CN112257923B

CN112257923B - Heavy overload early warning method and device and electronic equipment

Info

Publication number: CN112257923B
Application number: CN202011133583.7A
Authority: CN
Inventors: 孔庆泽; 张士然; 庞博; 张利锋; 刘海军; 王海燕; 迟承哲; 和娟; 李岩; 李莉; 张添洋
Original assignee: State Grid Corp of China SGCC; Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
Priority date: 2020-10-21
Filing date: 2020-10-21
Publication date: 2023-02-03
Anticipated expiration: 2040-10-21
Also published as: CN112257923A

Abstract

In the overload warning method provided by the embodiment of the application, because the overload warning model screens the electric quantity influence factor vectors included in the first data feature set and the second data feature set based on the weights corresponding to the electric quantity influence factor vectors, the correlation coefficients corresponding to at least one group of the first set, the combined influence degree corresponding to at least one group of the first set, and the first probability distribution corresponding to the electric quantity influence factor vectors, the electric quantity influence factor vectors are all electric quantity influence factor vectors with a large influence degree on overload of the first area, the overload prediction result of the first area obtained based on the third data feature set is more accurate, and overload warning of the transformer area is realized.

Description

Heavy overload early warning method and device and electronic equipment

Technical Field

The application relates to the field of power distribution systems, in particular to a heavy overload early warning method and device and electronic equipment.

Background

In an electric power system, a distribution room refers to a power supply area of a power supply device, and a distribution room includes a plurality of loads and the power supply device for supplying power to the plurality of loads, and the operating state of the power supply device directly affects the power supply quality of the distribution room to the plurality of loads. Heavy load operation or overload operation of the power supply equipment is one of main reasons for causing power failure in a transformer area, so that not only is the safety of power utilization influenced, but also the loss of the power supply equipment is accelerated, and the service life of the power supply equipment is shortened.

At present, the processing of the heavy load operation or the overload operation of the transformer area still stays at the post-processing stage, namely, the data acquisition and analysis are carried out on the transformer area with the heavy load operation or the overload operation, the reason for the heavy load operation or the overload operation of the transformer area is determined, and the heavy overload early warning of the transformer area cannot be realized.

Disclosure of Invention

In view of this, the present application provides a heavy overload early warning method, a heavy overload early warning device, and an electronic device, so as to implement heavy overload early warning for a distribution room.

The application provides the following technical scheme:

a heavy overload early warning method comprises the following steps:

acquiring a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period, wherein the first time period is a time period which is earlier than the current time and takes the current time as an ending time, the second time period is a time period which is later than the current time and takes the current time as an initial time, and the first data feature set and the second data feature set both comprise a plurality of electric quantity influence factor vectors;

acquiring weights corresponding to the electric quantity influence factor vectors respectively;

determining first attributes corresponding to the electric quantity influence factor vectors respectively to obtain a plurality of first attributes, wherein the first attributes represent attribute information corresponding to the electric quantity influence factor vectors;

obtaining correlation coefficients corresponding to at least one group of first sets, wherein the first sets comprise any two different first attributes in the first attributes, and the two first attributes contained in the different first sets are not completely the same;

acquiring joint influence degrees corresponding to at least one group of first sets, wherein the joint influence degree corresponding to one first set characterizes electric quantity output change of power supply equipment for supplying power to each load in the first area when two different first attributes contained in the first set simultaneously act on the first area;

aiming at a plurality of electric quantity influence factor vectors belonging to the same first attribute, acquiring first probability distributions corresponding to the electric quantity influence factor vectors respectively;

inputting weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first sets, joint influence degrees corresponding to at least one group of the first sets, first probability distributions corresponding to the electric quantity influence factor vectors, the first data feature set and the second data feature set into a pre-constructed overload warning model;

obtaining a first prediction result output by the overload early warning model, wherein the first prediction result comprises the operation state of the power supply equipment in the second time period, and the operation state comprises normal operation and/or overload operation;

the first prediction result is obtained by screening the multiple electric quantity influence factor vectors contained in the first data feature set and the second data feature set to obtain a third data feature set based on weights corresponding to the multiple electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first set, joint influence degrees corresponding to at least one group of the first set, and first probability distribution corresponding to the multiple electric quantity influence factor vectors, respectively, by the heavy overload early warning model, and outputting a prediction result of the operating state of the power supply device in the second time period based on the third data feature set.

Preferably, if the operation state includes an overload operation, the first prediction result further includes a time period during which the power supply device is in the overload operation;

and/or if the operation state comprises heavy load operation, the first prediction result further comprises that the power supply equipment is in a heavy load operation time period;

and/or if the operation state comprises normal operation, the first prediction result further comprises a time period when the power supply equipment is in normal operation.

Preferably, if the operation state includes an overload operation, the first prediction result further includes a first probability that the power supply device is in the overload operation;

and/or, if the operation state includes heavy-load operation, the first prediction result further includes a second probability that the power supply device is in heavy-load operation;

and/or if the operation state comprises normal operation, the first prediction result further comprises a third probability that the power supply equipment is in normal operation.

Preferably, the method further comprises the following steps:

if the first probability is greater than or equal to a first threshold value, setting icons representing the first areas in an operation state label graph as first identifications, wherein the operation state label graph comprises identifications respectively corresponding to at least one area, and the at least one area comprises the first areas;

if the first probability is larger than or equal to a second threshold value and smaller than a first threshold value, setting an icon representing the first area in the running state marker graph as a second identifier;

and if the first probability is smaller than the second threshold value, setting an icon representing the first area in the running state label graph as a third identifier.

Preferably, the acquiring a first data feature set corresponding to a first time period of the first region and a second data feature set corresponding to a second time period includes:

acquiring weather information and time information respectively corresponding to each day in a first time period in the first area, wherein the weather information corresponding to one day comprises: at least one of the temperature of the day, the pressure of the day, the humidity and the precipitation, wherein the time information corresponding to the day includes: at least one of a date to which the day belongs, a month to which the day belongs, a week corresponding to the day, whether the day is a holiday and a season to which the day belongs;

acquiring the load quantity corresponding to each day of at least one load type and the power consumption ratio corresponding to each day of the at least one load type in the first time period, wherein the loads belonging to the at least one load type are all located in the first area;

acquiring operation parameters of the power supply equipment corresponding to each day in the first time period, wherein the operation parameters corresponding to each day comprise at least one of overload times, overload duration, average load rate and maximum load rate;

respectively taking the temperature, the humidity, the air pressure, the precipitation, the date, the month, the week, the holiday, the season, the load quantity respectively corresponding to each day of the at least one load type, the power consumption duty ratio respectively corresponding to each day of the at least one load type, the overload times, the overload duration, the average load rate and the maximum load rate respectively corresponding to each day of the power supply equipment as the electric quantity influence factor vector in the first data feature set so as to obtain the first data feature set;

acquiring weather information and date information respectively corresponding to each day in the second time period in the first area;

and respectively taking the temperature, the humidity, the air pressure, the precipitation, the date, the month, the week, the holiday and the season corresponding to each day in the second time period as the electric quantity influence factor vector in the second data feature set so as to obtain the second data feature set.

