Load and energy efficiency monitoring method and system for quad-in-one terminal

Abstract

The present invention relates to the field of energy efficiency monitoring technology, and in particular to a load and energy efficiency monitoring method and system for a four-in-one terminal. The method uses any historical sampling moment other than the historical sampling moment as a comparison moment at the same time point as the current sampling moment in any historical period, and obtains the correlation reference credibility between the historical sampling moment and the comparison moment based on the degree of data correlation between the historical sampling moment and the comparison moment in each historical period; obtains the correlation reference credibility between the historical sampling moment and each comparison moment, and obtains the abnormal warning indicator of the power factor at the current sampling moment in combination with the degree of data correlation between the current sampling moment and each comparison moment in each historical period, so as to perform abnormal monitoring and warning on the electrical system, and realize accurate identification and warning of power factor abnormalities under dynamic load changes.

Description

Technical Field

[0001] The present invention relates to the technical field of energy efficiency monitoring, and in particular to a load and energy efficiency monitoring method and system for a four-in-one terminal. Background Art

[0002] With the continuous development of smart grids, the distribution side has put forward higher requirements for power consumption status perception and energy efficiency management. In order to achieve comprehensive monitoring and intelligent regulation of the operating status of the power system, a four-in-one terminal with "measurable, controllable, adjustable, and visual" capabilities has come into being. This terminal integrates functions such as electrical parameter collection, edge computing, communication transmission, and visualization. It can be deployed on the user side, the substation side, and in distributed power supply access scenarios to achieve real-time collection and analysis of key electrical parameters such as current, voltage, active power, and reactive power. In actual operation, power factor is an important indicator for measuring power utilization efficiency and power quality. Its abnormal fluctuations often reflect problems such as abnormal load, insufficient reactive compensation, or aging equipment. If not intervened, it may lead to increased energy consumption, increased line losses, and even affect the safety and economy of power supply. Therefore, a load and energy efficiency monitoring method for the power grid site is constructed based on the four-in-one terminal, which dynamically calculates and identifies anomalies in the power factor. It can not only continuously monitor the load operating status without interrupting power supply, but also provide accurate data support for energy efficiency optimization, power grid scheduling and fault warning, which has significant engineering practical value.

[0003] However, the existing technology for power factor monitoring only focuses on the numerical value of the power factor obtained by real-time calculation, and lacks the ability to intelligently perceive the operating status of electrical equipment behind the power factor changes, especially in dynamic application scenarios such as load start and stop. For example: in industrial scenarios, when multiple inductive load devices (such as variable frequency motors) start, stop or switch operating modes at the same time, it will cause instantaneous fluctuations in the power factor. Such fluctuations are often normal operating phenomena. However, traditional monitoring methods generally rely on fixed thresholds for abnormal alarms, and cannot identify whether these changes are caused by normal load behavior. They lack the ability to intelligently analyze and judge the scenarios, which easily leads to false alarms or missed alarms. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a load and energy efficiency monitoring method and system for a four-in-one terminal to solve the problem that traditional monitoring methods rely on fixed thresholds for abnormal alarms and lack the ability to intelligently perceive the operating status of electrical equipment behind power factor changes.

[0005] In a first aspect, an embodiment of the present invention provides a load and energy efficiency monitoring method for a four-in-one terminal, the method comprising the following steps:

[0006] Obtain the power factor of the electrical system at the current sampling moment in the current cycle;

[0007] For any historical period, any historical sampling moment other than the historical sampling moment that is at the same time point as the current sampling moment in the historical period is used as a comparison moment. Based on the time difference, apparent power difference, and power factor difference between the historical sampling moment and the comparison moment, the degree of data correlation between the historical sampling moment and the comparison moment in the historical period is obtained;

[0008] The data correlation degrees between the historical sampling moments and the comparison moments in each historical period are combined into a data correlation degree set, and the correlation reference credibility between the historical sampling moments and the comparison moments is obtained based on the data differences in the data correlation degree set;

[0009] Obtain the correlation reference credibility between the historical sampling moment and each comparison moment, combine the data correlation degree between the current sampling moment and each comparison moment in each historical cycle, and the data correlation degree between the historical sampling moment and each comparison moment in each historical cycle, and obtain the abnormal warning index of the power factor at the current sampling moment, which is used for abnormal monitoring and warning of the electrical system.

