Data processing method and device

Abstract

The invention provides a data processing method and device, and relates to the technical field of data processing, and the main technical scheme comprises the steps: responding to the determination of advanced power consumption management and control of the power consumption of a power user of a new energy power supply system in a specified future time period, and collecting target data related to the power generation of the new energy power supply system in the specified future time period; cleaning the target data based on feature data used for describing power generation features of the new energy power supply system; and determining the cleaned target data as data required for carrying out advanced power utilization management and control on the power utilization of the power utilization user in the specified future time period.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a data processing method and device. Background Art

[0002] Renewable energy power supply systems such as solar energy and wind energy are widely used in production and life to supply electricity to enterprises, residents and other electricity users. The amount of electricity generated by the renewable energy power supply system depends on factors such as weather, and the power generation is unstable. Therefore, it is necessary to control the electricity consumption of electricity users in a specified future time period in advance to ensure that electricity users can use electricity normally in the specified future time period.

[0003] However, when the electricity consumption of electricity users in a specified future time period is controlled in advance, the data used is relatively messy, and the data often contains abnormal situations such as invalid data, which makes it difficult to accurately control the electricity consumption of electricity users in the specified future time period in advance. Therefore, how to process the data to more accurately control the electricity consumption of electricity users in the specified future time period in advance has become a problem that needs to be solved urgently. Summary of the invention

[0004] A technical problem to be solved by the present disclosure is: how to process data so as to more accurately control electricity consumption of electricity users in a specified future time period in advance.

[0005] To solve the above technical problems, an embodiment of the present disclosure provides a data processing method, which includes: in response to determining to conduct advance electricity consumption control on electricity users of the new energy power supply system in a specified future time period, collecting target data related to the electricity generation of the new energy power supply system in the specified future time period; based on characteristic data used to describe the electricity generation characteristics of the new energy power supply system, cleaning the target data; and determining the cleaned target data as the data required for conducting advance electricity consumption control on electricity users in the specified future time period.

[0006] In some embodiments, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. The characteristic data includes a target value range corresponding to each target parameter, and the target value range is a value range corresponding to normal power generation of the new energy power supply system. Then, based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned, including: executing separately for the parameter values ​​corresponding to each target parameter at each time point, and judging whether the parameter value corresponding to the time point is within the target value range corresponding to the target parameter, if so, retaining the parameter value corresponding to the time point, and if not, discarding the parameter value corresponding to the time point.

[0007] In some embodiments, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. The characteristic data includes a mean value range and a standard deviation value range corresponding to each target parameter, and the mean value range and the standard deviation value range are both value ranges corresponding to normal power generation of the new energy power supply system. Then, based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned, including: based on the parameter value corresponding to each target parameter at each time point, the parameter value mean and parameter value standard deviation corresponding to each target parameter are determined respectively; for each target parameter, it is executed separately to determine whether the parameter value mean and parameter value standard deviation corresponding to the target parameter are respectively within the corresponding value range and standard deviation value range. If not, an abnormal prompt is issued for the target parameter; if yes, the parameter values ​​corresponding to the target parameter at multiple time points are cleaned based on the parameter value mean and parameter value standard deviation corresponding to the target parameter.

[0008] In some embodiments, based on the parameter value mean and parameter value standard deviation corresponding to the target parameter, the parameter values ​​corresponding to the target parameter at multiple time points are cleaned, including: executing separately for the parameter value corresponding to the target parameter at each time point, and judging whether the absolute value of the difference between the parameter value corresponding to the time point and the parameter value mean is greater than three times the parameter value standard deviation, if greater, then discard the parameter value corresponding to the time point, if not greater, then retain the parameter value corresponding to the time point.

[0009] In some embodiments, before eliminating the parameter value corresponding to the time point, the data processing method provided by the embodiment of the present disclosure also includes: issuing a elimination query for the parameter value corresponding to the time point; if a confirmation elimination feedback is received for the elimination query, executing the step of eliminating the parameter value corresponding to the time point; if a rejection elimination feedback is received for the elimination query, retaining the parameter value corresponding to the time point.