Preferably, the method further comprises the following steps:

acquiring a plurality of historical data feature sets corresponding to the first area, wherein one historical data feature set comprises a sixth data feature set corresponding to a third time period of the first area and a fourth data feature set corresponding to a fourth time period, the third time period is a time period which is earlier than a preset historical time and takes the preset historical time as a termination time, the fourth time period is earlier than the historical time at the earliest time and earlier than the current time at the latest time, and the sixth data feature set and the fourth data feature set both comprise a plurality of sample electric quantity influence factor vectors;

performing the following for each of the historical data feature sets:

obtaining weights corresponding to the sample electric quantity influence factor vectors respectively;

determining second attributes corresponding to the plurality of sample electric quantity influence factor vectors respectively to obtain a plurality of second attributes, wherein the second attributes represent attribute information corresponding to the sample electric quantity influence factor vectors;

obtaining correlation coefficients corresponding to at least one group of second sets, wherein the second sets comprise any two different second attributes in the second attributes, and the two second attributes contained in the different second sets are not completely the same;

acquiring joint influence degrees corresponding to at least one group of the second sets, wherein the joint influence degree corresponding to one first set characterizes the electric quantity output change of power supply equipment for supplying power to each load in the first area when two different second attributes contained in the first set act on the first area simultaneously;

aiming at a plurality of electric quantity influence factor vectors belonging to the same first attribute, obtaining second probability distributions corresponding to the electric quantity influence factor vectors respectively;

inputting weights corresponding to the sample electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of second sets, joint influence degrees corresponding to at least one group of second sets, second probability distributions corresponding to the electric quantity influence factor vectors and the historical data feature set into a machine learning model;

obtaining second prediction results corresponding to the historical data feature sets output by the machine learning model to obtain second prediction results corresponding to a plurality of historical data feature sets respectively, wherein one second prediction result comprises the operation state of the power supply equipment in a fourth time period, and the operation state comprises normal operation and/or overload operation;

and the second prediction result is a prediction result of the machine learning model on the basis of weights corresponding to the plurality of sample electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the second sets, combined influence degrees corresponding to at least one group of the second sets and second probability distributions corresponding to the plurality of electric quantity influence factor vectors, the historical data feature set is screened to obtain a fifth data feature set, and the prediction result of the operation state of the power supply equipment in the fourth time period is output on the basis of the fifth data feature set.

For each second prediction result, comparing the second prediction result with the actual operation state of the power supply equipment in a corresponding fourth time period to obtain a comparison result so as to obtain comparison results corresponding to a plurality of second prediction results respectively;

training the machine learning model based on a plurality of comparison results to obtain the heavy overload early warning model.

In a preferred embodiment of the method of the invention,

if the operation state in the second prediction result is overload operation, the second prediction result further comprises a first prediction time period when the power supply equipment is in overload operation; the actual operating state further comprises a first real time period during which the power supply apparatus is in overload operation for the fourth time period;

the comparing the second prediction result with the actual operating state of the power supply device in the corresponding fourth time period, and obtaining the comparison result comprises:

comparing the first predicted time period with the first real time period to obtain the comparison result;

and/or the presence of a gas in the gas,

if the operation state in the second prediction result is the heavy-load operation, the second prediction result further comprises a second prediction time period when the power supply equipment is in the heavy-load operation; the actual operating state further comprises a second real time period during which the power sourcing equipment is in heavy-duty operation during the fourth time period;

comparing the second predicted time period with the second real time period to obtain the comparison result;

and/or the presence of a gas in the atmosphere,

if the operation state in the second prediction result is normal operation, the second prediction result further comprises a third prediction time period when the power supply equipment is in normal operation; the actual operating state further includes a third real time period during which the power supply apparatus is in normal operation for the fourth time period;

comparing the third predicted time period with the third real time period to obtain the comparison result.

Preferably, if the operation state in the second prediction result is overload operation, the second prediction result further includes a first prediction probability that the power supply device is in overload operation, and the actual operation state includes a first true probability that the power supply device is in overload operation in the fourth time period;

the comparing the second prediction result with the actual operating state of the power supply equipment in the corresponding fourth time period, and obtaining the comparison result comprises:

comparing the first prediction probability with the first true probability to obtain a comparison result;

and/or the presence of a gas in the atmosphere,

if the operation state in the second prediction result is the heavy-load operation, the second prediction result further includes a second prediction probability that the power supply equipment is in the heavy-load operation, and the actual operation state includes a second real probability that the power supply equipment is in the heavy-load operation in the fourth time period;

comparing the second prediction probability with the second true probability to obtain a comparison result;

and/or the presence of a gas in the atmosphere,

if the operation state in the second prediction result is normal operation, the second prediction result further includes a third prediction probability that the power supply equipment is in normal operation, and the actual operation state includes a third true probability that the power supply equipment is in heavy-load operation in the fourth time period;

and comparing the third prediction probability with the third true probability to obtain the comparison result.

A heavy overload warning device, comprising:

the first acquisition module is used for acquiring a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period;

the first time period is a time period which is earlier than the current time and takes the current time as the termination time, the second time period is a time period which is later than the current time and takes the current time as the starting time, and the first data feature set and the second data feature set both comprise a plurality of electric quantity influence factor vectors;

the second obtaining module is used for obtaining weights corresponding to the electric quantity influence factor vectors respectively;

the first determining module is used for determining first attributes corresponding to the electric quantity influence factor vectors respectively to obtain a plurality of first attributes, and the first attributes represent attribute information corresponding to the electric quantity influence factor vectors;

a third obtaining module, configured to obtain correlation coefficients corresponding to at least one group of first sets, where each first set includes any two different first attributes of the multiple first attributes, and the two first attributes included in different first sets are not completely the same;

a fourth obtaining module, configured to obtain a joint influence degree corresponding to at least one group of the first sets, where the joint influence degree corresponding to one first set characterizes an electric quantity output change of a power supply device that supplies power to each load located in the first area when two different first attributes included in the first set simultaneously act on the first area;

a fifth obtaining module, configured to obtain, for multiple electric quantity influence factor vectors that belong to a same first attribute, first probability distributions corresponding to the multiple electric quantity influence factor vectors, respectively;

a first input module, configured to input weights corresponding to the multiple electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first sets, joint influence degrees corresponding to at least one group of the first sets, first probability distributions corresponding to the multiple electric quantity influence factor vectors, the first data feature set, and the second data feature set to a pre-constructed overload warning model;

a sixth obtaining module, configured to obtain a first prediction result output by the heavy overload early warning model, where the first prediction result includes an operation state of the power supply device in the first time period, and the operation state includes normal operation and/or overload operation and/or heavy load operation;

the first prediction result is that the overload warning model screens the electric quantity influence factor vectors contained in the first data feature set and the second data feature set to obtain a third data feature set based on weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first set, joint influence degrees corresponding to at least one group of the first set, and first probability distribution corresponding to the electric quantity influence factor vectors, and outputs a prediction result of the operating state of the power supply device in the second time period based on the third data feature set.

An electronic device, comprising:

a memory for storing a program;

a processor configured to execute the program, the program specifically configured to:

acquiring first probability distributions corresponding to the electric quantity influence factor vectors aiming at the electric quantity influence factor vectors belonging to the same first attribute;

obtaining a first prediction result output by the heavy overload early warning model, wherein the first prediction result comprises the operation state of the power supply equipment in the second time period, and the operation state comprises normal operation and/or overload operation and/or heavy load operation;

According to the technical scheme, the overload warning method, the overload warning device and the electronic equipment, a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period are obtained, wherein the first time period is a historical time period, the second time period is a time period to be predicted, and the first data feature set and the second data feature set respectively comprise a plurality of electric quantity influence factor vectors. And analyzing each electric quantity influence factor vector to determine the weight corresponding to each electric quantity influence factor vector, wherein the weight represents the influence degree of the electric quantity influence factor vector on the heavy overload of the first area. And determining first attributes corresponding to the electric quantity influence factor vectors respectively, wherein the first attributes represent attribute information corresponding to the electric quantity influence factor vectors. And obtaining a correlation coefficient between the two different first attributes, and if the correlation coefficient is greater than a preset threshold value, regarding the two first attributes as linear correlation, namely, replacing the electric quantity influence factor vector in one of the first attributes with the electric quantity influence factor vector in the other first attribute. And acquiring the combined influence degree corresponding to any two first attributes, wherein the combined influence degree represents the electric quantity output change of the power supply equipment for supplying power to each load in the first area when two different first attributes act on the first area simultaneously. In practical application, the weights corresponding to the two electric quantity influence factor vectors are possibly smaller, but the two electric quantity influence factor vectors belong to different first attributes, so that the joint influence of the two electric quantity influence factor vectors on the heavy overload of the first area is larger, and therefore the joint influence degree of the different first attributes on the heavy overload needs to be considered during screening. The method includes the steps that first probability distribution corresponding to a plurality of electric quantity influence factor vectors is obtained for the plurality of electric quantity influence factor vectors belonging to the same first attribute, the probability distribution of one electric quantity influence factor vector represents the dispersion of the electric quantity influence factor vector, if the electric quantity influence factor vector is too concentrated in a certain threshold range, the electric quantity influence factor vector changes slightly, and the reference significance for predicting heavy overload of a first area is small.