[0010] In the second aspect, an embodiment of the present invention provides a load and energy efficiency monitoring system for four-in-one terminals, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements a load and energy efficiency monitoring method for four-in-one terminals as described in the first aspect.

[0011] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0012] When the present invention performs abnormal monitoring on the power factor at the current sampling moment in the power grid, the present invention first obtains the historical sampling moment that belongs to the same time point as the current sampling moment in the historical period, and at the same time analyzes the degree of mutual reference of data between the historical sampling moment and each other historical sampling moment (comparison moment) in the historical period to characterize the similarity of the operating status of the electrical equipment and the user behavior characteristics at these two moments. Then, combined with the power factor difference between the historical sampling moment and the comparison sampling moment, the degree of mutual reference of data between the two sampling moments and the degree of mutual correlation of power factor data are analyzed to characterize the typical user power consumption behavior characteristics, thereby analyzing the historical sampling moments in each historical period. The degree of correlation between the data at the comparison sampling moments is compared to obtain the correlation reference credibility that can reflect the law of power factor change. The greater the correlation reference credibility, the more similar the subsequent law of similar power factor change is. Then, by comparing the difference in the degree of correlation between the data at the current sampling moment and the historical sampling moment compared with the comparison moment, weighted abnormality warning processing is performed to obtain the abnormality warning index of the power factor at the current sampling moment. The greater the difference, the more abnormal the power factor at the current sampling moment. This abnormality warning method improves the intelligent perception ability of power factor monitoring for the operating status of electrical equipment, realizes accurate identification and warning of power factor abnormalities under dynamic load changes, and improves the energy efficiency utilization rate of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 This is a method flow chart of a method for load and energy efficiency monitoring of four integrated terminals provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0015] The embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0016] It should be noted that the terms "first," "second," and the like in the specification of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0017] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0018] See also Figure 1 , is a method flow chart of a method for monitoring load and energy efficiency of a four-in-one terminal provided by the first embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0019] Step S101: obtaining the power factor of the electrical system at the current sampling moment in the current cycle.

[0020] The purpose of this invention is to enhance the intelligent perception of the operating status of electrical equipment by power factor monitoring, accurately identify and warn of power factor anomalies under dynamic load changes, and improve the utilization rate of power grid energy efficiency. Therefore, in the process of monitoring the power factor, key electrical parameter data in the electrical system is used, specifically including voltage data and current data. The voltage and current data are sampled by a voltage transformer (PT) and a current transformer (CT), and synchronously measured by an acquisition terminal. The corresponding active power (P) and apparent power (S) are calculated in real time from the voltage and current data. The power factor G = P / S is then calculated based on the active power and apparent power.

[0021] It should be noted that the embodiment of the present invention takes one day as a cycle and obtains voltage data, current data, active power, apparent power and power factor at each sampling moment in a day according to the sampling frequency. The sampling frequency is not limited and can be 1 minute, 5 minutes, 10 minutes, half an hour, 1 hour, etc., which can be set according to the implementation scenario.

[0022] Power factor is an important indicator for measuring the efficiency of electric energy utilization. It represents the ratio between active power and apparent power, and its value range is 0 to 1. The closer the power factor is to 1, the higher the proportion of electric energy effectively used for work and the more efficient the system operation. Conversely, the lower the power factor, the more reactive power there is, which can easily lead to energy waste, increased line losses, and even affect the power supply quality and operational stability of the power grid. Therefore, as one of the core parameters for load and energy efficiency monitoring, power factor is of great significance in power grid energy efficiency management.

