CN114282952A - Electricity price charging abnormity identification method and device, computer equipment and storage medium - Google Patents
Electricity price charging abnormity identification method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method and a device for identifying abnormal electricity price charging, computer equipment and a storage medium. The method comprises the following steps: calculating the average value of the electricity price at the current time point according to the total electricity charge and the total electricity consumption at the current time point; judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in the preset range, judging that the electricity price charging is abnormal; when the electricity price charging is abnormal, calculating a correlation coefficient between a load curve and a standard load curve in a preset time period of each user; and when the correlation coefficient is lower than a preset value, judging that the user has abnormal electricity price charging. By adopting the method, the calculation amount during abnormal charging identification can be reduced.
Description
Technical Field
The application relates to the technical field of power grid data processing, in particular to a method and a device for identifying abnormal electricity price charging, computer equipment and a storage medium.
Background
Along with the development of society, the demand for electricity is higher and higher, and the user power consumption is paid according to the power consumption. The price of electricity is designated by relevant departments, and is determined according to the coal burning cost, the construction and operation maintenance cost and the determined profit of power generation and power supply enterprises. The existing charging method of the electricity price adopts a step charging mode for resident electricity consumption and adopts four time periods of peak and valley to formulate the charging mode of the electricity price for enterprise electricity consumption so as to achieve peak-off electricity consumption and ensure stable and smooth operation of an electric power system.
However, in the process of charging electricity, the electricity consumption data of all residents need to be counted each time, and the abnormality is checked one by one, so that the abnormality of all residents is also checked under the condition of no abnormality, and finally, the waste of computing resources is caused.
Disclosure of Invention
In view of the above, it is desirable to provide a power rate billing abnormality identification method, apparatus, computer device, and storage medium capable of reducing the amount of calculation for abnormality identification.
An electricity price charging abnormality recognition method, the method comprising:
calculating the average value of the electricity price at the current time point according to the total electricity charge and the total electricity consumption at the current time point;
judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period;
if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in the preset range, judging that the electricity price charging is abnormal;
when the electricity price charging is abnormal, calculating a correlation coefficient between a load curve and a standard load curve in a preset time period of each user;
and when the correlation coefficient is lower than a preset value, judging that the user has abnormal electricity price charging.
In one embodiment, the calculating the average value of the electricity prices at the current time point according to the total amount of the electricity charges and the total amount of the electricity consumption at the current time point includes: and calculating the ratio of the total electricity charge number of the current time point to the total electricity consumption number to obtain the electricity price average value of the current time point.
In one embodiment, the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption in one year, the electricity price mean value of each month is obtained by calculating the ratio of the total electricity fee of all users per month to the total electricity consumption in one year, and the electricity price mean value change curve is obtained by fitting according to the electricity price mean value of each month and the corresponding total electricity consumption.
In one embodiment, the calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user includes: acquiring a value of each time point of a load curve in a preset time period of each user and a value of a corresponding time point in a standard load curve to form a basic load sample and a user load sample; and (3) estimating the covariance and the standard deviation of the sample by using the Pearson algorithm on the basis load and user load samples in a user preset time period to obtain the correlation coefficient of the sample.
In one embodiment, before calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user, the method comprises the following steps: acquiring temperature change data in a preset time period; and searching a user historical load curve in a time period similar to the temperature change data, and taking the user historical load curve as a standard load curve.
In one embodiment, before calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user, the method comprises the following steps: acquiring load data of all users at each time point in a preset time period; calculating a load average value according to the load data of all users; and fitting according to the load average value of each time point in a preset time period to obtain a standard load curve.
In one embodiment, when the correlation coefficient is lower than a preset value, after it is determined that the user has an abnormal electricity price charging, the method includes: acquiring user information with abnormal electricity price charging and generating a task work order; sending the task work order to a maintenance client; the maintenance client side matches the current task work order to be processed according to the work order information processed through the history, calculates the relevant indexes, and sequences the current task work order to be processed according to the relevant indexes.
