A power distribution cabinet operation state intelligent monitoring method and system

Abstract

This application relates to the field of power distribution cabinet operation status monitoring technology, specifically disclosing an intelligent monitoring method and system for power distribution cabinet operation status. The method mainly includes: determining a specified number of time points t within a day; acquiring real-time monitoring data and historical monitoring data; determining the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt based on the historical monitoring data; constructing an equipment temperature reference interval ST1_nt based on the equipment reference temperature RT1_nt; and determining whether the equipment temperature TE_nt of equipment En in the real-time monitoring data is within the range of the equipment temperature reference interval ST_nt. This application can construct a dynamic reference temperature interval under similar conditions and monitor the changes in equipment temperature and current load in real time. Even with small changes, potential problems can be quickly detected through abnormal fluctuations, allowing for early discovery of abnormal trends in equipment operation, thereby preventing small problems from developing into major failures and improving equipment safety and stability.

Description

Technical Field

[0001] This application relates to the field of power distribution cabinet operation status monitoring technology, and in particular to a method and system for intelligent monitoring of power distribution cabinet operation status. Background Technology

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

[0003] Traditional methods for monitoring abnormalities in power distribution cabinets typically involve monitoring whether the equipment temperature reaches a preset threshold. The system only issues an alarm when the temperature exceeds this threshold. However, since the alarm is only triggered after the temperature reaches the threshold, the system often struggles to detect potential faults or abnormalities in a timely manner. This is especially true during the process of the equipment temperature rising, when potential hazards may have already emerged, making it impossible to take preventative measures in advance and further exacerbating the risk of failure. Summary of the Invention

[0004] This application provides an intelligent monitoring method and system for the operating status of power distribution cabinets. This solves the problem that in the traditional method of monitoring power distribution cabinet equipment, potential problems may have already emerged during the temperature rise of the equipment, but it is difficult to detect potential faults or abnormalities in time. This method enables early detection of abnormal trends in equipment operation, thereby preventing small problems from developing into major faults and improving the safety and stability of the equipment.

[0005] In a first aspect, this application provides an intelligent monitoring method for the operating status of a power distribution cabinet, comprising the following steps: determining a specified number of time points t within a day; acquiring real-time monitoring data and historical monitoring data, wherein the monitoring data includes: the external temperature of the power distribution cabinet AE_t, the equipment temperature TE_nt of device En, and the current load CE_nt of device En; wherein, the external temperature AE is the ambient 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; determining the equipment reference temperature RT1_nt, the external reference temperature RA_t, and the 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 power distribution cabinet at time point t, and RC_nt represents the reference current load of the nth device at time point t; and determining the equipment reference temperature RT1_nt, the external reference temperature RA_t, and the reference current load RC_nt based on the equipment reference temperature RT1_nt; and determining the equipment reference temperature RT1_nt, the external reference temperature RA_t, and the reference current load RC_nt based on the equipment reference temperature RT1_nt. T1_nt constructs the equipment temperature reference interval ST1_nt; where ST1_nt represents the temperature reference interval of the nth equipment at time point t; it determines whether the equipment temperature TE_nt of 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, it calculates 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; it then determines whether the differences D_0, D_1, and D_3 satisfy one of the following two conditions a1 and b1: a1: the difference D_0 is positive, and the differences D_1 and D_2 are not positive; b1: the difference D_0 is negative, and the differences D_1 and D_2 are not negative; if one of the two conditions a and b is satisfied, an abnormal warning is issued.

[0006] Furthermore, if either condition a or b is not met, an external temperature reference range SA and a current load reference range SC_n are constructed based on real-time monitoring data; where SC_n represents the current load reference range of the nth device; records R that meet specific conditions are searched from the historical monitoring data of device En, the specific conditions being: a2. The external temperature AE is located within the external temperature reference range SA; b2. The current load CE_n is located within the current load reference range SC_n; a device temperature reference range ST2_n is constructed based on the record R; it is determined whether the device temperature TE_nt in the real-time monitoring data is within the range of the device temperature reference range ST2_n, and if not, an abnormal warning is issued.