[0010] In some embodiments, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. Therefore, before determining the cleaned target data as the data required for early electricity consumption management and control of electricity users in the specified future time period, the data processing method provided by the embodiment of the present disclosure also includes: for each target parameter included in the cleaned target data, detecting whether there is a first time point with missing parameter values ​​and adjacent time points on both sides having corresponding parameter values; if so, determining the first parameter value based on the parameter values ​​corresponding to the adjacent time points on both sides, and adding the first parameter value for the first time point.

[0011] In some embodiments, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. Therefore, before determining the cleaned target data as the data required for early electricity consumption management and control of electricity users in the specified future time period, the data processing method provided by the embodiment of the present disclosure also includes: for each target parameter included in the cleaned target data, detecting whether there are multiple second time points with consecutive missing parameter values; if so, based on the time points where the target parameter has a parameter value, determining the second parameter value corresponding to each second time point by using a parameter value determination model based on a time series, and adding the corresponding second parameter value for each second time point.

[0012] In some embodiments, the data processing method provided by the embodiments of the present disclosure also includes: scoring the target data to obtain a scoring score, the scoring score is used to reflect the degree of support of the target data for advance electricity consumption management and control of electricity users of the new energy power supply system in a specified future time period; if the scoring score reaches a score threshold, executing a step of cleaning the target data based on characteristic data used to describe the power generation characteristics of the new energy power supply system; if the scoring score does not reach the score threshold, issuing a prompt that the target data cannot support advance electricity consumption management and control of electricity users of the new energy power supply system in a specified future time period.

[0013] In some embodiments, the target data after the cleaning process is determined as the data required for early electricity consumption management and control of electricity users in a specified future time period, including: performing data smoothing on the target data after the cleaning process; determining the target data after the data smoothing process as the data required for early electricity consumption management and control of electricity users in a specified future time period.

[0014] In some embodiments, the target data includes at least one of the following: weather data, time data, and user behavior data, wherein the time data is used to indicate the season of the geographical location of the new energy power supply system.

[0015] In some embodiments, the new energy power supply system includes at least one of the following: a solar power supply system, a wind power supply system, a geothermal power supply system, and a tidal power supply system.

[0016] The present disclosure provides a data processing device, the data processing device comprising:

[0017] A collection module, configured to collect target data related to power generation of the new energy power supply system in the specified future time period in response to determining to perform advance power consumption control on power users of the new energy power supply system in the specified future time period;

[0018] A processing module, used for cleaning the target data based on the characteristic data used to describe the power generation characteristics of the new energy power supply system;

[0019] The determination module is used to determine the cleaned target data as the data required for early power consumption control of the power users in a specified future time period.

[0020] An embodiment of the present disclosure provides a computer-readable storage medium, wherein the storage medium includes a stored program, wherein when the program is executed, a device where the storage medium is located is controlled to execute the data processing method of the first aspect.

[0021] An embodiment of the present disclosure provides an electronic device, which includes: a memory for storing a program; and a processor, coupled to the memory, for running the program to execute the data processing method of the first aspect.

[0022] Through the above technical scheme, the data processing method and device provided by the present disclosure, in response to determining to carry out advance electricity consumption control on the electricity consumption of the electricity users of the new energy power supply system in the specified future time period, collects the target data related to the electricity generation of the new energy power supply system in the specified future time period, and then, based on the characteristic data used to describe the electricity generation characteristics of the new energy power supply system, cleans the target data. Finally, the cleaned target data is determined as the data required for the advance electricity consumption control of the electricity users in the specified future time period. It can be seen that the scheme provided by the present disclosure determines the cleaned target data as the data required for the advance electricity consumption control of the electricity users in the specified future time period. Since the cleaned target data has excluded abnormal situations, the advance electricity consumption control of the electricity users in the specified future time period based on such data can improve the accuracy of electricity control. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 is a flow chart of a data processing method disclosed in an embodiment of the present disclosure;