An electricity rate billing abnormality recognition apparatus, the apparatus comprising:
the electricity price mean value calculating module is used for calculating the electricity price mean value of the current time point according to the total electricity fee and the total electricity consumption of the current time point;
the judging module is used for judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period;
the abnormal existence determining module is used for judging that the abnormal electricity price charging exists if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in a preset range;
the correlation coefficient calculation module is used for calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user when the electricity price charging is abnormal;
and the abnormal user determining module is used for judging that the user has abnormal electricity price charging when the correlation coefficient is lower than a preset value.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
calculating the average value of the electricity price at the current time point according to the total electricity charge and the total electricity consumption at the current time point;
judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period;
if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in the preset range, judging that the electricity price charging is abnormal;
when the electricity price charging is abnormal, calculating a correlation coefficient between a load curve and a standard load curve in a preset time period of each user;
and when the correlation coefficient is lower than a preset value, judging that the user has abnormal electricity price charging.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
calculating the average value of the electricity price at the current time point according to the total electricity charge and the total electricity consumption at the current time point;
judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period;
if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in the preset range, judging that the electricity price charging is abnormal;
when the electricity price charging is abnormal, calculating a correlation coefficient between a load curve and a standard load curve in a preset time period of each user;
and when the correlation coefficient is lower than a preset value, judging that the user has abnormal electricity price charging.
According to the identification method, the identification device, the computer equipment and the storage medium for the abnormal electricity price charging, whether the abnormal electricity price charging exists in the user population is judged by calculating the average electricity price value of the current time point, and the abnormal electricity price charging of the single user is calculated only when the abnormal electricity price charging exists in the user population, so that the waste of calculation resources can be reduced; meanwhile, whether the electricity price charging is abnormal or not is judged by judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range, the calculation is simple, and the judgment of the electricity price charging abnormality can be realized only by comparing the prestored electricity price mean value change curve with the electricity price mean value of the current time point; in addition, when the overall abnormal condition exists, the correlation coefficient is calculated for each user through the load curve and the standard load curve in the preset time period, and the abnormal electricity price charging of the user can be accurately judged.
Drawings
Fig. 1 is a diagram of an application environment of the electricity rate charging abnormality identification method in one embodiment;
fig. 2 is a flowchart illustrating a method for identifying an abnormal electricity rate charge in one embodiment;
FIG. 3 is a graph illustrating a variation of the mean value of electricity prices in one embodiment;
fig. 4 is a block diagram showing the structure of an electricity rate charging abnormality recognition apparatus in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for identifying the abnormal electricity price charging can be applied to the application environment shown in fig. 1. Wherein the service client 102 communicates with the server 104 over a network. The server 104 calculates the average value of the electricity prices at the current time point according to the total electricity charges and the total electricity consumption at the current time point; judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period; if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in the preset range, judging that the electricity price charging is abnormal; when the electricity price charging is abnormal, calculating a correlation coefficient between a load curve and a standard load curve in a preset time period of each user; and when the correlation coefficient is lower than a preset value, judging that the user has abnormal electricity price charging. The service client 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, there is provided an abnormal electricity rate billing identification method, which is described by taking the application of the method to the server in fig. 1 as an example, and includes the following steps:
and S110, calculating the average value of the electricity price at the current time point according to the total electricity fee and the total electricity consumption at the current time point.
The current time point may be a settlement time point of the electricity fee, and the server may calculate the electricity price average value every preset time. The average value of the electricity prices at the current time point is equal to the ratio of the total amount of the electricity charges at the current time point to the total amount of the electricity used at the current time point.
S120, judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period.
Wherein, the change curve of the electricity price mean value is shown in figure 3. Because the resident carries out the ladder price of electricity now, the higher the power consumption, the higher the average value of the price of electricity is; the relation between the total power consumption and the average value of the electricity price can be counted to form a change curve of the average value of the electricity price. The preset time period can be one week, one month, one quarter or one year, and the statistics of the total electricity fee and the total electricity consumption of a period of time are counted at the time point; for example, the total electricity rate and the total electricity consumption of each day in a month of city a are counted, then the average electricity rate of each day is calculated, and an electricity rate average change curve is generated according to the relationship between the average electricity rate and the total electricity consumption of one month. In the electricity price mean value change curve, each total electricity consumption amount can determine a reference electricity price mean value; as shown in fig. 3, the average value of the electricity prices collected at the current time point is 5.7 degrees and the total electricity consumption is 6300 kw · h, which is denoted as point L1 in fig. 3, the point M1 is determined on the change curve of the average value of the electricity prices when the total electricity consumption is 6300 kw · h, and the reference average value of the electricity prices corresponding to the point M1 is 7 degrees. The electricity price average value change curve can be stored in advance.
And S130, if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in a preset range, judging that the electricity price charging is abnormal.
The preset range is determined according to the abnormal statistical condition of the power price at ordinary times, for example, the preset range can be 1 angle; of course, the predetermined range may also be a percentage of the reference electricity price average, such as 20% of the reference electricity price average. For example, as shown in fig. 3, point L1 indicates that the average value of the collected electricity prices at the current time point is 5.7 degrees and the total electricity consumption is 6300 kw, and the determined reference electricity price average value is 7 degrees, then the difference between the average value of the collected electricity prices at the current time point and the reference electricity price average value determined by the change curve of the electricity price average value is 1.3 degrees, and if the preset range is 1 degree, it is determined that there is an electricity price charging abnormality.
