2_n, it means that the device temperature TE_nt in the real-time monitoring data is still significantly different from the data in the historical monitoring record, which may indicate that the device En is in an abnormal state, thereby issuing an abnormal warning.

[0046] For example, the temperature of the device in a similar environment recorded in R is 52°C, 50°C, and 54°C. The device temperature reference interval ST2_n constructed based on the temperature data of these devices is [47°C, 57°C]. If the device temperature TE_nt in the real-time monitoring data is 60°C, which is not within the device temperature reference interval ST2_n, it is likely that the device En is in an abnormal state.

[0047] In S300, determining the device reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt of each device based on historical monitoring data includes: S310. Determining the current date; S320. Selecting the historical monitoring data of the day before the current date as the device reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt.

[0048] Using the data from the previous day as a benchmark, we construct a benchmark temperature and benchmark current load range suitable for the current environment. In a year, except for days when the weather changes from sunny to cloudy, the temperature difference between two adjacent days generally does not change much. The historical monitoring data from the previous day is used as the equipment benchmark temperature RT1_nt, external benchmark temperature RA_t, and benchmark current load RC_nt. This dynamic update mechanism can accurately improve the accuracy of anomaly detection and reduce the risk of misjudgment.

[0049] In S400, the device temperature reference interval ST1_nt is: ST1_nt=[RT1_nt-k1,RT1_nt+k1], where k1 represents a preset parameter. The reference interval ST1_nt is defined by the device's reference temperature RT1_nt and parameter k1. The value of the k1 parameter setting depends on the device's operating characteristics and environmental conditions to ensure that the temperature fluctuation is within a reasonable range.

[0050] In S700, the external temperature reference interval SA is: SA=[AE_t-k2,AE_t+k2]; where k2 represents a parameter; the current load reference interval SC_n is: SC_n=[CE_nt-k3,CE_nt+k3]; where k3 represents a parameter.

[0051] The external temperature reference interval SA is constructed based on the real-time external temperature AE_t and parameter k2, and the current load reference interval SC_n is constructed based on the real-time current load CE_nt and parameter k3. By setting the reference intervals of external temperature and current load respectively, the environmental changes and load fluctuations of the equipment can be effectively captured, ensuring the accuracy of judging the operating status of the equipment, and further improving the system's abnormality detection capability.

[0052] In this embodiment, k2=c*k21, k3=c*k31; wherein k21 and k31 represent preset parameters, and c represents a coefficient. Meanwhile, in S700, searching for a record R that meets a specific condition from the historical monitoring data of the device En includes the following steps: Sa. Initialize the value of c to 1.

[0053] Sb. traverses the historical monitoring data of device En, and for each record in the historical monitoring data, searches for a record that meets a specific condition.

[0054] Sc. If no record satisfying the specific condition is found, the value of coefficient c is increased by 1, and Sb is executed until a specified number of records R satisfying the specific condition are found or the coefficient c reaches a preset maximum value c_max.

[0055] By dynamically adjusting the coefficient c, the width of the external temperature reference interval SA and the current load reference interval SC_n can be flexibly adjusted. If no suitable record is found under the initial value of c, the range of the reference interval is expanded by gradually increasing c to ensure that qualified historical records are found within the appropriate range, effectively improving the flexibility and accuracy of anomaly detection.

[0056] For example, k21 and k31 are both 3, k2 and k3 are also 3. In the first round of traversal, the external temperature reference interval SA=[30-3,30+3]=[27,33], and the current load reference interval SC_n is: SC_n=[50-3,50+3]=[47,53]. If records that meet specific conditions are found, then in the second round of traversal, the external temperature reference interval SA=[30-6,30+6]=[30,36], and the current load reference interval SC_n is: SC_n=[50-6,50+6]=[44,56], thereby ensuring that the most similar historical monitoring data can be found to improve the accuracy of abnormal warning.

[0057] In S800, constructing the device temperature reference interval ST2_n based on the record R includes: calculating the average temperature of a specified number of device temperatures TE_n in the record R as the device reference temperature RT2_n; constructing the device temperature reference interval ST2_n based on the device reference temperature RT2_n, ST2_n is: ST2_n=[RT2_n-k4,RT2_n+k4], where k4 represents a parameter.

[0058] By calculating the average temperature of the equipment in the historical record R, a new temperature reference interval ST2_n is constructed. k4 determines the width of the reference interval, which can more accurately reflect the normal operating temperature range of the equipment, and then construct a more representative reference interval, thereby improving the accuracy of abnormal judgment.

[0059] In the process of S100-S800, if the device temperature TE_nt is greater than the preset device temperature threshold TE_nmax or the current load CE_nt is greater than the preset current load CE_nmax, an abnormal warning is issued. When the temperature or current load exceeds the preset maximum value, the system will directly issue an abnormal warning to remind maintenance personnel to check the equipment operation status in time. By setting clear temperature and current load thresholds, a warning can be issued in time when the equipment operation exceeds the safe range. Relying on the judgment of historical data, this method can directly respond to extreme situations and avoid monitoring omissions caused by improper setting of the benchmark interval in S100-S800, which improves the real-time and reliability of the distribution cabinet operation status monitoring.