[0025] Figure 2 It is one of the structural schematic diagrams of a data processing device disclosed in the embodiment of the present disclosure;

[0026] Figure 3 This is the second structural schematic diagram of a data processing device disclosed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The following is a further detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present disclosure, but cannot be used to limit the scope of the present disclosure. The present disclosure can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

[0028] The present disclosure provides these embodiments to make the present disclosure thorough and complete, and to fully express the scope of the present disclosure to those skilled in the art. It should be noted that unless otherwise specifically stated, the relative arrangement of the parts and steps, the composition of the materials, the numerical expressions and the numerical values ​​set forth in these embodiments should be interpreted as being merely exemplary, and not as limiting.

[0029] It should be noted that, in the description of the present disclosure, unless otherwise specified, the meaning of "multiple" is greater than or equal to two; the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating the orientation or positional relationship, are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present disclosure. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0030] In addition, the words "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different parts. "Vertical" does not mean vertical in the strict sense, but is within the tolerance range. "Parallel" does not mean parallel in the strict sense, but is within the tolerance range. "Include" or "comprising" and similar words mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of including other elements.

[0031] It should also be noted that in the description of the present disclosure, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to the specific circumstances. When a specific device is described as being located between a first device and a second device, there may or may not be an intermediate device between the specific device and the first device or the second device.

[0032] All terms used in the present disclosure have the same meanings as those understood by those of ordinary skill in the art to which the present disclosure belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries, for example, should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0033] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0034] Currently, when conducting advance electricity consumption control on electricity users in a specified future time period, the data used often includes abnormal situations such as invalid data, resulting in low accuracy in conducting advance electricity consumption control on electricity users in a specified future time period.

[0035] After research, it was found that if after collecting the data needed for advance electricity consumption control of electricity users in a specified future time period, the data is first cleaned and then used as the data needed for advance electricity consumption control of electricity users in a specified future time period, then by conducting advance electricity consumption control of electricity users in a specified future time period based on such data, the accuracy of electricity consumption control can be improved.

[0036] Based on the above findings, the embodiments of the present disclosure specifically provide a technical solution for data processing, specifically, in response to determining to conduct advance power consumption control on power users of the new energy power supply system in a specified future time period, target data related to power generation of the new energy power supply system in the specified future time period is collected. Based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned. The cleaned target data is determined as the data required for conducting advance power consumption control on power users in the specified future time period.

[0037] The technical solution for data processing provided by the embodiments of the present disclosure can be applied to any type of new energy power supply system, so that after collecting the data required for advance electricity consumption control of electricity users of the new energy power supply system in a specified future time period, the data can be processed to be able to more accurately conduct advance electricity consumption control of electricity users in the specified future time period, thereby improving the accuracy of electricity consumption control.

[0038] Based on the technical solution for data processing provided by the embodiments of the present disclosure, the embodiments of the present disclosure specifically provide a data processing method and device. The data processing method and device provided by the embodiments of the present disclosure are specifically described below.

[0039] like Figure 1As shown, some embodiments of the present disclosure provide a data processing method, which may include the following steps 101 to 103:

[0040] 101. In response to determining to conduct advance electricity consumption control on electricity users of the new energy power supply system in a specified future time period, target data related to electricity generation of the new energy power supply system in the specified future time period is collected.

[0041] At present, some enterprises or individuals usually establish self-sustaining new energy power supply systems to power themselves through the new energy power supply systems to achieve the purpose of reducing electricity costs. However, the amount of electricity generated by the new energy power supply system depends on factors such as weather, and the power generation is unstable. Therefore, it is necessary to control the electricity consumption of electricity users in a specified future time period in advance to ensure that electricity users can use electricity normally in the specified future time period. The specific type of the new energy power supply system can be flexibly selected based on business needs, and this embodiment does not limit this. The type of new energy power supply system can be selected based on specific business needs, and this embodiment does not limit this. Exemplarily, the new energy power supply system may include, but is not limited to, at least one of the following: a solar power supply system, a wind power supply system, a geothermal power supply system, and a tidal power supply system.