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

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

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

[0010] Further, k2 = c * k21, k3 = c * k31; where k21 and k31 represent preset parameters, and c represents a coefficient; finding 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, for each record in the historical monitoring data, searching for records that meet specific conditions; Sc. If no record that meets specific conditions is found, the value of coefficient c is increased by 1, and Sb is executed until a specified number of records R that meet specific conditions are found or the coefficient c reaches the preset maximum value c_max.

[0011] Further, 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, where ST2_n is: ST2_n=[RT2_n-k4,RT2_n+k4], where k4 represents a parameter.

[0012] Furthermore, if the equipment temperature TE_nt is greater than the preset equipment temperature threshold TE_nmax or the current load CE_nt is greater than the preset current load CE_nmax, an abnormal warning will be issued.

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

[0014] Secondly, this application provides an intelligent monitoring system for the operating status of a power distribution cabinet, which adopts the intelligent monitoring method for the operating status of a power distribution cabinet as described in the first aspect, and includes: a monitoring time point determination module, a monitoring data acquisition module, a reference parameter determination module, and an anomaly 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, the monitoring data including: the external temperature of the distribution cabinet AE_t, the equipment temperature of device En TE_nt, and the current load of device En CE_nt; where the external temperature AE is the ambient temperature of the distribution cabinet, 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 reference parameter determination module is used to determine the equipment reference temperature RT1_nt, the external reference temperature RA_t, and the reference current load RC_nt based on historical monitoring data; where 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 equipment reference temperature RT1_nt, the following parameters are defined: Establish a temperature reference interval ST1_nt for the equipment; where ST1_nt represents the temperature reference interval for the nth equipment at time point t; the anomaly judgment module is used to determine whether the equipment temperature TE_nt of 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; determine whether the differences D_0, D_1, and D_3 satisfy one of the following two conditions a1 and b1: a1: the difference D_0 is positive, and the differences D_1 and D_2 are not positive; b1: the difference D_0 is negative, and the differences D_1 and D_2 are not negative; if one of the two conditions a and b is satisfied, an anomaly warning is issued.

[0016] Thirdly, this application also provides an intelligent monitoring device for the operating status of a power distribution cabinet, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the intelligent monitoring method for the operating status of the power distribution cabinet described in the first aspect.

[0017] The technical solution provided in this application has at least the following technical effects or advantages:

[0018] By introducing 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 in the traditional method of monitoring distribution cabinet equipment, potential problems may have already occurred during the temperature rise of the equipment, but it is difficult to detect potential faults or abnormalities in a timely manner. This allows for the rapid detection of potential problems through abnormal fluctuations, even if the changes are small, and the early detection of abnormal trends in equipment operation. This prevents small problems from developing into major faults and improves the safety and stability of the equipment. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall process of the intelligent monitoring method for the operating status of the distribution cabinet in this application;

[0020] Figure 2 This is a flowchart illustrating the process of finding records R that meet specific conditions from the historical monitoring data of device En in this application;

[0021] Figure 3 This is a block diagram of the intelligent monitoring system for the operation status of the distribution cabinet in this application. Detailed Implementation

[0022] To address the problems mentioned in the background technology, this application introduces a device reference temperature, an external reference temperature, and a 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. This allows potential problems to be detected quickly through abnormal fluctuations, even if the changes are small.

[0023] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0024] Example 1: As Figures 1-2 As shown, this embodiment provides an intelligent monitoring method for the operating status of a power distribution cabinet, which includes the following steps: S100. Determine a specified number of time points t within a day.

[0025] In S100, starting from midnight 00:00, a time point t can be recorded every half hour until 23:30. By recording the time point t every half hour within 24 hours, the operating status of the power distribution cabinet can be monitored around the clock.

[0026] S200. Acquire real-time monitoring data and historical monitoring data. The monitoring data includes: external temperature of the distribution cabinet AE_t, equipment temperature of device En TE_nt, and current load of device En CE_nt. Wherein, external temperature AE is the ambient temperature of the distribution cabinet, En represents the nth device in the distribution cabinet, TE_nt represents the temperature of the nth device at time t, and CE_nt represents the current load of the nth device at time t.