[0060] Embodiment 2: Figure 3 As shown, this embodiment provides an intelligent monitoring system for the operating status of a distribution cabinet, which adopts the intelligent monitoring method for the operating status of a distribution cabinet in Embodiment 1, and includes: a monitoring time point determination module, a monitoring data acquisition module, a benchmark parameter determination module and an abnormality judgment module.

[0061] The monitoring time point determination module is used to determine a specified number of time points t within a day; the monitoring data acquisition module is used to obtain real-time monitoring data and historical monitoring data, and the monitoring data include: external temperature AE_t of the distribution cabinet, equipment temperature TE_nt of equipment En, and current load CE_nt of equipment En; wherein, the external temperature AE is the air temperature of the environment where the distribution cabinet is located, En represents the nth equipment in the distribution cabinet, TE_nt represents the temperature of the nth equipment at time point t, and CE_nt represents the current load of the nth equipment at time point t; the benchmark parameter determination module is used to determine the equipment benchmark temperature RT1_nt, external benchmark temperature RA_t, and benchmark current load RC_nt based on historical monitoring data; wherein RT1_nt represents the benchmark temperature of the nth equipment at time point t, RA_t represents the external benchmark temperature of the distribution cabinet at time point t, and RC_nt represents the benchmark current load of the nth equipment at time point t; construct based on the equipment benchmark temperature RT1_nt Equipment temperature reference interval ST1_nt; wherein ST1_nt represents the temperature reference interval of the nth equipment at time point t; the abnormal judgment module is used to judge whether the equipment temperature TE_nt of the equipment En in the real-time monitoring data is within the range of the equipment temperature reference interval ST_nt; if the equipment temperature TE_nt is not within the range of the temperature reference interval ST_nt, then calculate the difference D_0 between the equipment temperature TE_nt and the equipment reference temperature RT1_nt, the difference D_1 between the external temperature AE_t and the external reference temperature RA_t, and the difference D_2 between the current load CE_nt and the reference current load RC_nt; judge whether the difference D_0, the difference D_1 and the difference D_3 meet one of the following two conditions a1 and b1: a1: the difference D_0 is a positive value, and the difference D_1 and the difference D_2 are not positive values; b1: the difference D_0 is a negative value, and the difference D_1 and the difference D_2 are not negative values; if one of the two conditions a and b is met, an abnormal warning is issued.

[0062] This embodiment has all the advantages of the method for intelligently monitoring the operating status of a distribution cabinet in the first embodiment, and can automatically implement all the steps of the method for intelligently monitoring the operating status of a distribution cabinet.

[0063] Embodiment 3: This embodiment provides an intelligent monitoring device for the operating status of a distribution cabinet, which includes: a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the intelligent monitoring method for the operating status of the distribution cabinet in Embodiment 1.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Claims

1. A method for intelligently monitoring the operating status of a power distribution cabinet, characterized in that: It includes the following steps: Determine a specified number of time points t in a day; Acquire real-time monitoring data and historical monitoring data, the monitoring data including: external temperature AE_t of the power distribution cabinet, device temperature TE_nt of device En, and current load CE_nt of device En; wherein, external temperature AE is the air temperature of the environment where the power distribution cabinet is located, En represents the nth device in the power distribution cabinet, TE_nt represents the temperature of the nth device at time point t, and CE_nt represents the current load of the nth device at time point t; Determine the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt based on historical monitoring data; wherein RT1_nt represents the reference temperature of the nth equipment at time point t, RA_t represents the external reference temperature of the power distribution cabinet at time point t, and RC_nt represents the reference current load of the nth equipment at time point t; Constructing a device temperature reference interval ST1_nt based on the device reference temperature RT1_nt; wherein ST1_nt represents the temperature reference interval of the nth device at time point t; Determine whether the device temperature TE_nt of the device En in the real-time monitoring data is within the range of the device temperature reference interval ST_nt; If the device temperature TE_nt is not within the temperature reference interval ST_nt, the difference D_0 between the device temperature TE_nt and the device reference temperature RT1_nt, the difference D_1 between the external temperature AE_t and the external reference temperature RA_t, and the difference D_2 between the current load CE_nt and the reference current load RC_nt are calculated; Determine whether the difference D_0, difference D_1 and difference D_3 meet one of the following two conditions a1 and b1: a1: The difference D_0 is positive, and the difference D_1 and the difference D_2 are not positive; b1: The difference D_0 is negative, and the difference D_1 and the difference D_2 are not negative; If one of the two conditions a and b is met, an abnormal warning will be issued.

2. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 1, characterized in that: If one of the two conditions a and b is not met, then an external temperature reference interval SA and a current load reference interval SC_n are constructed based on the real-time monitoring data; wherein SC_n represents the current load reference interval of the nth device; Find records R that meet specific conditions from the historical monitoring data of equipment En. The specific conditions are: a2. The external temperature AE is within the external temperature reference interval SA; b2. The current load CE_n is within the current load reference interval SC_n; Constructing a device temperature reference interval ST2_n based on the record R; It is determined whether the device temperature TE_nt in the real-time monitoring data is within the device temperature reference interval ST2_n. If not, an abnormal warning is issued.

3. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 1, characterized in that: Determining the device reference temperature RT1_nt, the external reference temperature RA_t, and the reference current load RC_nt of each device based on historical monitoring data includes: Determine the current date; The historical monitoring data of the day before the current date is selected as the equipment reference temperature RT1_nt, the external reference temperature RA_t, and the reference current load RC_nt.

4. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 3, characterized in that: The equipment temperature reference interval ST1_nt is: ST1_nt=[RT1_nt-k1,RT1_nt+k1], where k1 represents a preset parameter.

5. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 2, characterized in that: The external temperature reference interval SA is: SA=[AE_t-k2,AE_t+k2]; where k2 represents a parameter; The current load reference interval SC_n is: SC_n=[CE_nt-k3,CE_nt+k3]; where k3 represents a parameter.

6. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 5, characterized in that: k2=c*k21, k3=c*k31; where k21 and k31 represent preset parameters, and c represents the coefficient; Searching for records R that meet specific conditions from the historical monitoring data of equipment En includes: Sa. Initialize the value of c to 1; Sb. traverse the historical monitoring data of device En, and for each record in the historical monitoring data, find the record that meets the specific conditions; Sc. If no record satisfying the specific condition is found, the value of coefficient c is increased by 1, and Sb is executed until a specified number of records R satisfying the specific condition are found or the coefficient c reaches a preset maximum value c_max.

7. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 5, characterized in that: Constructing the device temperature reference interval ST2_n based on the record R includes: Calculate the average temperature of the specified number of device temperatures TE_n in the record R as the device reference temperature RT2_n; The device temperature reference interval ST2_n is constructed based on the device reference temperature RT2_n, where ST2_n is: ST2_n=[RT2_n-k4,RT2_n+k4], where k4 represents a parameter.

8. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 1, characterized in that: If the device temperature TE_nt is greater than a preset device temperature threshold TE_nmax or the current load CE_nt is greater than a preset current load CE_nmax, an abnormality warning is issued.

9. The intelligent monitoring method for the operation status of a power distribution cabinet according to claim 1, characterized in that: Starting from 00:00 midnight, a time point t is recorded every half hour until 23:

30.

10. An intelligent monitoring system for the operation status of a power distribution cabinet, which adopts the intelligent monitoring method for the operation status of a power distribution cabinet according to any one of claims 1 to 9, comprising: Monitoring time point determination module: it is used to determine a specified number of time points t in a day; Monitoring data acquisition module: It is used to obtain real-time monitoring data and historical monitoring data, and the monitoring data includes: external temperature AE_t of the distribution cabinet, device temperature TE_nt of device En, and current load CE_nt of device En; wherein the external temperature AE is the air temperature of the environment where the distribution cabinet is located, En represents the nth device in the distribution cabinet, TE_nt represents the temperature of the nth device at time point t, and CE_nt represents the current load of the nth device at time point t; Benchmark parameter determination module: it is used to determine the equipment benchmark temperature RT1_nt, external benchmark temperature RA_t, and benchmark current load RC_nt based on historical monitoring data; wherein RT1_nt represents the benchmark temperature of the nth equipment at time point t, RA_t represents the external benchmark temperature of the power distribution cabinet at time point t, and RC_nt represents the benchmark current load of the nth equipment at time point t; construct the equipment temperature benchmark interval ST1_nt based on the equipment benchmark temperature RT1_nt; wherein ST1_nt represents the temperature benchmark interval of the nth equipment at time point t; Abnormal judgment module: It is used to judge whether the device temperature TE_nt of the device En in the real-time monitoring data is within the range of the device temperature reference interval ST_nt; if the device temperature TE_nt is not within the range of the temperature reference interval ST_nt, then calculate the difference D_0 between the device temperature TE_nt and the device reference temperature RT1_nt, the difference D_1 between the external temperature AE_t and the external reference temperature RA_t, and the difference D_2 between the current load CE_nt and the reference current load RC_nt; judge whether the difference D_0, the difference D_1 and the difference D_3 meet one of the following two conditions a1 and b1: a1: the difference D_0 is a positive value, and the difference D_1 and the difference D_2 are not positive values; b1: the difference D_0 is a negative value, and the difference D_1 and the difference D_2 are not negative values; if one of the two conditions a and b is met, an abnormal warning is issued.

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