[0042] The method for determining to carry out advance power consumption control on the power consumption of the power users of the new energy power supply system in the specified future time period may include the following two methods: one is, if a time period instruction is received, the time period specified by the time period instruction is determined as the specified future time period, and it is determined that the power consumption of the power users of the new energy power supply system in the specified future time period is controlled in advance. Exemplarily, if the business personnel predicts based on experience that the power supply of the new energy power supply system in a future time period will be highly unstable, they can issue a time period instruction carrying the future time period. Another is that in order to continuously ensure the normal power consumption of the power users, a preset power supply cycle is usually set. When the new energy power supply system enters a current power supply cycle, the time period corresponding to the next power supply cycle adjacent to the current power supply cycle is determined as the specified future time period, and it is determined that the power consumption of the power users of the new energy power supply system in the specified future time period is controlled in advance. Exemplarily, the preset power supply cycle is one month, and the current power supply cycle is June 2024. The time period "July 1, 2024 to July 31, 2024" corresponding to the next power supply cycle adjacent to the current power supply cycle is determined as the designated future time period, and it is determined that electricity consumption of electricity users of the new energy power supply system in the designated future time period "July 1, 2024 to July 31, 2024" will be subject to advance electricity consumption control.

[0043] After determining to implement advance electricity usage control on electricity users of the new energy power supply system in a specified future time period, in response to determining to implement advance electricity usage control on electricity users of the new energy power supply system in the specified future time period, target data related to electricity production of the new energy power supply system in the specified future time period is collected to predict the power load value that may be produced by the new energy power supply system in the specified future time period based on the target data, thereby setting the power usage strategy of the power user in advance based on the power load value, so as to control the power usage of the power user in the specified future time period in advance based on the power usage strategy.

[0044] The target data is data related to the power generation of the new energy power supply system in a specified future time period, which determines the size of the power load value generated by the new energy power supply system in the specified future time period. The target data may include but is not limited to at least one of the following: weather data, time data, and user's power consumption behavior data, wherein the time data is used to indicate the season of the geographical location of the new energy power supply system.

[0045] Different types of target data have different collection methods. Therefore, the methods for collecting target data related to the power generation of the new energy power supply system in a specified future time period may include at least the following three methods:

[0046] First, the target data includes weather data. Then, the specific process of collecting target data related to the power generation of the new energy power supply system in a specified future time period may include the following steps: determining the geographical location of the new energy power supply system, and collecting weather data corresponding to the geographical location in the specified future time period.

[0047] Weather is one of the key factors that affect the power generation of the new energy power supply system, so it is necessary to obtain weather data. The meteorological department usually predicts the weather data of different geographical locations in the future time period in advance, so it is possible to connect with the data platform of the meteorological department to obtain the weather data corresponding to the geographical location of the new energy power supply system in the specified future time period from the data platform of the meteorological department. Weather data is related to the type of new energy power supply system, which may include but is not limited to at least one of the following: temperature, humidity, wind speed, snowfall, and snowfall.

[0048] Second, when the target data includes time data, the specific process of collecting target data related to power generation of the new energy power supply system in a specified future time period may include the following steps: collecting time data corresponding to the specified future time period.

[0049] In different seasons, the power generation capacity of the new energy power supply system is different. Therefore, in order to more accurately predict the power load value produced by the new energy power supply system in the specified future time period, and to carry out advance power consumption control of the power users of the new energy power supply system in the specified future time period, it is necessary to obtain time data indicating the season of the geographical location of the new energy power supply system. Exemplarily, if the specified future time period is from July 1, 2024 to July 31, 2024, then July 1, 2024 to July 31, 2024 are collected as the time data corresponding to the specified future time period, and the time data indicates that the season of the geographical location of the new energy power supply system is summer.