And inputting weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first sets, joint influence degrees corresponding to at least one group of the first sets, first probability distributions corresponding to the electric quantity influence factor vectors, the first data characteristic set and the second data characteristic set into a pre-constructed overload early warning model to obtain a first prediction result of the operation state of the power supply equipment in a second time period, wherein the first prediction result is output by the overload early warning model.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a schematic flowchart of a heavy overload warning method according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of one implementation of a first set of data features and a second set of data features provided by an embodiment of the present application;

fig. 3 is a flowchart of a process for constructing a heavy overload early warning model according to an embodiment of the present disclosure;

fig. 4 is a structural diagram of a heavy overload early warning device according to an embodiment of the present application;

fig. 5 is a block diagram of an implementation manner of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The embodiment of the application provides a heavy overload early warning method and device and electronic equipment. Before describing the technical solution provided by the embodiment of the present application in detail, an application scenario related to the embodiment of the present application is briefly described here.

In the power system, a station area refers to a power supply area of one power supply apparatus, and one station area includes a plurality of loads and the power supply apparatus that supplies power to the plurality of loads. In the embodiment of the present application, a technical solution is described by referring to any one station area as a first area.

The operating state of the power supply equipment located in the first area directly affects the quality of the power supply equipment to the plurality of loads located in the first area. The operating state of the power supply device can be divided into: heavy load operation, overload operation, and normal operation.

The following description will be made by taking examples of heavy load operation, overload operation, and normal operation.

For example, in the embodiment of the present application, if the load rate of the power supply device reaches 80% for 2 hours or more continuously, it is determined that the power supply device is in heavy load operation; if the load rate of the power supply equipment reaches 100% after 2 hours and more, determining that the power supply equipment is in overload operation; and if the power supply equipment is in other states except heavy load operation and overload operation, determining that the power supply equipment is in normal operation.

It can be understood that the power supply device in the first area is in heavy load operation or overload operation for a long time, which not only affects the safety of power utilization, but also accelerates the loss of the power supply device, and reduces the service life of the power supply device, so how to realize the early warning of heavy overload in the transformer area becomes the problem that the power system needs to be solved urgently.

Based on the above reasons, the embodiment of the application provides a heavy overload early warning method and device, an electronic device and a storage medium. In the method, a plurality of electric quantity influence factor vectors in a first data feature set and a plurality of electric quantity influence factor vectors in a second data feature set are screened based on weights corresponding to the electric quantity influence factor vectors respectively, correlation coefficients corresponding to at least one group of first sets, joint influence degree corresponding to at least one group of first sets and probability distribution corresponding to the electric quantity influence factor vectors respectively, and the electric quantity influence factor vectors in an obtained third data feature set are electric quantity influence factor vectors with large influence degree on the heavy overload of a first area, so that the heavy overload prediction result of the first area obtained based on the third data feature set is more accurate, and the heavy overload early warning of a transformer area is realized.

The heavy overload early warning method provided by the embodiment of the application is explained in detail below.

Fig. 1 is a schematic flow chart of a heavy overload warning method according to an embodiment of the present application. The method includes the following steps S101 to S108 in implementation.

Step S101: and acquiring a first data feature set corresponding to a first time period of the first region and a second data feature set corresponding to a second time period.

Illustratively, the first time period is a time period which is earlier than the current time and takes the current time as the ending time, and the second time period is a time period which is later than the current time and takes the current time as the starting time.

For example, the first and second sets of data features each include a plurality of vectors of electrical quantity impact factors

Illustratively, one of the electric quantity influence factor vectors is an influence factor that influences a power supply device located in the first area to supply power to a plurality of loads.

For example, the dimensions of the different electricity-affecting factor vectors may be the same, e.g., the electricity-affecting shadow vector of the lower dimension may be filled with element 0, so that the electricity-affecting shadow vector of the lower dimension can reach the dimension of the higher dimension.

For example, the dimensions of different charge impact factor vectors may be different.

The first time period, the second time period, and the electric quantity influence factor vector are described in the following with specific examples.

The first time period is a historical time period and the second time period is a predicted time period. For example, the first time period may be a 7 day period earlier than the current time, and the second time period may be a3 day period later than the current time.

The plurality of electric quantity influence factor vectors included in the first data feature set corresponding to the first time period may be electric quantity influence factor vectors corresponding to each day of the 7 days, and the plurality of electric quantity influence factor vectors included in the second data feature set corresponding to the second time period may be electric quantity influence factor vectors corresponding to each day of the 3 days in the future. The plurality of electric quantity influence factor vectors contained in the second data feature set corresponding to the second time period can be weather information corresponding to each day in the future 3 days, such as one or more of temperature, air pressure, precipitation and humidity, and time information, such as one or more of date, month and whether the day is a holiday.

Illustratively, the first time period may include a current time. For example, if the current time is T days, the first time period may be (T days, T-1 days, T-2 days, T-3 days, T-4 days, T-5 days, and T-6 days), and the second time period may be (T +1 days, T +2 days, T +3 days). Correspondingly, the first data feature set is a collection of a plurality of electric quantity influence factor vectors corresponding to days T, days T-1, days T-2, days T-3, days T-4, days T-5 and days T-6 respectively, and the second data feature set is a collection of a plurality of electric quantity influence factor vectors corresponding to days T +1, days T +2 and days T +3 respectively.

Step S102: and acquiring weights corresponding to the electric quantity influence factor vectors respectively.

For example, different power influence factor vectors may affect the power supply device to supply power to multiple loads to different extents. And the weight corresponding to one electric quantity influence factor vector represents the influence degree of the electric quantity influence factor vector on the power supply of the power supply equipment to a plurality of loads.

For example, random forest algorithm may be used to calculate the weights corresponding to the electric quantity influence factor vectors.

For example, the greater the influence degree of one of the electric quantity influence factor vectors on the power supply of the power supply device to the plurality of loads, the greater the corresponding weight thereof.

For example, the weights corresponding to the electric quantity influence factor vectors are all any values greater than or equal to 0 and less than or equal to 1.

Illustratively, the sum of the weights respectively corresponding to a plurality of the electric quantity influence factor vectors is equal to 1.

Step S103: and determining first attributes corresponding to the electric quantity influence factor vectors respectively.

And the first attribute represents attribute information corresponding to the electric quantity influence factor vector.

For example, the first attributes of the electric quantity influence factor vector T daily overload times, T-1 daily overload times and T-2 daily overload times are 'overload times'; for the electric quantity influence factor vectors of T day temperature, T-1 day temperature and T +1 day temperature, the first attributes are 'temperature'

Step S104: and acquiring at least one group of correlation coefficients corresponding to the first set.

The first set comprises any two different first attributes of the first attributes, and the two first attributes contained in different first sets are not completely the same.

Exemplarily, if the number of the first attributes corresponding to the plurality of electric quantity influence factor vectors is 4, the method includes: first attribute 1, first attribute 2, first attribute 3, and first attribute 4.

The first set includes: { first attribute 1, first attribute 2}, { first attribute 2, first attribute 3}, { first attribute 3, first attribute 4}, { first attribute 1, first attribute 3}, { first attribute 1, first attribute 4}, and { first attribute 2, first attribute 4}.