[0027] In the S200, monitoring data is divided into real-time monitoring data and historical monitoring data. Sensors collect data at each time point t, including the external temperature AE_t, the device temperature TE_nt, and the current load CE_nt of device En. Real-time monitoring data is obtained, and historical monitoring data is simultaneously retrieved for subsequent benchmark comparisons. Since there are typically many devices within a distribution cabinet, the device temperature TE_nt and current load CE_n can be monitored separately for different devices, with monitoring performed once at each time point t. This effectively captures the temperature and current load changes of the devices throughout the day, while also effectively balancing the real-time nature of the data with the system's processing load, thus improving the overall system's operating efficiency.

[0028] S300. Determine the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt based on historical monitoring data; where RT1_nt represents the reference temperature of the nth device at time t, RA_t represents the external reference temperature of the distribution cabinet at time t, and RC_nt represents the reference current load of the nth device at time t.

[0029] 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 operate continuously to ensure a stable power supply, 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.

[0030] For example, at 10:00 AM yesterday, the temperature of equipment En was 45°C, the current load was 90A, and the ambient temperature outside the distribution cabinet was 30°C. Therefore, the reference temperature RT1_nt of the equipment was determined to be 45°C, the reference current load RC_nt was determined to be 90A, and the external reference temperature RA_t was determined to be 30°C.

[0031] S400. Construct the device temperature reference interval ST1_nt based on the device reference temperature RT1_nt; where ST1_nt represents the temperature reference interval of the nth device at time point t.

[0032] In S400, the reference range is centered on the device reference temperature RT1_nt, with k1 fluctuating up and down, thus tolerating a certain range of temperature fluctuations, 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, then the device temperature reference range ST1_nt is [45°C, 55°C].

[0033] S500. Determine 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.

[0034] In S500, the device's temperature is judged to be normal by comparing the device's real-time temperature TE_nt with the reference temperature range ST1_nt.

[0035] S600. If the equipment temperature TE_nt is not within the temperature reference range ST_nt, 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; determine whether the differences D_0, D_1, and 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.

[0036] a1: The difference D_0 is positive, while the differences D_1 and D_2 are not positive.

[0037] b1: The difference D_0 is negative, while the differences D_1 and D_2 are not negative.

[0038] If the system detects an excessive change in equipment temperature, it will enter the anomaly judgment process. If the difference values ​​D_0, D_1, and D_3 satisfy one of the two conditions a1 and b1, it indicates that the changes in external ambient temperature and current load current are insufficient to explain the changes in equipment temperature, and thus an anomaly warning will be issued.

[0039] 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, and the reference range ST1_nt is [40°C, 50°C]. However, the real-time monitoring data shows that the device temperature TE_nt is 60°C. The system detects that the device temperature is too high and enters the anomaly judgment process.

[0040] During the anomaly detection process, the real-time monitoring data shows that the device's current load CE_nt is 88A, the external reference temperature RA_t is 28°C, and the differences D_0, D_1, and D_3 are 15, -2, and -2, respectively. Since the changes in the external ambient temperature and the current load cannot explain the changes in the device temperature, an anomaly warning is issued.

[0041] In S100-S600, a reference temperature range ST1_nt is constructed based on historical data to determine whether the equipment temperature TE_nt is within this range. If the real-time equipment temperature TE_nt is not within the reference range ST_nt, the system further calculates the difference D_0 between the equipment 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 differences D_0, D_1, and D_2 satisfy condition a1 or b1, the system determines whether the equipment is malfunctioning and issues a warning. By constructing a dynamic reference temperature range under similar conditions and monitoring the changes in equipment temperature and current load in real time, even small changes can be quickly detected through abnormal fluctuations, 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.

[0042] In low-temperature environments during winter, even if the temperature of a device inside the distribution cabinet has not yet reached the preset threshold, its operating status has deviated from the normal baseline range. This technical solution can detect this abnormality in a timely manner, issue an early warning, and prevent the equipment from shutting down or being damaged.

[0043] S700. If either condition a or b is not met, then construct an external temperature reference interval SA and a current load reference interval SC_n based on real-time monitoring data; where SC_n represents the current load reference interval of the nth device; find a record R that meets specific conditions from the historical monitoring data of device En, the specific conditions being:

[0044] a2. The external temperature AE is located within the external temperature reference range SA.

[0045] b2. The current load CE_n is located within the current load reference range SC_n.