[0050] The third type is that the target data includes the electricity consumption behavior data of the electricity users. Then, the specific process of collecting the target data related to the electricity generation of the new energy power supply system in the specified future time period may include the following steps: collecting the electricity consumption behavior data corresponding to the electricity users in the specified future time period.

[0051] The storage capacity of the energy storage module of the new energy power supply system is limited. If the electricity user cannot use the electric charge in the energy storage module in time, the new energy power supply system will stop generating electricity or the rate of generating electricity will decrease. Therefore, the electricity consumption behavior of the electricity user also affects the amount of electricity generated by the new energy power supply system. Therefore, in order to realize the advance electricity consumption control of the electricity users of the new energy power supply system in the specified future time period, it is necessary to obtain the electricity consumption behavior data of the electricity users. In actual applications, in order to accurately obtain the electricity consumption behavior data of the electricity users, the electricity consumption behavior data of the electricity users in different historical time periods are recorded. The electricity consumption behavior data may include but is not limited to at least one of the following: hourly electricity consumption, daily electricity consumption, monthly electricity consumption, electricity consumption habit data on weekdays and holidays, and electricity consumption habit data of large-scale events held at designated times. When it is necessary to collect target data, determine the historical time period that matches the specified future time period from the recorded historical time periods (for example, if the specified future time period is "July 1, 2024 to July 31, 2024", the matching historical time period is "July 1, 2023 to July 31, 2023"), and collect the electricity usage behavior data corresponding to the matching historical time period as the electricity user's electricity usage behavior data in the specified future time period.

[0052] 102. Based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned.

[0053] In order to obtain data that can more accurately carry out advance electricity consumption management and control of electricity users in a specified future time period, the embodiments of the present disclosure do not directly use the target data to carry out advance electricity consumption management and control of electricity users of the new energy power supply system in the specified future time period after the target data is collected. Instead, the target data is first cleaned.

[0054] In order to clean the target data into data that can more accurately reflect the characteristics of power generation by the renewable energy power supply, the embodiments of the present disclosure clean the target data based on the characteristic data used to describe the characteristics of power generation by the renewable energy power supply system.

[0055] The method for cleaning the target data based on the characteristic data used to describe the power generation characteristics of the new energy power supply system is related to the specific data content included in the characteristic data and the target data. The following is an explanation of the method for cleaning the target data based on the characteristic data used to describe the power generation characteristics of the new energy power supply system based on the specific data content included in the characteristic data and the target data. The method may include the following two methods:

[0056] The first type is that the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in the specified future time period. The characteristic data includes the target value range corresponding to each target parameter, and the target value range is the value range corresponding to the normal power generation of the new energy power supply system. Then, based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the specific process of cleaning the target data can be to perform the following steps for the parameter value corresponding to each target parameter at each time point: determine whether the parameter value corresponding to the time point is within the target value range corresponding to the target parameter, if so, retain the parameter value corresponding to the time point, if not, remove the parameter value corresponding to the time point.

[0057] The target data includes parameter values ​​corresponding to at least one target parameter at multiple time points. The types of target parameters included in the target data are related to the type of target data. Exemplarily, if the target data is weather data, the target parameters included in the weather data may include, but are not limited to: temperature, humidity, wind speed, snowfall, and snowfall. The time point is a time point included in a specified future time period, and the number of time points can be determined based on business needs. In principle, the more time points there are, the more data involved in power consumption control, and the greater the computing power consumption. The fewer time points there are, the less data involved in power consumption control, and the more it will affect the accuracy of power consumption control. Therefore, the number of time points can be determined based on the two points of computing power and accuracy.

[0058] The characteristic data includes the target value range corresponding to each target parameter. The target value range is the value range corresponding to the normal power generation of the new energy power supply system. If the parameter value of the target parameter is not within the corresponding target value range, it means that the new energy power supply system is likely to fail to generate electricity normally.