Illustratively, the correlation coefficient characterizes a degree of correlation between two electric quantity influence factor vectors in the first set.

For example, the following may be based on a preset formula:

and calculating correlation coefficients of the two electric quantity influence factor vectors in the first set.

Where ρ is _X,Y Representing the correlation coefficient of the electric quantity influence factor vector X and the electric quantity influence factor vector Y, COV (X, Y) representing the covariance of the electric quantity influence factor vector X and the electric quantity influence factor vector Y, sigma _X Representing the mean value, σ, of the vector X of the electric quantity influence factors _Y Representing the average of the charge impact factor vector Y.

Illustratively, the correlation coefficient ρ _XY Taking the value between-1 and 1, p _XY When =0, X and Y are said to be irrelevant; | ρ _XY When | =1, X and Y are called to be completely related, and at this time, a linear functional relationship exists between X and Y; l ρ _XY |<1, the variation of X causes a partial variation of Y, rho _XY The larger the absolute value of (c), the larger the variation of Y caused by the variation of X, and | ρ _XY |>The first value is called the height correlation, when | ρ _XY |<The second value is called low correlation and other times moderate correlation.

The first value is greater than the second value, for example, the first value =0.8, the second value =0.3 is only an example, and 0.8 and 0.3 are only examples, which is not limited in this embodiment of the application.

For example, in the embodiment of the present application, if the correlation coefficient ρ is _XY And representing that X and Y are completely or highly correlated, replacing the electric quantity influence factor vector Y with the electric quantity influence factor vector X, or replacing the electric quantity influence factor vector X with the electric quantity influence factor vector Y to reduce the operationAnd (5) calculating.

The operation process comprises a training process of training to obtain a heavy overload early warning model; and a calculation process of obtaining a first prediction result by the heavy overload early warning model based on the second data feature set.

Step S105: and acquiring the combined influence degree corresponding to at least one group of the first set.

And the joint influence degree corresponding to one first set characterizes the change of the electric quantity output of the power supply equipment for supplying power to each load in the first area when two different first attributes contained in the first set simultaneously act on the first area.

For example, in practical applications, the electric quantity influence factor vectors of two different first attributes may have a larger weight, but have a smaller combined influence on the heavy overload of the first area.

For example, in practical application, the weights corresponding to the electric quantity influence factor vectors with two different first attributes may be different greatly, but the two vectors have a smaller or larger combined influence on the heavy overload of the first area.

For example, in practical applications, the electric quantity influence factor vectors of two different first attributes may have a smaller weight, but the two electric quantity influence factor vectors belong to different first attributes, and thus have a larger combined influence on the heavy overload of the first area.

Therefore, the combined influence degree of the different first attributes on the overload needs to be considered when screening.

The following describes the combined influence degree by taking two different first attributes, namely humidity and air pressure as an example. If the weight of the electric quantity influence factor vector is smaller than the third value, it is assumed that the electric quantity influence factor vector has a smaller influence on the power supply of the power supply device to the plurality of loads. The following description will take an example in which the joint influence is between 0 and 1, and the humidity and the air pressure are both 1 × 1 dimensional vectors, and the third value is 0.5.

For example, if the weight corresponding to the T-day humidity is 0.2 and the weight corresponding to the T-day air pressure is 0.3, the T-day humidity and the T-day air pressure are both less than 0.5, and it can be seen that the T-day humidity and the T-day humidity have a small influence on the power supply of the power supply device to the plurality of loads. If the calculated joint influence degree of the humidity and the air pressure on the power supply of the power supply equipment to the multiple loads is greater than or equal to 0.5, it is indicated that the joint influence of the first attributes with different humidity and air pressure on the power supply of the power supply equipment to the multiple loads is large, and therefore the T-day humidity and the T-day humidity need to be screened into the third data feature set when screening the electric quantity influence factor vectors.

Step S106: and acquiring first probability distribution corresponding to the electric quantity influence factor vectors aiming at the electric quantity influence factor vectors belonging to the same first attribute.

For example, if the dimension of the electric quantity influence factor vector is a1 × 1-dimensional vector, the first probability distribution corresponding to the electric quantity influence factor vector is a 0-1 distribution.

For example, if the dimension of the electric quantity influence factor vector is a multidimensional vector, a first probability distribution of an electric quantity influence factor vector represents dispersion of all elements included in the electric quantity influence factor vector, and if the all elements included in the electric quantity influence factor vector are too concentrated in a certain threshold range, it indicates that the electric quantity influence factor vector changes less, and the reference meaning for predicting the operating state of the power supply device located in the first area is less.

In the following, the power influence factor vector is taken as a temperature vector as an example, and the probability distribution corresponding to the power influence factor vector is introduced.

For example, the temperature is an important influence factor that influences the power supply of the power supply device on the load power supply, that is, when the temperature is used as the electric quantity influence factor vector, the corresponding weight is larger.

The higher the temperature in summer, the longer the air conditioner (a kind of load) located in the first area is operated, and the power supply amount of the power supply equipment may be greatly increased. And in spring, due to proper temperature, the air conditioner in the first area is greatly shortened in running time and even does not run, so that the power supply amount of the power supply equipment is greatly reduced.

However, for a short period of time, the temperature may not vary much, for example, the temperature may remain between 20 degrees and 22 degrees for less than a week, and thus the reference of the temperature to predict the heavy overload of the first area is less significant, and thus, if predicted for a short period of time, for example, three days into the future, the temperature vector may not be considered, i.e., the temperature vector may not be screened into the third data feature set.

Illustratively, if the prediction is made over a long period of time, such as one month in the future, the temperature vector may need to be considered, i.e., the temperature vector may be screened into the third data feature set.

For example, different thresholds may be set for weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first sets, joint influence degrees corresponding to at least one group of the first sets, and probability distributions corresponding to a plurality of electric quantity influence factor vectors, respectively, so as to determine a second data feature set from the first data feature set.

For example, the first threshold value corresponding to the weight is 0.5, the second threshold value corresponding to the correlation system is 0.8, the third threshold value corresponding to the joint influence degree is 0.5, and the fourth threshold value corresponding to the first probability distribution is 0.9.

Step S107: and inputting weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of first sets, joint influence degrees corresponding to at least one group of first sets, probability distribution corresponding to the electric quantity influence factor vectors, the first data feature set and the second data feature set to a pre-constructed overload early warning model.

Step S108: and obtaining a first prediction result output by the heavy overload early warning model.

Wherein the first prediction result is an operation state of the power supply device in a first time period and in a second time period, and the operation state includes: normal operation and/or overload operation and/or heavy-duty operation.

For example, if the current power supply power of the power supply device is greater than or equal to 80% of the rated power supply of the power supply device and less than or equal to the rated power supply of the power supply device, it is determined that the power supply device is in the overload operation, if the current power supply power of the power supply device is greater than the rated power supply of the power supply device, it is determined that the power supply device is in the overload operation, and if the current power supply power of the power supply device is less than 80% of the rated power supply of the power supply device, it is determined that the power supply device is in the normal operation.

For example, the overload warning model may set different thresholds for weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first sets, joint influence degrees corresponding to at least one group of the first sets, and probability distributions corresponding to a plurality of electric quantity influence factor vectors, so as to determine a third data feature set from the first data feature set and the second data feature set.

For example, the first threshold corresponding to the weight is 0.5, the second threshold corresponding to the correlation system is 0.8, the third threshold corresponding to the joint influence degree is 0.5, and the fourth threshold corresponding to the first probability distribution is 0.9.