And S140, when the electricity price charging is abnormal, calculating a correlation coefficient between the load curve and the standard load curve in a preset time period of each user.
The preset time period can be 1 day, one week or one month, the change curve of the power consumption and the time of each sampling time point in the preset period is a load curve of the user in the preset time period, and the labeled load curve is a load curve of the user in a historical period or the change curve of the average power consumption and the time of all the users in each sampling time point in the preset period.
The correlation coefficient between the load curve and the standard load curve in the preset time period of each user is calculated by the pearson algorithm, and of course, other algorithms for calculating the similarity of the curves may be used.
S150, when the correlation coefficient is lower than the preset value, judging that the user has abnormal electricity price charging.
The preset value is determined according to the abnormal charging condition of the user. For example, if the correlation coefficient calculated by the pearson algorithm is 0.3, the preset value is 0.4, and the correlation coefficient is lower than the preset value, the user has abnormal electricity rate charging.
In the method for identifying the abnormal electricity price charging, whether the abnormal electricity price charging exists in the user population is judged by calculating the average electricity price value of the current time point, and the abnormal electricity price charging of the single user is calculated when the abnormal electricity price charging exists in the user population, so that the waste of calculation resources can be reduced; meanwhile, whether the electricity price charging is abnormal or not is judged by judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range, the calculation is simple, and the judgment of the electricity price charging abnormality can be realized only by comparing the prestored electricity price mean value change curve with the electricity price mean value of the current time point; in addition, when the overall abnormal condition exists, the correlation coefficient is calculated for each user through the load curve and the standard load curve in the preset time period, and the abnormal electricity price charging of the user can be accurately judged.
In one embodiment, the calculating the average value of the electricity prices at the current time point according to the total amount of the electricity charges and the total amount of the electricity consumption at the current time point includes: and calculating the ratio of the total electricity charge number of the current time point to the total electricity consumption number to obtain the electricity price average value of the current time point.
In one embodiment, the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption in one year, the electricity price mean value of each month is obtained by calculating the ratio of the total electricity fee of all users per month to the total electricity consumption in one year, and the electricity price mean value change curve is obtained by fitting according to the electricity price mean value of each month and the corresponding total electricity consumption.
In one embodiment, the calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user includes: acquiring a value of each time point of a load curve in a preset time period of each user and a value of a corresponding time point in a standard load curve to form a basic load sample and a user load sample; and (3) estimating the covariance and the standard deviation of the sample by using the Pearson algorithm on the basis load and user load samples in a user preset time period to obtain the correlation coefficient of the sample.
The load curve may be a curve composed of values of the electrical load per hour in 1 day, and the standard load curve may be a typical curve composed of values of the electrical load per hour in 1 day. And obtaining values of the power utilization load at the same time point of the load curve and the standard load curve in a preset time period to form a sample data pair, and estimating the covariance and the standard deviation of the sample by the sample data pair through a Pearson algorithm to obtain the correlation coefficient of the sample. The load curve may be a curve consisting of values of electrical load for each day of a month.
In one embodiment, before calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user, the method comprises the following steps: acquiring temperature change data in a preset time period; and searching a user historical load curve in a time period similar to the temperature change data, and taking the user historical load curve as a standard load curve. In the embodiment, the user historical load curve is selected as the standard load curve, and whether the user has abnormal electricity price charging can be judged according to the electricity utilization habits of the user.
In one embodiment, before calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user, the method comprises the following steps: acquiring load data of all users at each time point in a preset time period; calculating a load average value according to the load data of all users; and fitting according to the load average value of each time point in a preset time period to obtain a standard load curve. In this embodiment, the load average value of all users in the same period is selected to be fitted to obtain a standard load curve, and comparison can be performed according to the power consumption conditions of other users in the same period to determine whether the user has abnormal power rate charging.
In one embodiment, when the correlation coefficient is lower than a preset value, after it is determined that the user has an abnormal electricity price charging, the method includes: acquiring user information with abnormal electricity price charging and generating a task work order; sending the task work order to a maintenance client; the maintenance client side matches the current task work order to be processed according to the work order information processed through the history, calculates the relevant indexes, and sequences the current task work order to be processed according to the relevant indexes. In the embodiment, the maintenance client matches the task work order to be processed according to the processing condition of the local historical task work order, can match the corresponding task according to the experience of the maintenance personnel, and can improve the efficiency of processing the electricity price charging abnormity.