[0059] Under the specific data content included in the target data and characteristic data, for the parameter value corresponding to each target parameter at each time point, determine whether the parameter value corresponding to the time point is within the target value range corresponding to the target parameter. If it is within the target value range, it means that the parameter value corresponding to the target parameter at this time point is the parameter value of normal power generation of the new energy power supply system, so the parameter value corresponding to the target parameter at this time point is retained. If it is not within the target value range, it means that the parameter value corresponding to the target parameter at this time point is the parameter value that the new energy power supply system cannot generate electricity normally. If the parameter value is retained, it may affect the accuracy of power consumption control, so the parameter value corresponding to the target parameter at this time point is eliminated.

[0060] For example, the target parameter is temperature, and the temperature value corresponding to time point 1 is 23°C, and the temperature value corresponding to time point 2 is 60°C. The characteristic data includes a target value range of -50°C to 50°C corresponding to the temperature. After judgment, it is determined that the temperature value 23°C corresponding to time point 1 is within the target value range, so the temperature value 23°C corresponding to time point 1 is retained. It is determined that the temperature value 60°C corresponding to time point 2 is not within the target value range, so the temperature value 60°C corresponding to time point 2 is eliminated.

[0061] The second type is that the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. The characteristic data includes the mean value range and standard deviation value range corresponding to each target parameter, and the mean value range and standard deviation value range are both value ranges corresponding to normal power generation of the new energy power supply system. Then, based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the specific process of cleaning the target data may include the following steps 102A to 102B:

[0062] 102A. Based on the parameter value corresponding to each target parameter at each time point, determine the parameter value mean and parameter value standard deviation corresponding to each target parameter.

[0063] For each target parameter, the following steps are performed respectively: determining the sum of parameter values ​​corresponding to the target parameter at all time points, and determining the ratio between the sum and the total number of time points of the target parameter as the mean value of the parameter value corresponding to the target parameter.

[0064] For each target parameter, perform the following steps to calculate the standard deviation of the parameter value corresponding to the target parameter: Among them, Sj represents the standard deviation of the parameter value corresponding to the jth target parameter, X i represents the parameter value of the jth target parameter at time point i, represents the mean value of the parameter of the j-th target parameter at all time points, and N represents the total number of time points of the j-th target parameter.

[0065] 102B. Execute for each target parameter separately to determine whether the parameter value mean and parameter value standard deviation corresponding to the target parameter are respectively within the corresponding value range and standard deviation value range. If not, issue an abnormal prompt for the target parameter. If so, clean the parameter values ​​corresponding to the target parameter at multiple time points based on the parameter value mean and parameter value standard deviation corresponding to the target parameter.

[0066] For a target parameter, if it does not include abnormal data or includes less abnormal data, the corresponding parameter value mean and parameter value standard deviation should be respectively within the corresponding mean value range and standard deviation value range. Therefore, for each target parameter, the following steps can be performed: determine whether the parameter value mean and parameter value standard deviation corresponding to the target parameter are respectively within the corresponding value range and standard deviation value range.

[0067] If the parameter value mean and parameter value standard deviation corresponding to the target parameter are not respectively within the corresponding value range and standard deviation value range, that is, the parameter value mean and parameter value standard deviation are both not within the corresponding value range, or any one of the parameter value mean and parameter value standard deviation is not within the corresponding value range, it means that the parameter values ​​of the target parameter at multiple time points are likely to include abnormal values ​​that affect the accuracy of electricity consumption management and control. Therefore, an abnormal prompt is issued for the target parameter to let business personnel know through the prompt that the target parameter is difficult to support the accuracy of electricity consumption management and control.

[0068] If the parameter value mean and parameter value standard deviation corresponding to the target parameter are both within the corresponding value range and standard deviation value range, it means that the probability of abnormal values ​​existing in the parameter values ​​at multiple time points corresponding to the target parameter is small, and the parameter values ​​at multiple time points corresponding to the target parameter can be used to more accurately control electricity consumption. Therefore, continue to execute the step of cleaning the parameter values ​​corresponding to the target parameter at multiple time points based on the parameter value mean and parameter value standard deviation corresponding to the target parameter.