According to the technical scheme, a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period are obtained, wherein the first time period is a historical time period, the second time period is a time period to be predicted, and the first data feature set and the second data feature set both comprise a plurality of electric quantity influence factor vectors. And analyzing each electric quantity influence factor vector to determine the weight corresponding to each electric quantity influence factor vector, wherein the weight represents the influence degree of the electric quantity influence factor vector on the heavy overload of the first area. And determining first attributes corresponding to the electric quantity influence factor vectors respectively, wherein the first attributes represent attribute information corresponding to the electric quantity influence factor vectors. And obtaining a correlation coefficient between the two different first attributes, and if the correlation coefficient is greater than a preset threshold value, regarding the two first attributes as linear correlation, namely, replacing the electric quantity influence factor vector in one of the first attributes with the electric quantity influence factor vector in the other first attribute. And acquiring the combined influence degree corresponding to any two first attributes, wherein the combined influence degree represents the electric quantity output change of the power supply equipment for supplying power to each load in the first area when two different first attributes act on the first area simultaneously. In practical application, the weights corresponding to the two electric quantity influence factor vectors are possibly smaller, but the two electric quantity influence factor vectors belong to different first attributes, so that the joint influence of the two electric quantity influence factor vectors on the heavy overload of the first area is larger, and therefore the joint influence degree of the different first attributes on the heavy overload needs to be considered during screening. The method includes the steps that first probability distribution corresponding to a plurality of electric quantity influence factor vectors is obtained for the plurality of electric quantity influence factor vectors belonging to the same first attribute, the probability distribution of one electric quantity influence factor vector represents the dispersion of the electric quantity influence factor vector, if the electric quantity influence factor vector is too concentrated in a certain threshold range, the electric quantity influence factor vector changes slightly, and the reference significance for predicting heavy overload of a first area is small.

In summary, in the overload warning method provided by the embodiment of the present application, because the overload warning model screens a plurality of electric quantity influence factor vectors included in the first data feature set and the second data feature set based on weights corresponding to the plurality of electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first set, combined influence degrees corresponding to at least one group of the first set, and first probability distributions corresponding to the plurality of electric quantity influence factor vectors, respectively, a result of the screening that the plurality of electric quantity influence factor vectors in the third data feature set are electric quantity influence factor vectors having a large influence degree on overload of the first area is obtained, and thus, a overload prediction result of the first area obtained based on the third data feature set is more accurate, thereby implementing overload warning on the transformer area.

In an alternative embodiment, in order to ascertain the specific operating state of the first region in the first time interval of the individual time intervals of the power supply system, the power supply system of the first region is adjusted in a targeted manner in the following. The embodiment of the application provides an implementation mode for obtaining a first prediction result output by a heavy overload early warning model.

The implementation mode comprises the following steps: step A1 to step A3.

Step A1: if the first prediction result represents that the power supply equipment is in overload operation in a second time period, the first prediction result further comprises the time period that the power supply equipment is in overload operation.

And/or the presence of a gas in the gas,

step A2, if the first prediction result represents that the power supply equipment has heavy-load operation in a second time period, the first prediction result also comprises the time period that the power supply equipment is in heavy-load operation;

and/or the presence of a gas in the gas,

step A3: if the first prediction result represents that the power supply equipment normally operates in a second time period, the first prediction result further comprises the time period that the power supply equipment normally operates.

Taking the specific example as an example, the first predicted result is shown below.

The following is the operation state of the power supply apparatus in each time period in a day in the first prediction result, for example, 0:00 to 6:00, the running state of the power supply equipment is normal running; 6:00 to 12:00, the running state of the power supply equipment is heavy-load running; 12:00 to 22:00, the running state of the power supply equipment is overload running; 22:00 to 24:00, the running state of the power supply equipment is normal running. The first prediction result includes time periods corresponding to the operation states, that is, the normal operation time of the power supply device is 0:00 to 6:00, and 22:00 to 24:00, the time length of normal operation is 8 hours; the moment of heavy load operation of the power supply equipment is 6:00 to 12:00, the time length of heavy-load operation is 6 hours; the moment of overload operation of the power supply equipment is 12:00 to 22:00, the time length of overload operation is 10 hours.

In the embodiment of the present application, the specific operation state of the power supply device in the first area in each time period in the second time period can be grasped based on the prediction result, and thus the power supply device in the first area can be adjusted in a targeted manner in each time period.

In an optional embodiment, in order to implement accurate warning of heavy overload of each area in the first area, the embodiment of the present application provides another implementation manner of obtaining a prediction result output by the heavy overload warning model.

The implementation mode comprises the following steps:

step B1: if the first prediction result represents that the power supply equipment has overload operation in a second time period, the first prediction result further comprises a second probability that the power supply equipment is in overload operation.

And/or the presence of a gas in the gas,

and step B2: if the prediction result represents that the power supply equipment has heavy-load operation in a second time period, the first prediction result further comprises a second probability that the power supply equipment is in a heavy-load state;

and/or the presence of a gas in the gas,

and step B3: if the prediction result indicates that the power supply equipment normally operates in the second time period, the first prediction result further comprises a third probability that the power supply equipment is in a normal state.

In the embodiment of the present application, the first probability represents a probability that the power supply device is in overload allowance in the second time period, the second probability represents a probability that the power supply device is in overload operation in the second time period, and the third probability represents a probability that the power supply device is in normal operation in the second time period. If the first probability is higher, the probability that the first area is overloaded in the first time period is higher, so that accurate early warning of heavy overload of each area in the first area is realized.

In an alternative embodiment, the power supply unit in the overload state is monitored because the overload state exceeds the rated power supply of the power supply unit. Therefore, the embodiment of the present application provides a method for marking a power supply device in an overload state, so as to monitor the power supply device in the overload state in the following. The method comprises the following steps:

and C1, if the first probability is greater than or equal to a first threshold value, setting an icon representing the first area in the running state marker graph as a first identifier.

And C2: and if the first probability is greater than or equal to a second threshold and smaller than a first threshold, setting an icon representing the first area in the running state marker graph as a second identifier.

And C3: and if the first probability is smaller than the second threshold value, setting an icon representing the first area in the running state label graph as a third identifier.

Illustratively, the operation state label graph comprises at least one identification corresponding to each of the at least one region, and the at least one region comprises the first region.

Illustratively, the first threshold is greater than the second threshold, e.g., the first threshold is 80% and the second threshold is 60%.

That is, if the first probability is equal to or greater than 80%, the first area is a high risk area, if the first probability is equal to or greater than 60% and less than 80%, the first area is a medium risk area, and if the first probability is less than 60%, the first area is a low risk area.

For example, the identity of the first zone device may be different based on the risk level of the first zone. For example, if the first area is a high risk area, a first flag is set for the first area, if the first area is a medium risk area, a second flag is set for the first area, and if the first area is a low risk area, a third flag is set for the first area.

For example, the first mark, the second mark and the third mark may be marks of different shapes or different colors.

For example, the first mark is red, the second mark is yellow, and the third mark is green.

For example, the first identifier, the second identifier and the third identifier may be displayed in a blinking manner.

The color and shape of the first mark, the second mark and the third mark are not limited in the embodiments of the present application, and the first mark, the second mark and the third mark may be different shapes and different color combinations.

In an optional embodiment, the present application discloses an implementation manner for acquiring a first data feature set and a second data feature set.

Fig. 2 is a flowchart of an implementation manner of obtaining the first data feature set and the second data feature set according to an embodiment of the present application. The implementation mode comprises the following steps: step S201 to step S206.

Step S201: and acquiring weather information and time information respectively corresponding to each day in a first time period in the first area.

The weather information corresponding to a day includes: at least one of the temperature of the day, the pressure of the day, the humidity and the precipitation, wherein the time information corresponding to the day includes: the day belongs to at least one of the date, the month, the week, whether the day is a holiday and the season.

Step S202: and acquiring the load quantity corresponding to each day of at least one load type in the first time period and the power consumption ratio corresponding to each day of the at least one load type.

One of the load types corresponds to at least one load located in the first area.

For example, the load types may be divided based on different angles. For example, if the load type is a power consumption type, the load type may be divided into a high power consumption load, a medium power consumption load, and a low power consumption load; if the load type is an industry type, the load type can be divided into a production type power load, a living type power load and a service type power load; if the load type is the city type, the load type can be divided into a first-level city power load, a second-level city power load and a third-level city user load.