[0046] If either condition a or b is not met, it is necessary to further determine whether changes in the external ambient temperature and the current load can explain changes in the equipment temperature. By constructing an external temperature reference range SA and a current load reference range SC_n, records R of the equipment in a similar environment are found from historical monitoring data, so as to compare with real-time monitoring data and determine whether to issue an abnormal warning.

[0047] For example, if the current external temperature is 25°C, the constructed external temperature reference range SA is [23°C, 27°C], and the current load reference range SC_n of the device is [80A, 100A]. The record R that meets both conditions is found from the historical monitoring data for subsequent judgment.

[0048] S800. Construct the equipment temperature reference interval ST2_n based on the record R; determine whether the equipment temperature TE_nt in the real-time monitoring data is within the range of the equipment temperature reference interval ST2_n. If not, issue an abnormal warning.

[0049] After obtaining the record R, the temperature of the device in a similar environment can be obtained from the record R. It is then determined whether the device temperature TE_nt in the real-time monitoring data is still similar to the data in the historical monitoring records. Specifically, this is 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 range of the device temperature reference interval ST2_n, it indicates that the device temperature TE_nt in the real-time monitoring data is still significantly different from the data in the historical monitoring records. This may indicate that the device En is in an abnormal state, thus issuing an abnormal warning.

[0050] For example, if the temperature of the device in a similar environment is recorded as 52°C, 50°C, and 54°C in R, and the device temperature reference interval ST2_n is constructed based on these device temperature data as [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 very likely that the device En is in an abnormal state.

[0051] In S300, determining the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt for each device based on historical monitoring data includes: S310. Determining the current date; S320. Selecting historical monitoring data from the day before the current date as the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt.

[0052] Using the previous day's data as a benchmark, a reference temperature and reference current load range suitable for the current environment is constructed. Throughout the year, excluding days with alternating sunny and cloudy weather, the temperature difference between two adjacent days generally does not change much. By using the historical monitoring data of the previous day as the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt, this dynamic update mechanism can accurately improve the accuracy of anomaly detection and reduce the risk of misjudgment.

[0053] In S400, the equipment temperature reference range ST1_nt is: ST1_nt = [RT1_nt - k1, RT1_nt + k1], where k1 represents a preset parameter. The reference range ST1_nt is defined by the equipment's reference temperature RT1_nt and the parameter k1. The value of the k1 parameter depends on the equipment's operating characteristics and environmental conditions to ensure that temperature fluctuations are within a reasonable range.

[0054] In S700, the external temperature reference range SA is: SA=[AE_t-k2,AE_t+k2]; where k2 represents a parameter; the current load reference range SC_n is: SC_n=[CE_nt-k3,CE_nt+k3]; where k3 represents a parameter.

[0055] The external temperature reference range SA is constructed based on the real-time external temperature AE_t and parameter k2, and the current load reference range SC_n is constructed based on the real-time current load CE_nt and parameter k3. By setting the reference ranges for 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 anomaly detection capability.

[0056] In this embodiment, k2 = c * k21, k3 = c * k31; where k21 and k31 represent preset parameters, and c represents a coefficient. Meanwhile, in S700, finding a record R that meets specific conditions from the historical monitoring data of device En includes the following steps:

[0057] Sa. initializes the value of c to 1.

[0058] Sb. Iterate through the historical monitoring data of device En, and for each record in the historical monitoring data, find the record that meets the specific conditions.

[0059] Sc. If no record that meets the specific conditions is found, the value of coefficient c is increased by 1, and Sb is executed until a specified number of records R that meet the specific conditions are found or the coefficient c reaches the preset maximum value c_max.

[0060] By dynamically adjusting the coefficient c, the widths of the external temperature reference range SA and the current load reference range SC_n can be flexibly adjusted. If no suitable record is found at the initial value of c, the range of the reference range is expanded by gradually increasing c, ensuring that a historical record that meets the conditions is found within a suitable range, thus effectively improving the flexibility and accuracy of anomaly detection.

[0061] For example, if k21 and k31 are both 3, and k2 and k3 are also both 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 a record that meets specific conditions is 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]. This ensures that the most similar historical monitoring data can be found, thereby improving the accuracy of anomaly warnings.

[0062] 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, where ST2_n is: ST2_n=[RT2_n-k4,RT2_n+k4], where k4 represents a parameter.