[0023] In practical applications, changes in power factor are often closely related to the operating status of electrical equipment and exhibit certain historical regularities, particularly in equipment start-up and shutdown behavior, load composition, and the cyclical nature of power usage patterns. For example, in industrial sites, the power factor of inductive loads (such as variable-frequency motors) is low during concentrated daytime operation, then gradually recovers after nighttime shutdown. Concentrated daytime operation of air conditioning in commercial buildings during summer can also cause a periodic decrease in power factor.

[0024] Based on the above analysis, by analyzing the historical power factor and load operation data, a typical "behavioral feature" can be established, and combined with the user's electricity consumption mode, a dynamic and intelligent judgment of the power factor change state can be realized, abnormal conditions beyond the normal fluctuation range can be discovered in time, and reactive power compensation measures can be implemented to effectively improve the energy efficiency of the power grid. Therefore, in the embodiment of the present invention, taking the current sampling time in the current cycle as an example, the voltage data of the electrical system at the current sampling time is first obtained in the current cycle. , current data , active power , apparent power and power factor Then, the voltage data, current data, active power, apparent power and power factor at each historical sampling moment in the five historical cycles before the current cycle are obtained. By analyzing the historical power factor and load operation data, a typical "behavioral feature" is established to determine whether the power factor at the current sampling moment is abnormal.

[0025] Step S102: For any historical period, among the historical sampling moments that belong to the same time point as the current sampling moment in any historical period, any historical sampling moment other than the historical sampling moment is used as a comparison moment. Based on the time difference, apparent power difference, and power factor difference between the historical sampling moment and the comparison moment, the degree of data correlation between the historical sampling moment and the comparison moment in any historical period is obtained.

[0026] When analyzing user electricity usage characteristics, if the time interval between any two sampling moments in the time sequence of a day (cycle) is short and the corresponding apparent power difference is small, it generally means that the types of electrical equipment operated by the user at these two sampling moments and their operating states are likely to be more similar. This increases the degree of cross-reference between such data when analyzing power factor changes. Therefore, in this embodiment of the present invention, historical sampling moments that coincide with the current sampling moment within a historical cycle are first analyzed to determine whether electricity usage behavior is consistent at the same sampling moment each day. Furthermore, the consistency of the types of electrical equipment operated and their operating states between the historical sampling moments and other sampling moments within the cycle to which they belong is analyzed to determine the rationality of power factor changes.

[0027] Taking any historical period as an example, assuming that the current sampling time If it is 12 o'clock on the current day, then 12 o'clock on the previous day is used as the historical sampling time that is the same as the current sampling time. At the same time, any historical sampling time other than 12 o'clock (for example, 13 o'clock) in the historical day is used as the comparison time m. Then, based on the time difference, apparent power difference, and power factor difference between the historical sampling time and the comparison time, the degree of data correlation between the historical sampling time and the comparison time in any historical period is obtained. The specific method is as follows:

[0028] (1) Obtaining historical sampling time The time interval between the comparison time m is recorded as the time distance For example, if there is a one-hour difference between the historical sampling time of 12 o'clock and the comparison time of 13 o'clock, the time distance between the two is ; Get historical sampling time The absolute value of the apparent power difference between the time m and the comparison time m is recorded as the power distance .

[0029] (2) The historical sampling moment The time distance and apparent power distance line between the comparison time m are inversely normalized to a unified interval, that is, mapped to the preset mapping interval, where the lower limit and upper limit of the mapping interval are set to and , 、 are set to 0.2 and 1 respectively, where and The value of is greater than 0, and Any value of can be used, which is intended to eliminate the impact of the time distance and apparent power difference between different sampling moments due to different dimensions. The value selected for the mapping interval under the above conditions has no effect on subsequent analysis and calculation.