In one embodiment, sqoop and information are used in the server for data extraction. The method aims to assist in efficient large data communication between a Relational Database (RDBMS) and Hadoop. The user can utilize Sqoop to import the data of the relational database into a data storage component (such as Hive) in Hadoop; meanwhile, data can be extracted from the Hadoop system and exported to a relational database; related data cleaning rules and technologies such as mathematical statistics, data mining or predefining and the like are utilized in the server to convert error data and incomplete data into data meeting the application quality requirement; in a server, the HDFS is adopted to store data, and the method is mainly used for storing and inquiring full-type data (structured, semi-structured, real-time and unstructured) and is characterized by mass scale storage and quick query and reading; distributed computing, data mining and multi-dimensional analysis capabilities are mainly provided, and mainly used tools comprise: SPSS, R _ hadoop, Mahout, Hive, Spark, etc. In the server, algorithms of multiple loop iteration, such as data aggregation processing, data discretization, evolution analysis and heterogeneous analysis, are not needed for mass data, and Hadoop offline calculation is adopted. For the algorithms which need to scan data in a full amount for many times, algorithms such as association analysis, clustering and the like are completed by adopting a Spark memory computing framework. And on the client side, based on a data calculation layer, a data analysis result is displayed in a visual mode, so that the user can use the data analysis result conveniently, and related key technologies comprise technologies such as Echarts, Pentaho, JQuery, Bootstrap and the like, so that Web development is faster, and the application is simple and flexible.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In one embodiment, as shown in fig. 4, there is provided an electricity rate billing abnormality recognition apparatus including: the electricity price average value calculating module 210, the judging module 220, the abnormal existence determining module 230, the correlation coefficient calculating module 240 and the abnormal user determining module 250, wherein:
and an electricity price average value calculating module 210, configured to calculate an electricity price average value at the current time point according to the total electricity fee and the total electricity consumption at the current time point.
The judging module 220 is configured to judge whether a difference between the electricity price mean value at the current time point and a reference electricity price mean value determined by an electricity price mean value change curve is within a preset range under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period.
And an anomaly existence determining module 230, configured to determine that an electricity price charging anomaly exists if a difference between the electricity price mean value at the current time point and a reference electricity price mean value determined by the electricity price mean value change curve is not within a preset range.
And the correlation coefficient calculating module 240 is configured to calculate a correlation coefficient between the load curve and the standard load curve within a preset time period of each user when the electricity price charging is abnormal.
And an abnormal user determining module 250, configured to determine that the user has an abnormal electricity price charging when the correlation coefficient is lower than a preset value.
In one embodiment, the electricity price average calculation module 210 is further configured to calculate a ratio of the total electricity price and the total electricity consumption at the current time point, so as to obtain an electricity price average at the current time point.
In one embodiment, the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption in one year, the electricity price mean value of each month is obtained by calculating the ratio of the total electricity fee of all users per month to the total electricity consumption in one year, and the electricity price mean value change curve is obtained by fitting according to the electricity price mean value of each month and the corresponding total electricity consumption.
In one embodiment, the correlation coefficient calculation module 240 includes: the sample selecting unit is used for acquiring the value of each time point of the load curve in the preset time period of each user and the value of the corresponding time point in the standard load curve to form a basic load sample and a user load sample; and the correlation coefficient calculating unit is used for estimating the covariance and the standard deviation of the sample by the Pearson algorithm according to the basic load and the user load sample in the preset time period of the user to obtain the correlation coefficient of the sample.
In one embodiment, the device for identifying abnormal electricity rate charging further includes: the temperature change data acquisition module is used for acquiring temperature change data in a preset time period; and the standard load curve determining module is used for searching a user historical load curve in a time period similar to the temperature change data and taking the user historical load curve as a standard load curve.
In one embodiment, the device for identifying abnormal electricity rate charging further includes: the load data acquisition module is used for acquiring the load data of all users at each time point in a preset time period; the load average value calculation module is used for calculating the load average value according to the load data of all the users; and the standard load curve determining module is used for fitting according to the load average value of each time point in a preset time period to obtain a standard load curve.
In one embodiment, the device for identifying abnormal electricity rate charging further includes: the task work order generation module is used for acquiring the user information with abnormal electricity price charging and generating a task work order; the sending module is used for sending the task work order to the maintenance client; the maintenance client side matches the current task work order to be processed according to the work order information processed through the history, calculates the relevant indexes, and sequences the current task work order to be processed according to the relevant indexes.