[0063] By calculating the average device temperature in the historical records 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 device, thereby constructing a more representative reference interval and improving the accuracy of anomaly detection.

[0064] During the S100-S800 process, if the equipment temperature TE_nt exceeds the preset equipment temperature threshold TE_nmax or the current load CE_nt exceeds the preset current load CE_nmax, an abnormal warning will be issued. When the temperature or current load exceeds the preset maximum value, the system will directly issue an abnormal warning, reminding maintenance personnel to check the equipment operating status in a timely manner. By setting clear temperature and current load thresholds, warnings can be issued in a timely manner when the equipment operates outside the safe range. Relying on historical data, this method can directly respond to extreme situations, avoiding monitoring omissions caused by improper setting of the reference range in S100-S800, and improving the real-time performance and reliability of the distribution cabinet operating status monitoring.

[0065] Example 2: Figure 3 As shown, this embodiment provides an intelligent monitoring system for the operating status of a power distribution cabinet, which adopts the intelligent monitoring method for the operating status of a power distribution cabinet in Embodiment 1. It includes: a monitoring time point determination module, a monitoring data acquisition module, a reference parameter determination module, and an anomaly judgment module.

[0066] 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, including: the external temperature of the distribution cabinet AE_t, the equipment temperature of device En TE_nt, and the current load of device En CE_nt; where the external temperature AE is the ambient temperature of the distribution cabinet, 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 reference parameter determination module is used to determine the equipment reference temperature RT1_nt, the external reference temperature RA_t, and the reference current load RC_nt based on historical monitoring data; where 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 equipment reference temperature RT1_nt, a reference parameter is constructed... The equipment temperature reference range is ST1_nt; where ST1_nt represents the temperature reference range of the nth equipment at time point t. The anomaly judgment module is used to determine whether the equipment temperature TE_nt of equipment En in the real-time monitoring data is within the range of the equipment temperature reference range ST_nt. If the equipment temperature TE_nt is not within the range of the temperature reference range ST_nt, the module calculates 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. The module then determines whether the differences D_0, D_1, and D_3 satisfy one of the following two conditions a1 and b1: a1: the difference D_0 is positive, and the differences D_1 and D_2 are not positive; b1: the difference D_0 is negative, and the differences D_1 and D_2 are not negative. If either condition a or b is satisfied, an anomaly warning is issued.

[0067] This embodiment has all the advantages of the intelligent monitoring method for the operating status of the distribution cabinet in Embodiment 1, and can automate all the steps of the intelligent monitoring method for the operating status of the distribution cabinet.

[0068] Example 3: This example provides an intelligent monitoring device for the operating status of a power distribution cabinet, which includes: a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the intelligent monitoring method for the operating status of the power distribution cabinet in Example 1.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

Claims

1. A method for intelligent monitoring of the operating status of a power distribution cabinet, characterized in that, It includes the following steps: Determine a specified number of time points t within a day; Acquire real-time monitoring data and historical monitoring data. The real-time monitoring data includes: the external temperature of the distribution cabinet AE_t, the equipment temperature of device En TE_nt, and the current load of device En CE_nt; where the external temperature AE_t 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 t, and CE_nt represents the current load of the nth device at time t. Based on historical monitoring data, the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt are determined; where RT1_nt represents the reference temperature of the nth device at time t, RA_t represents the external reference temperature of the distribution cabinet at time t, and RC_nt represents the reference current load of the nth device at time t. The device temperature reference interval ST1_nt is constructed based on the device reference temperature RT1_nt; where ST1_nt represents the temperature reference interval of the nth device at time point t. Determine 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 ST1_nt; If the equipment temperature TE_nt is not within the temperature reference range ST1_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. Determine whether the differences D_0, D_1, and D_3 satisfy one of the following two conditions, a1 and b1: a1: The difference D_0 is positive, while the differences D_1 and D_2 are not positive; b1: The difference D_0 is negative, while the differences D_1 and D_2 are not negative; If either condition a1 or b1 is met, an exception warning will be issued. If either condition a1 or b1 is not met, then an external temperature reference range SA and a current load reference range SC_n are constructed based on real-time monitoring data; where SC_n represents the current load reference range of the nth device. Find records R from the historical monitoring data of device En that meet specific conditions, namely: a2. The external temperature AE_t is located within the external temperature reference range SA; b2. The current load CE_n is located within the current load reference range SC_n; Based on the recorded R, construct the device temperature reference interval ST2_n; Determine whether the device temperature TE_nt in the real-time monitoring data is within the device temperature reference range ST2_n. If not, issue an abnormal warning.

2. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 1, characterized in that, Based on historical monitoring data, the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt for each device are determined as follows: Determine the current date; Select historical monitoring data from the day before the current date as the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt.

3. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 2, characterized in that, The equipment temperature reference range ST1_nt is: ST1_nt=[RT1_nt-k1,RT1_nt+k1], where k1 represents the preset parameter.

4. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 1, characterized in that, The external temperature reference range SA is: SA=[AE_t-k2,AE_t+k2]; where k2 represents the parameter; The current load reference range SC_n is: SC_n=[CE_nt-k3,CE_nt+k3]; where k3 represents a parameter.

5. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 4, characterized in that, k2=c*k21, k3=c*k31; where k21 and k31 represent preset parameters, and c represents a coefficient; The records R that meet specific conditions from the historical monitoring data of device En include: Sa. initializes the value of c to 1; Sb. Iterate through 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 that meets the specific conditions is found, the value of coefficient c is increased by 1, and Sb is executed until a specified number of records R that meet the specific conditions are found or the coefficient c reaches the preset maximum value c_max.

6. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 4, characterized in that, The device temperature reference interval ST2_n constructed based on the recorded R includes: Calculate the average temperature of a specified number of device temperatures TE_n in record R as the device reference temperature RT2_n; Based on the equipment reference temperature RT2_n, construct the equipment temperature reference interval ST2_n, where ST2_n is: ST2_n=[RT2_n-k4,RT2_n+k4], and k4 represents the parameter.

7. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 1, characterized in that, 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 will be issued.

8. The intelligent monitoring method for the operating status of a power distribution cabinet as described in claim 1, characterized in that, Starting at midnight 00:00, a time point t is recorded every half hour until 23:

30.

9. A power distribution cabinet operation status intelligent monitoring system, which employs the power distribution cabinet operation status intelligent monitoring method as described in any one of claims 1-8, comprising: Monitoring time point determination module: It is used to determine a specified number of time points t within a day; Monitoring data acquisition module: It is used to acquire real-time monitoring data and historical monitoring data. The real-time monitoring data includes: external temperature of the distribution cabinet AE_t, equipment temperature of device En TE_nt, and current load of device En CE_nt; where external temperature AE_t 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 t, and CE_nt represents the current load of the nth device at time t. The reference parameter determination module is used to determine the equipment reference temperature RT1_nt, external reference temperature RA_t, and reference current load RC_nt based on historical monitoring data. Here, RT1_nt represents the reference temperature of the nth device at time t, RA_t represents the external reference temperature of the distribution cabinet at time t, and RC_nt represents the reference current load of the nth device at time t. Based on the equipment reference temperature RT1_nt, the module constructs the equipment temperature reference interval ST1_nt, where ST1_nt represents the temperature reference interval of the nth device at time t. Anomaly Detection Module: This module determines whether the device temperature TE_nt of device En in the real-time monitoring data is within the device temperature reference range ST1_nt. If the device temperature TE_nt is not within the temperature reference range ST1_nt, it 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. It then determines whether the differences D_0, D_1, and D_3 satisfy one of the following two conditions, a1 and b1: a1: Difference D_0 is positive, and differences D_1 and D_2 are not positive; b1: Difference D_0 is negative, and differences D_1 and D_2 are not negative. If either condition a1 or b1 is satisfied, an anomaly warning is issued. If either condition a1 or b1 is not met, then an external temperature reference range SA and a current load reference range SC_n are constructed based on real-time monitoring data; where SC_n represents the current load reference range of the nth device. Find records R from the historical monitoring data of device En that meet specific conditions, namely: a2. The external temperature AE_t is located within the external temperature reference range SA; b2. The current load CE_n is located within the current load reference range SC_n; Based on the recorded R, construct the device temperature reference interval ST2_n; Determine whether the device temperature TE_nt in the real-time monitoring data is within the device temperature reference range ST2_n. If not, issue an abnormal warning.

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