[0030] Therefore, the distance mapping formula is used to obtain the mapping values ​​of the time distance respectively. and the mapping value of the power distance , where the distance mapping formula is:

[0031]

[0032] in, Represents the mapping value of the i-th distance, Indicates the minimum value of the preset mapping interval, Indicates the maximum value of the preset mapping interval, represents the maximum value of the i-th distance in all historical periods, represents the minimum value of the i-th distance in all historical periods, represents the i-th distance, which includes the time distance and the power distance.

[0033] Then, the product of the mapping value of the time distance and the mapping value of the power distance is used as the mutual reference degree of the data between the historical sampling moment and the comparison moment, that is, ,in, Indicates the degree of mutual reference between the historical sampling moment and the comparison moment. The larger the value of , the higher the mutual reference degree between the two sampling moments in the subsequent analysis, and the greater the mutual reference degree of the corresponding data.

[0034] (3) The higher the degree of mutual reference of data, the more similar the two sampling moments are in terms of the operating status of the electrical equipment and the user behavior characteristics, and the more likely the corresponding power factors are to be close. Therefore, the degree of mutual reference not only reflects the consistency of electricity consumption behavior, but also serves as an important basis for judging the rationality of power factor changes. Further combined with the historical sampling moments The power factor difference between the comparison time m and the historical sampling time is analyzed The possible correlation between the degree of mutual reference of data at the comparison time m and the power factor. The specific analysis method is as follows: obtaining the absolute value of the power factor difference between the historical sampling time and the comparison time, normalizing the data mutual reference degree to obtain a first normalized value, normalizing the absolute value of the power factor difference to obtain a second normalized value, obtaining the difference between a constant 1 and the second normalized value, and multiplying the difference by the first normalized value as the degree of mutual correlation of data between the historical sampling time and the comparison time within any historical period.

[0035] In one embodiment, the historical sampling time The calculation formula for the degree of correlation between the data at and the comparison time m is:

[0036]

[0037] It should be noted that Indicates the historical sampling time and the degree of correlation between the data at the comparison time m, Indicates the degree of mutual reference between the historical sampling moment and the comparison moment. Indicates the power factor at the historical sampling moment, Indicates the power factor at the time of comparison, 1 indicates a constant, || indicates the absolute value symbol, Represents the normalization function.

[0038] It should be noted that if the historical sampling time The higher the mutual reference degree of the data at the comparison moment m, the smaller the difference in the corresponding power factors between the two, which means that the correlation between the mutual reference degree of the data at the two moments and the power factor is greater, that is, The larger the value, the greater the degree of mutual reference between the data at these two moments and the possible correlation between the power factors, and the greater the degree of mutual correlation between the corresponding data.

[0039] Step S103 , the data correlation degrees between the historical sampling moments and the comparison moments in each historical period are combined into a data correlation degree set, and the correlation reference credibility between the historical sampling moments and the comparison moments is obtained based on the data differences in the data correlation degree set.

[0040] According to the historical sampling time in any of the above historical periods The method for obtaining the degree of correlation between the data at the comparison moment m is to obtain the historical sampling moments at the same time point as the current sampling moment in each historical period, and obtain the degree of correlation between the data at the historical sampling moments and the comparison moment in each historical period. Assuming that the current sampling moment If it is 12 o'clock on the same day, the data correlation degree between 12 o'clock and the comparison time 13 o'clock is obtained in each historical period, thereby forming a data correlation degree set between the historical sampling time and the comparison time, which is used to analyze the historical sampling time and the comparison moment m as the credibility of the mutual reference analysis.