For the specific definition of the electricity price charging abnormality identification device, reference may be made to the above definition of the electricity price charging abnormality identification method, and details are not described herein again. Each module in the above-mentioned electricity price charging abnormality recognition apparatus may be wholly or partially implemented by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing user electricity utilization data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a power rate billing abnormality identification method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An abnormal electricity price charging identification method is characterized by comprising the following steps:
calculating the average value of the electricity price at the current time point according to the total electricity charge and the total electricity consumption at the current time point;
judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period;
if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in the preset range, judging that the electricity price charging is abnormal;
when the electricity price charging is abnormal, calculating a correlation coefficient between a load curve and a standard load curve in a preset time period of each user;
and when the correlation coefficient is lower than a preset value, judging that the user has abnormal electricity price charging.
2. The method of claim 1, wherein calculating the average value of the electricity prices at the current time point according to the total amount of the electricity charges and the total amount of the electricity consumption at the current time point comprises:
and calculating the ratio of the total electricity charge number of the current time point to the total electricity consumption number to obtain the electricity price average value of the current time point.
3. The method according to claim 1, wherein the electricity price mean value change curve is a change curve between the electricity price mean value and the total amount of electricity used in one year, the electricity price mean value of each month is obtained by calculating the ratio of the total amount of electricity charges and the total amount of electricity used of all users per month in one year, and the electricity price mean value change curve is obtained by fitting the electricity price mean value of each month and the corresponding total amount of electricity used.
4. The method of claim 1, wherein calculating the correlation coefficient between the load curve and the standard load curve for each user in a preset time period comprises:
acquiring a value of each time point of a load curve in a preset time period of each user and a value of a corresponding time point in a standard load curve to form a basic load sample and a user load sample;
and (3) estimating the covariance and the standard deviation of the sample by using the Pearson algorithm on the basis load and user load samples in a user preset time period to obtain the correlation coefficient of the sample.
5. The method of claim 1, wherein before calculating the correlation coefficient between the load curve and the standard load curve for each user within a preset time period, the method comprises:
acquiring temperature change data in a preset time period;
and searching a user historical load curve in a time period similar to the temperature change data, and taking the user historical load curve as a standard load curve.
6. The method of claim 1, wherein before calculating the correlation coefficient between the load curve and the standard load curve for each user within a preset time period, the method comprises:
acquiring load data of all users at each time point in a preset time period;
calculating a load average value according to the load data of all users;
and fitting according to the load average value of each time point in a preset time period to obtain a standard load curve.
7. The method of claim 1, wherein when the correlation coefficient is lower than a preset value, after determining that the user has an abnormal electricity rate billing function, the method comprises:
acquiring user information with abnormal electricity price charging and generating a task work order;
sending the task work order to a maintenance client; the maintenance client side matches the current task work order to be processed according to the work order information processed through the history, calculates the relevant indexes, and sequences the current task work order to be processed according to the relevant indexes.
8. An electricity rate charging abnormality recognition apparatus, characterized in that the apparatus comprises:
the electricity price mean value calculating module is used for calculating the electricity price mean value of the current time point according to the total electricity fee and the total electricity consumption of the current time point;
the judging module is used for judging whether the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is within a preset range or not under the condition of the total electricity consumption; the electricity price mean value change curve is a change curve between the electricity price mean value and the total electricity consumption amount at each time point in a preset time period, and the electricity price mean value at each time point is equal to the ratio of the total electricity fee sum and the total electricity consumption amount of all users at each time point in the preset time period;
the abnormal existence determining module is used for judging that the abnormal electricity price charging exists if the difference between the electricity price mean value of the current time point and the reference electricity price mean value determined by the electricity price mean value change curve is not in a preset range;
the correlation coefficient calculation module is used for calculating the correlation coefficient between the load curve and the standard load curve in the preset time period of each user when the electricity price charging is abnormal;
and the abnormal user determining module is used for judging that the user has abnormal electricity price charging when the correlation coefficient is lower than a preset value.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN116298441A (en) * | 2023-02-28 | 2023-06-23 | 东莞市冠达自动化设备有限公司 | Outdoor power supply charge and discharge control method, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116298441A (en) * | 2023-02-28 | 2023-06-23 | 东莞市冠达自动化设备有限公司 | Outdoor power supply charge and discharge control method, equipment and storage medium |
| CN116298441B (en) * | 2023-02-28 | 2023-11-07 | 东莞市冠达自动化设备有限公司 | Outdoor power supply charge and discharge control method, equipment and storage medium |
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