Step S203: and acquiring the operating parameters of the power supply equipment respectively corresponding to each day in the first time period.

The operation parameters corresponding to one day comprise at least one of overload times, overload duration, average load rate and maximum load rate.

Step S204: and respectively taking the temperature, the humidity, the air pressure, the precipitation, the date, the month, the week, the holiday, the season, the load quantity corresponding to each day of the at least one load type, the power consumption ratio corresponding to each day of the at least one load type, the overload times, the overload duration, the average load rate and the maximum load rate corresponding to each day of the power supply equipment as the electric quantity influence factor vector in the first data feature set so as to obtain the first data feature set.

Step S205: and acquiring weather information and date information respectively corresponding to each day in the second time period in the first area.

The weather information at least includes: at least one of temperature, air pressure, humidity and precipitation, the date information at least comprises: at least one of a month, a week, a holiday, and a season.

Step S206: respectively taking the temperature, humidity, air pressure, precipitation, date, month, week, holiday and season corresponding to each day in the second time period as the electric quantity influence factor vector in the second data feature set to obtain the second data feature set

In an optional embodiment, the embodiment of the application further provides a construction method of the heavy overload early warning model.

Fig. 3 is a flowchart of a process for constructing a heavy overload early warning model according to an embodiment of the present disclosure. The process comprises the following steps: step S301 to step S310.

Step S301: and acquiring a plurality of historical data feature sets corresponding to the first area.

One of the historical data feature sets comprises a sixth data feature set corresponding to a third time period of the first area and a fourth data feature set corresponding to a fourth time period, the third time period is a time period which is earlier than a preset historical time and takes the preset historical time as an ending time, the fourth time period is earlier than the historical time at the earliest time and earlier than the current time at the latest time, and the sixth data feature set and the fourth data feature set respectively comprise a plurality of sample electric quantity influence factor vectors.

For example, in the embodiment of the present application, the elements included in the historical data feature sets corresponding to the multiple historical times respectively are the same as the elements included in the first data feature vector set corresponding to the current time.

For example, the element values corresponding to the elements in the historical data feature sets corresponding to different historical times may be different.

Illustratively, for a historical time, the third data characteristic corresponding to the historical time includes:

weather information and date information respectively corresponding to each day in a third time period, wherein the weather information at least comprises: temperature, atmospheric pressure, humidity and precipitation, the date information includes at least: month, week, holiday and season

And in the third time period, the load quantity and the power consumption ratio respectively correspond to at least one load type corresponding to the first area, and one load type corresponds to at least one load located in the first area.

And the third time period corresponds to the overload times, the overload duration, the average load rate and the maximum load rate of the power supply equipment respectively every day.

The historical time corresponding to the fourth data feature set comprises:

weather information and date information corresponding to each day in a fourth time period, wherein the weather information at least comprises: temperature, atmospheric pressure, humidity and precipitation, the date information includes at least: month, week, holiday, and season.

Performing operation S302 of steps S302 to S307 for each of the historical data feature sets: and obtaining weights corresponding to the sample electric quantity influence factor vectors respectively.

Step S303: and determining second attributes corresponding to the sample electric quantity influence factor vectors respectively.

Step S304: and acquiring at least one group of correlation coefficients corresponding to the second set.

The second set comprises any two different second attributes of the plurality of second attributes, and the two second attributes contained in different second sets are not completely the same.

Step S305: and acquiring the joint influence degree corresponding to at least one group of the second set.

And the joint influence degree corresponding to one first set characterizes the change of the electric quantity output of the power supply equipment for supplying power to each load in the first area when two different second attributes contained in the first set simultaneously act on the first area.

Step S306: and acquiring second probability distribution corresponding to the electric quantity influence factor vectors aiming at the electric quantity influence factor vectors belonging to the same first attribute.

Step S307: and inputting weights corresponding to the sample electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the second set, combined influence degrees corresponding to at least one group of the second set, second probability distribution corresponding to the electric quantity influence factor vectors and the historical data feature set into a machine learning model.

Illustratively, the machine learning model may be any one of a neural network model, a logistic regression model, a linear regression model, a light tgbm model, a Support Vector Machine (SVM), adaboost, XGboost, a Transformer-encorer model.

Illustratively, the neural network model may be any one of a cyclic neural network-based model, a convolutional neural network-based model, and a transform-encoder-based classification model.

Illustratively, the machine learning model may be a deep hybrid model of a cyclic neural network-based model, a convolutional neural network-based model, and a transform-encoder-based classification model.

Illustratively, the machine learning model may be any one of an attention-based depth model, a memory network-based depth model, and a deep learning-based short text classification model.

The short text classification model based on deep learning is a Recurrent Neural Network (RNN) or a Convolutional Neural Network (CNN) or is based on a variant of the recurrent neural network or the convolutional neural network.

Illustratively, some simple domain adaptation may be performed on an already pre-trained model to obtain a machine learning model.

Exemplary "simple domain adaptation" includes, but is not limited to, re-using large-scale unsupervised domain corpora to perform secondary pre-training on a pre-trained model, and/or performing model compression on a pre-trained model by model distillation.

Exemplary supervised learning and semi-supervised learning can be performed on the machine learning model. Semi-supervised learning is a learning method combining supervised learning and unsupervised learning. Semi-supervised learning uses large amounts of unlabeled data, and simultaneously labeled data, to perform pattern recognition operations.

Step S308: and obtaining second prediction results corresponding to the historical data feature set output by the machine learning model so as to obtain second prediction results corresponding to the multiple historical data feature sets respectively.

One of the second prediction results comprises an operation state of the power supply device during a fourth time period, wherein the operation state comprises normal operation and/or overload operation.

For example, the specific implementation process of step S301 to step S308 can refer to step S101 to step S108 in fig. 1, which is not described herein again.

Step S309: and for each second prediction result, comparing the second prediction result with the actual operation state of the power supply equipment in the corresponding fourth time period to obtain a comparison result so as to obtain comparison results corresponding to the plurality of second prediction results respectively.

Step S310: training the machine learning model based on a plurality of comparison results to obtain the heavy overload early warning model.

It can be understood that the embodiment of the present application is not limited to the heavy overload early warning model training method, and a person skilled in the art may select an appropriate training method to train to obtain the heavy overload early warning model based on the current working condition.

Illustratively, a mode of gradually iterating a plurality of electric quantity influence factor vectors is adopted to train and optimize the overload early warning model.

In an alternative embodiment, the step S309 of comparing the second prediction result with the actual operation state of the power supply device in the corresponding fourth time period, and obtaining the comparison result includes:

step D1: if the operation state in the second prediction result is overload operation, the second prediction result further includes a first prediction probability that the power supply device is in overload operation, and the actual operation state includes a first true probability that the power supply device is in overload operation in the fourth time period.

Step D2: and comparing the first prediction probability with the first real probability to obtain the comparison result.

Illustratively, the first true probability is 1 if the power supply apparatus is determined to be in an overload state for a fourth period of time, and the first true probability is 0 if the power supply apparatus is determined to be in a non-overload state for the fourth period of time.

And/or the presence of a gas in the gas,

and D3: if the operation state in the second prediction result is the heavy-load operation, the second prediction result further includes a second prediction probability that the power supply equipment is in the heavy-load operation, and the actual operation state includes a second true probability that the power supply equipment is in the heavy-load operation in the fourth time period.

Step D4: and comparing the second prediction probability with the second true probability to obtain the comparison result.

For example, if the power supply device is determined to be in the heavy-load state in the fourth time period, the second true probability is 1, and if the power supply device is determined to be in the non-heavy-load state in the fourth time period, the second true probability is 0.

And/or the presence of a gas in the gas,

step D5: if the operation state in the second prediction result is normal operation, the second prediction result further includes a third prediction probability that the power supply device is in normal operation, and the actual operation state includes a third true probability that the power supply device is in heavy-load operation in the fourth time period.