[0041] Historical sampling moments The greater and more stable the correlation degree in the data correlation degree set between the current sampling moment n and the comparison moment m is, the higher the reliability of the calculation result may be when the relationship is used to select several comparison moments as mutual reference analysis for the current sampling moment n. Therefore, in the embodiment of the present invention, according to the historical sampling moment The data difference in the data correlation degree set between the comparison time m and the historical sampling time is obtained The correlation reference credibility between and comparison time m is obtained as follows:

[0042] For any data intercorrelation degree in the data intercorrelation degree set, calculating the absolute value of the difference between the any data intercorrelation degree and each other data intercorrelation degree in the data intercorrelation degree set, obtaining a mean of the absolute values ​​of the differences, recording the mean as the difference value of the any data intercorrelation degree, obtaining the difference value of each data intercorrelation degree in the data intercorrelation degree set, and obtaining a mean of the difference values;

[0043] Calculate the element mean of all elements in the data correlation degree set, obtain the reciprocal of the sum of the constant 1 and the mean of the difference values ​​as the stability index of the data correlation degree set, and use the product of the element mean and the stability index as the reference credibility of the correlation between the historical sampling moment and the comparison moment.

[0044] In one embodiment, the historical sampling time The calculation formula of the correlation reference credibility between and comparison time m is:

[0045]

[0046] in, Indicates the historical sampling time and the correlation reference credibility between the comparison time m, The number of elements in the set that represents the degree of correlation between data. Represents the rth element in the set of data correlation degrees, Represents the sth element in the set of data correlation degrees, || represents the absolute value symbol, and 1 represents a constant.

[0047] It should be noted that The element mean of all elements in the set that represents the degree of correlation between data. The larger the element mean, the higher the historical sampling time. The greater the correlation between the time and the comparison moment m, the higher the reliability when used as a reference for subsequent analysis; It indicates the mean of the absolute value of the difference between each two elements in the set of data correlation, that is, the mean of the difference value. The smaller the value, The larger the value of , the more stable the data correlation in the data correlation set is. When the analysis reference is based on the set, the greater the possibility that similar patterns will be found in the subsequent analysis. The higher the credibility of the set for subsequent reference analysis.

[0048] Step S104, obtain the correlation reference credibility between the historical sampling moment and each comparison moment, combine the data mutual correlation degree between the current sampling moment and each comparison moment in each historical period, and the data mutual correlation degree between the historical sampling moment in each historical period and each comparison moment, and obtain the abnormal warning indicator of the power factor at the current sampling moment, which is used for abnormal monitoring and warning of the electrical system.

[0049] According to the method of step S103, obtain the historical sampling time The reference credibility associated with each comparison moment, where the comparison moment refers to the time within a day excluding the historical sampling moment Other historical sampling times, assuming that the sampling unit is hourly, the current sampling time n is 12 o'clock, and the historical sampling time If it is also 12 o'clock, the comparison times are 1 o'clock, 2 o'clock, 3 o'clock, ..., 11 o'clock, 13 o'clock, ... 24 o'clock.

[0050] At the same time, according to the method for obtaining the data correlation degree in step S102 above, the data correlation degree between the current sampling moment n and each comparison moment in each historical period is obtained, which is recorded as , and then set a weight for each comparison moment according to the data mutual correlation degree between the current sampling moment and each comparison moment in each historical period, as well as the data mutual correlation degree between the historical sampling moment and each comparison moment in each historical period. The specific setting method is: for any comparison moment m, use the data mutual correlation degree acquisition method in step S102 to respectively acquire the data mutual correlation degree between the current sampling moment n and the comparison moment m in each historical period, and obtain the data mutual correlation degree mean , taking each data mutual correlation degree in the data mutual correlation degree set between the historical sampling moment and any comparison moment as the numerator, taking the sum of the mean value of the data mutual correlation degree and the preset value as the denominator, obtaining the corresponding ratio, calculating the average value of all ratios, and recording it as the power factor abnormality warning value of the current sampling moment compared with any comparison moment m.

[0051] In one embodiment, taking the comparison time m as an example, the calculation formula of the power factor abnormality warning value at the current sampling time compared with any comparison time m is:

[0052]

[0053] in, Indicates the power factor abnormal warning value at the current sampling moment compared to the comparison moment m, Indicates the historical sampling time The number of elements in the set of data correlation between the comparison time m, Represents the rth element in the set of data correlation degrees, It represents the mean value of the correlation between the data. represents the hyperparameters to ensure that the fractions make sense.

[0054] It should be noted that It is used to represent the average correlation degree between the current sampling moment n and the comparison moment m. Indicates that the current sampling time n corresponds to the same historical sampling time The average of the ratios of the mutual correlation degree of each data in the data mutual correlation degree set corresponding to the comparison time m and the average correlation degree between the current sampling time n and the comparison time m. The larger the value, the better the current sampling time n and the comparison time m combination compared with the same historical sampling time. The lower the correlation degree obtained by combining with the comparison time m, the greater the probability that it is caused by the power factor abnormality at the current sampling time n, and the greater the power factor abnormality warning value corresponding to the current sampling time compared with the comparison time m.

[0055] Similarly, the power factor abnormality warning value at the current sampling moment compared with each comparison moment is obtained, the associated reference credibility between the historical sampling moment and each comparison moment is used as the weight, all power factor abnormality warning values ​​are weighted averaged to obtain the corresponding weighted average value, the weighted average value is normalized, and the power factor abnormality warning index at the current sampling moment is obtained.

[0056] In one embodiment, the calculation formula of the abnormal warning indicator of the power factor at the current sampling moment is:

[0057]

[0058] in, Indicates the abnormal warning indicator of the power factor at the current sampling moment. represents the normalization function, Indicates the number of comparison moments, that is, the number of comparison moments in a cycle, Indicates the power factor abnormal warning value at the current sampling moment compared to the comparison moment m, Indicates the historical sampling time and comparison time The correlation reference credibility between them is also the weight.

[0059] It should be noted that when the historical sampling time and the comparison time The higher the correlation reference credibility is, the more accurate the corresponding power factor abnormal warning value is for the abnormal warning at the current sampling moment, and then through weighted processing, the final calculated power factor abnormal warning index at the current sampling moment is more accurate.

[0060] After obtaining the power factor abnormality warning indicator at the current sampling moment, the system compares it with the set abnormality warning threshold K = 0.35 (the specific value can be flexibly adjusted based on actual operating experience). If the power factor abnormality warning indicator at the current sampling moment exceeds the abnormality warning threshold, a preliminary assessment is made of the potential power factor abnormality or fault risk in the power grid. At this point, the system enters the delayed confirmation phase, continuously monitoring the warning indicator changes for a period of time (e.g., 2 minutes for a one-minute sampling interval). The system then calculates the average abnormality warning indicator over this period. If the average abnormality warning indicator remains above the abnormality warning threshold, it indicates that the abnormality is persistent and may indicate a stability problem or reactive power compensation failure in the power grid. To prevent electrical equipment overheating, reduced energy efficiency, or energy waste caused by a low power factor, the system can immediately implement early warning intervention measures, such as dynamically switching capacitor banks and activating reactive power compensation equipment, to restore the power factor to a reasonable level, thereby improving system efficiency and safety.