Step D6: and comparing the third prediction probability with the third true probability to obtain the comparison result.

For example, if it is determined that the power supply apparatus is in a normal state for a fourth period of time, the third true probability is 1, and if it is determined that the power supply apparatus is in an abnormal state for the fourth period of time, the third true probability is 0.

In an optional embodiment, the comparing the second prediction result with the actual operating status of the power supply device in the corresponding fourth time period in step S309, and obtaining the comparison result further includes:

step E1: if the operation state in the second prediction result is overload operation, the second prediction result further comprises a first prediction time period when the power supply equipment is in overload operation; the actual operating state further includes a first real time period during which the power supply apparatus is in overload operation during the fourth time period.

Step E2: comparing the first predicted time period with the first real time period to obtain the comparison result.

For example, the actual time and the actual duration that the power supply apparatus is in overload operation during the fourth time period may be obtained based on historical data of the power system.

And/or the presence of a gas in the gas,

step E3: if the operation state in the second prediction result is the heavy-load operation, the second prediction result further comprises a second prediction time period when the power supply equipment is in the heavy-load operation; the actual operating state further includes a second real time period during which the power supply apparatus is in heavy-duty operation during the fourth time period.

Step E4: and comparing the second prediction time period with the second real time period to obtain the comparison result.

And/or the presence of a gas in the gas,

and E5: if the operation state in the second prediction result is normal operation, the second prediction result further comprises a third prediction time period when the power supply equipment is in normal operation; the actual operating state further includes a third real time period during which the power supply apparatus is in normal operation during the fourth time period.

And E6: comparing the third predicted time period with the third real time period to obtain the comparison result.

The method is described in detail in the embodiment provided by the application, and the method can be implemented by devices in various forms, so that the application also provides a heavy overload early warning device, and specific embodiments are given below for detailed description.

In an optional embodiment, the present application provides a heavy overload early warning device. Fig. 4 is a structural diagram of a heavy overload warning apparatus according to an embodiment of the present application.

The device includes: a first obtaining module 401, a second obtaining module 402, a first determining module 403, a third obtaining module 404, a fourth obtaining module 405, a fifth obtaining module 406, a first input module 407, and a sixth obtaining module 408.

The first obtaining module 401 is configured to obtain a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period.

The first time period is a time period which is earlier than the current time and takes the current time as the termination time, the second time period is a time period which is later than the current time and takes the current time as the starting time, and the first data feature set and the second data feature set both comprise a plurality of electric quantity influence factor vectors.

A second obtaining module 402, configured to obtain weights corresponding to the electric quantity influence factor vectors respectively.

A first determining module 403, configured to determine first attributes corresponding to the electric quantity influence factor vectors, respectively, so as to obtain a plurality of first attributes, where the first attributes represent attribute information corresponding to the electric quantity influence factor vectors.

A third obtaining module 404, configured to obtain correlation coefficients corresponding to at least one group of first sets, where the first set includes any two different first attributes among the multiple first attributes, and two first attributes included in different first sets are not completely the same.

A fourth obtaining module 405, configured to obtain at least one group of joint influence degrees corresponding to the first sets, where a joint influence degree corresponding to one first set characterizes a change in electric quantity output of a power supply device that supplies power to each load located in the first area when two different first attributes included in the first set simultaneously act on the first area.

A fifth obtaining module 406, configured to obtain, for multiple electric quantity influence factor vectors belonging to the same first attribute, first probability distributions corresponding to the multiple electric quantity influence factor vectors respectively.

A first input module 407, configured to input weights corresponding to the multiple electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first sets, joint influence degrees corresponding to at least one group of the first sets, first probability distributions corresponding to the multiple electric quantity influence factor vectors, the first data feature set, and the second data feature set to a pre-constructed overload warning model.

A sixth obtaining module 408, configured to obtain a first prediction result output by the heavy overload early warning model, where the first prediction result includes an operation state of the power supply device in the first time period, and the operation state includes a normal operation and/or an overload operation and/or a heavy load operation.

It can be understood that the embodiment of the heavy overload early warning device provided in the embodiment of the present application is suitable for the embodiment of the heavy overload early warning method provided in fig. 1, and a specific implementation process of the embodiment of the device may refer to an implementation process of the embodiment of the method in fig. 1, which is not described herein again.

Illustratively, the sixth obtaining module includes:

a first obtaining unit, configured to obtain a time period during which the power supply device is in overload operation if the operation state of the first prediction result includes overload operation.

And/or the presence of a gas in the gas,

and the second obtaining unit is used for obtaining the time period of the heavy-load operation of the power supply equipment if the operation state of the first prediction result comprises the heavy-load operation.

And/or the presence of a gas in the gas,

and the third obtaining unit is used for obtaining the time and duration of the power supply equipment in the normal state if the running state of the first prediction result comprises normal running.

Illustratively, the sixth obtaining module further includes:

a fourth obtaining unit, configured to obtain a first probability that the power supply device is in an overload state if the operation state of the first prediction result includes heavy-load operation.

And/or the presence of a gas in the gas,

a fifth obtaining unit, configured to obtain a second probability that the power supply device is in a heavy load state if the operation state of the first prediction result includes heavy load operation.

And/or the presence of a gas in the gas,

a sixth obtaining unit, configured to obtain a third probability that the power supply device is in a normal state if the operation state of the first prediction result includes normal operation.

Illustratively, the first obtaining module includes:

a seventh obtaining unit, configured to obtain weather information and time information that correspond to each day in a first time period in the first area, where the weather information corresponding to a day includes: at least one of the temperature of the day, the pressure of the day, the humidity and the precipitation, wherein the time information corresponding to the day includes: the day belongs to at least one of the date, the month of the day, the week of the day, whether the day is a holiday and the season of the day.

An eighth obtaining unit, configured to obtain a load quantity corresponding to each day of at least one load type and a power consumption ratio corresponding to each day of the at least one load type in the first time period, where one load type corresponds to at least one load located in the first area.

And the ninth acquisition subunit is configured to acquire the operation parameters of the power supply device corresponding to each day in the first time period, where the operation parameters corresponding to each day include at least one of an overload frequency, a reload frequency, an overload duration, a reload duration, an average load rate, and a maximum load rate.

A first determining unit, configured to use the temperature, humidity, air pressure, precipitation, date, month, week, holiday, season corresponding to each day in the first time period, the load quantity corresponding to each day of the at least one load type, the power consumption duty ratio corresponding to each day of the at least one load type, the overload frequency corresponding to each day of the power supply device, the overload frequency, the overload time, the average load rate, and the maximum load rate respectively as the electric quantity influence factor vector in the first data feature set, so as to obtain the first data feature set.

And the tenth acquiring subunit is configured to acquire weather information and date information that correspond to each day in the second time period in the first area, respectively.

And a second determining unit, configured to use the temperature, humidity, air pressure, precipitation, date, month, week, holiday, and season corresponding to each day in the second time period as the electric quantity influence factor vector in the second data feature set, so as to obtain the second data feature set.

Exemplarily, the heavy overload early warning device further includes: and the first construction module is used for constructing the heavy overload early warning model.

Illustratively, the first building block comprises:

the first construction unit is used for acquiring a first area and acquiring a plurality of historical data feature sets corresponding to the first area.

And the second construction unit is used for acquiring weights corresponding to the sample electric quantity influence factor vectors respectively.

And the third construction unit is used for determining second attributes corresponding to the sample electric quantity influence factor vectors respectively.

The fourth construction unit obtains at least one group of correlation coefficients corresponding to a second set, where the second set includes any two different second attributes of the plurality of second attributes, and the two second attributes included in different second sets are not completely the same.