[0061] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a load and energy efficiency monitoring system for four integrated terminals, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned load and energy efficiency monitoring methods for four integrated terminals.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention 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. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for monitoring load and energy efficiency of a four-in-one terminal, characterized in that: The method comprises: Obtain the power factor of the electrical system at the current sampling moment in the current cycle; For any historical period, any historical sampling moment other than the historical sampling moment that is at the same time point as the current sampling moment in the historical period is used as a comparison moment. Based on the time difference, apparent power difference, and power factor difference between the historical sampling moment and the comparison moment, the degree of data correlation between the historical sampling moment and the comparison moment in the historical period is obtained; The data correlation degrees between the historical sampling moments and the comparison moments in each historical period are combined into a data correlation degree set, and the correlation reference credibility between the historical sampling moments and the comparison moments is obtained based on the data differences in the data correlation degree set; Obtain the correlation reference credibility between the historical sampling moment and each comparison moment, combine the data correlation degree between the current sampling moment and each comparison moment in each historical cycle, and the data correlation degree between the historical sampling moment and each comparison moment in each historical cycle, and obtain the abnormal warning indicator of the power factor at the current sampling moment, which is used for abnormal monitoring and warning of the electrical system; The step of obtaining the degree of data correlation between the historical sampling moments and the comparison moments in any historical period based on the time difference, apparent power difference, and power factor difference between the historical sampling moments and the comparison moments includes: Obtain the interval between the historical sampling time and the comparison time, recorded as the time distance, and obtain the absolute value of the apparent power difference between the historical sampling time and the comparison time, recorded as the power distance; Using a distance mapping formula, respectively obtaining a mapping value of the time distance and a mapping value of the power distance, and taking the product of the mapping value of the time distance and the mapping value of the power distance as the mutual reference degree of data between the historical sampling moment and the comparison moment; The distance mapping formula is: ； in, Represents the mapping value of the i-th distance, Indicates the minimum value of the preset mapping interval, Indicates the maximum value of the preset mapping interval, represents the maximum value of the i-th distance in all historical periods, represents the minimum value of the i-th distance in all historical periods, represents the i-th distance, the i-th distance including the time distance and the power distance; Obtain an absolute value of a power factor difference between a historical sampling moment and a comparison moment, perform normalization on the degree of mutual reference of the data to obtain a first normalized value, perform normalization on the absolute value of the power factor difference to obtain a second normalized value, obtain a difference between a constant 1 and the second normalized value, and use the product of the difference and the first normalized value as the degree of mutual correlation of the data between the historical sampling moment and the comparison moment in any historical period.

2. The load and energy efficiency monitoring method for a four-in-one terminal according to claim 1, characterized in that: The obtaining of the correlation reference credibility between the historical sampling moment and the comparison moment according to the data difference in the data correlation degree set includes: For any data intercorrelation degree in the data intercorrelation degree set, calculating the absolute value of the difference between the any data intercorrelation degree and each other data intercorrelation degree in the data intercorrelation degree set, obtaining a mean of the absolute values ​​of the differences, recording the mean as the difference value of the any data intercorrelation degree, obtaining the difference value of each data intercorrelation degree in the data intercorrelation degree set, and obtaining a mean of the difference values; Calculate the element mean of all elements in the data correlation degree set, obtain the reciprocal of the sum of the constant 1 and the mean of the difference values ​​as the stability index of the data correlation degree set, and use the product of the element mean and the stability index as the reference credibility of the correlation between the historical sampling moment and the comparison moment.

3. The load and energy efficiency monitoring method for a four-in-one terminal according to claim 1, characterized in that: The method for obtaining an abnormal warning indicator of the power factor at the current sampling moment includes: For any comparison moment, based on the data mutual correlation degree between the current sampling moment and any comparison moment in each historical period, a mean value of the data mutual correlation degree is obtained. Each data mutual correlation degree in the set of data mutual correlation degrees between the historical sampling moment and any comparison moment is used as a numerator, and the sum of the mean value of the data mutual correlation degree and a preset value is used as a denominator to obtain a corresponding ratio. The average value of all ratios is calculated and recorded as the power factor abnormality warning value for the current sampling moment compared to any comparison moment. Obtain the power factor abnormality warning value at the current sampling moment compared with each comparison moment, use the associated reference credibility between the historical sampling moment and each comparison moment as the weight, perform weighted averaging on all power factor abnormality warning values, obtain the corresponding weighted average value, normalize the weighted average value, and obtain the abnormal warning index of the power factor at the current sampling moment.

4. A load and energy efficiency monitoring system for a four-in-one terminal, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the load and energy efficiency monitoring method for a four-in-one terminal as described in any one of claims 1 to 3 are implemented.

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