And the fifth construction unit is configured to obtain joint influence degrees corresponding to at least one group of the second sets, where a joint influence degree corresponding to one first set characterizes a change in electric quantity output of a power supply device that supplies power to each load located in the first area when two different second attributes included in the first set simultaneously act on the first area.

And the sixth construction unit is used for acquiring second probability distributions corresponding to the electric quantity influence factor vectors aiming at the electric quantity influence factor vectors belonging to the same first attribute.

And the seventh construction unit is used for inputting the weights corresponding to the sample electric quantity influence factor vectors, the correlation coefficients corresponding to at least one group of the second sets, the combined influence degrees corresponding to at least one group of the second sets, the second probability distributions corresponding to the electric quantity influence factor vectors and the historical data feature set into a machine learning model. .

An eighth construction unit, configured to obtain second prediction results corresponding to the historical data feature sets output by the machine learning model, where one of the second prediction results includes an operation state of the power supply device in a fourth time period, and the operation state includes normal operation and/or overload operation.

And the first comparison unit is used for comparing the second prediction result with the actual operation state of the power supply equipment in the corresponding fourth time period for each second prediction result to obtain a comparison result so as to obtain a comparison result corresponding to each of the second prediction results.

And the first training unit is used for training the machine learning model based on a plurality of comparison results to obtain the heavy overload early warning model.

Illustratively, the first comparing unit includes:

the first obtaining subunit is configured to, if the operation state in the second prediction result is an overload operation, further obtain the second prediction result, where the second prediction result further includes a first prediction time period in which the power supply device is in the overload operation; the actual operating state further includes a first real time period during which the power supply apparatus is in overload operation during the fourth time period:

comparing the first predicted time period with the first real time period to obtain the comparison result.

And/or;

the second obtaining subunit is configured to, if the operation state in the second prediction result is the heavy-load operation, further include a second prediction time period during which the power supply device is in the heavy-load operation; the actual operating state further comprises a second real time period during which the power sourcing equipment is in heavy-duty operation during the fourth time period;

and comparing the second prediction time period with the second real time period to obtain the comparison result.

And/or the presence of a gas in the gas,

a third obtaining subunit, configured to, if the operation state in the second prediction result is normal operation, further include a third prediction time period in which the power supply device is in normal operation; the actual operating state further includes a third real time period during which the power supply apparatus is in normal operation during the fourth time period;

Exemplarily, the first comparing unit further includes:

a fourth obtaining subunit, where the operation status in the second prediction result is an overload operation, the second prediction result further includes a first prediction probability that the power supply apparatus is in the overload operation, and the actual operation status includes a first true probability that the power supply apparatus is in the overload operation for the fourth time period;

and comparing the first prediction probability with the first true probability to obtain a comparison result.

And/or the presence of a gas in the gas,

a fifth obtaining subunit, configured to, if the operation state in the second prediction result is heavy-load operation, further include a second prediction probability that the power supply device is in heavy-load operation, where the actual operation state includes a second true probability that the power supply device is in heavy-load operation in the fourth time period;

and comparing the second prediction probability with the second true probability to obtain the comparison result.

And/or the presence of a gas in the gas,

a sixth obtaining subunit, configured to, if the operation state in the second prediction result is normal operation, further include a third prediction probability that the power supply apparatus is in normal operation, and the actual operation state includes a third true probability that the power supply apparatus is in heavy-load operation in the fourth time period;

Exemplarily, the heavy overload early warning device further includes:

and the first marking module is used for determining that the first area is a high risk area if the first probability is greater than or equal to a first threshold value, and setting an icon representing the first area in the running state marking diagram as a first identifier.

And the second marking module is used for determining that the first area is a medium risk area if the first probability is greater than or equal to a second threshold and smaller than the first threshold, and setting an icon representing the first area in the running state marking diagram as a first identifier.

And the third marking module is used for determining that the first area is a low risk area if the first probability is smaller than the second threshold, and setting an icon representing the first area in the running state marking diagram as a first identifier.

As shown in fig. 5, which is a structural diagram of an implementation manner of an electronic device provided in an embodiment of the present application, the electronic device includes:

the memory 501 stores programs.

A processor 502 configured to execute the program, the program specifically configured to:

The processor 502 may be a central processing unit CPU or an Application Specific Integrated Circuit ASIC (Application Specific Integrated Circuit).

The electronic device may further comprise a communication interface 503 and a communication bus 504, wherein the memory 501, the processor 502 and the communication interface 503 are in communication with each other via the communication bus 504.

The embodiment of the present invention further provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps included in any one of the above embodiments of the heavy overload early warning method are implemented.

Note that the features described in the embodiments in the present specification may be replaced with or combined with each other. For the device or system type embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A heavy overload early warning method is characterized by comprising the following steps:

acquiring a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period, wherein the first time period is a time period which is earlier than the current time and takes the current time as a termination time, the second time period is a time period which is later than the current time and takes the current time as an initiation time, and the first data feature set and the second data feature set both comprise a plurality of electric quantity influence factor vectors;

inputting weights corresponding to the electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the first set, joint influence degrees corresponding to at least one group of the first set, first probability distribution corresponding to the electric quantity influence factor vectors, the first data feature set and the second data feature set into a pre-constructed overload warning model;

2. The heavy overload early warning method according to claim 1, wherein if the operation state includes overload operation, the first prediction result further includes a time period during which the power supply device is in overload operation;

and/or if the operation state comprises heavy-load operation, the first prediction result further comprises that the power supply equipment is in a heavy-load operation time period;

3. The overload warning method according to claim 1, wherein if the operation state includes overload operation, the first prediction result further includes a first probability that the power supply equipment is in overload operation;

4. The heavy overload early warning method according to claim 3, further comprising:

5. The heavy overload early warning method according to any one of claims 1 to 4, wherein the obtaining a first data feature set corresponding to a first time period of a first area and a second data feature set corresponding to a second time period comprises:

6. The heavy overload early warning method according to claim 1, further comprising:

performing the following for each of the sets of historical data features:

acquiring joint influence degrees corresponding to at least one group of the second sets, wherein the joint influence degree corresponding to one first set characterizes the electric quantity output change of power supply equipment for supplying power to each load in the first area when two different second attributes contained in the first set simultaneously act on the first area;

acquiring second probability distributions corresponding to the electric quantity influence factor vectors aiming at the electric quantity influence factor vectors belonging to the same first attribute;

the second prediction result is obtained by screening the historical data feature set to obtain a fifth data feature set based on weights corresponding to the plurality of sample electric quantity influence factor vectors, correlation coefficients corresponding to at least one group of the second sets, joint influence degrees corresponding to at least one group of the second sets, and second probability distributions corresponding to the plurality of electric quantity influence factor vectors by the machine learning model, and outputting a prediction result of the operation state of the power supply device in the fourth time period based on the fifth data feature set;

7. The heavy overload warning method according to claim 6,

and/or the presence of a gas in the gas,

and/or the presence of a gas in the atmosphere,

if the operation state in the second prediction result is normal operation, the second prediction result further comprises a third prediction time period when the power supply equipment is in normal operation; the actual operating state further includes a third real time period during which the power supply apparatus is in normal operation during the fourth time period;

8. The heavy overload early warning method according to claim 6, wherein if the operation status in the second prediction result is overload operation, the second prediction result further comprises a first prediction probability that the power supply device is in overload operation, and the actual operation status comprises a first real probability that the power supply device is in overload operation for the fourth time period;

comparing the first prediction probability and the first true probability to obtain a comparison result;

and/or the presence of a gas in the gas,

9. A heavy overload early warning device, its characterized in that includes:

the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a first data feature set corresponding to a first time period of a first region and a second data feature set corresponding to a second time period;

10. An electronic device, comprising:

a memory for storing a program;

acquiring joint influence degrees corresponding to at least one group of the first sets, wherein the joint influence degree corresponding to one first set characterizes the electric quantity output change of power supply equipment for supplying power to each load in the first area when two different first attributes contained in the first set act on the first area simultaneously;