[0069] Based on the parameter value mean and parameter value standard deviation corresponding to the target parameter, the specific process of cleaning the parameter values ​​corresponding to the target parameter at multiple time points can be as follows: for the parameter value corresponding to the target parameter at each time point, the following steps are performed respectively to determine whether the absolute value of the difference between the parameter value corresponding to the time point and the parameter value mean is greater than three times the parameter value standard deviation; if so, the parameter value corresponding to the time point is discarded; if not, the parameter value corresponding to the time point is retained.

[0070] The process of judging whether the absolute value of the difference between the parameter value corresponding to the time point and the mean of the parameter value is greater than three times the standard deviation of the parameter value can be achieved by the following formula: Among them, S j represents the standard deviation of the parameter value corresponding to the jth target parameter, X i represents the parameter value of the jth target parameter at time point i, Represents the mean value of the parameter of the j-th target parameter at all time points.

[0071] For a parameter value at a time point, if the absolute value of the difference between the parameter value corresponding to the time point and the mean of the parameter value is greater than three times the standard deviation of the parameter value, it means that the parameter value corresponding to the time point is likely to be an abnormal value. In order to avoid affecting the accuracy of power consumption control, the parameter value corresponding to the time point is eliminated. If the absolute value of the difference between the parameter value corresponding to the time point and the mean of the parameter value is not greater than three times the standard deviation of the parameter value, it means that the parameter value corresponding to the time point is likely not an abnormal value, and power consumption control can be performed more accurately through it, so the parameter value corresponding to the time point is retained.

[0072] The above two methods can be used alone or in combination, which is not specifically limited in this embodiment. For example, when used in combination, the first method can be used first, and then the second method can be used.

[0073] In addition, both of the above two methods involve the step of eliminating the parameter value corresponding to the time point. In the embodiment of the present disclosure, in order to avoid the normal parameter values ​​from being eliminated, before executing the step of eliminating the parameter value corresponding to the time point, the following steps may also be included: issuing a elimination inquiry for the parameter value corresponding to the time point; if a confirmation elimination feedback is received for the elimination inquiry, then executing the step of eliminating the parameter value corresponding to the time point; if a rejection elimination feedback is received for the elimination inquiry, then retaining the parameter value corresponding to the time point.

[0074] Before eliminating the parameter value corresponding to the time point, a elimination query is issued for the parameter value corresponding to the time point, so as to confirm whether the parameter value corresponding to the time point needs to be eliminated based on the elimination query, so as to reduce the probability of normal parameter values ​​being eliminated.

[0075] If a confirmation rejection feedback is received for the rejection inquiry, it means that the business personnel confirms that the parameter value corresponding to the time point is an abnormal value, so the step of eliminating the parameter value corresponding to the time point is performed. If a rejection rejection feedback is received for the rejection inquiry, it means that the business personnel confirms that the parameter value corresponding to the time point is not an abnormal value, so the parameter value corresponding to the time point is retained.

[0076] 103. The cleaned target data is determined as data required for early power consumption control of power users in a specified future time period.

[0077] The target data after cleaning no longer includes data that affects the accuracy of electricity consumption management and control. The target data after cleaning can more accurately carry out advance electricity consumption management and control on electricity users in a specified future time period. Therefore, the target data after cleaning is determined as the data required for advance electricity consumption management and control on electricity users in a specified future time period.

[0078] Methods for determining the cleaned target data as the data required for early power consumption control of power users in a specified future time period may include the following two methods:

[0079] One is to directly determine the cleaned target data as the data required for early power consumption control of power users in a specified future time period.

[0080] Another method is to perform data smoothing on the cleaned target data; determine the target data after data smoothing as the data required for early power consumption control of the power user in a specified future time period. The purpose of performing data smoothing on the cleaned target data in the embodiment of the present disclosure is to reduce noise interference and further improve data quality. The method for performing data smoothing on the cleaned target data can be to perform data smoothing on the cleaned target data by using a moving weighted average or a median filter.

[0081] The above two methods can be flexibly selected based on business needs, and this embodiment does not limit this.

[0082] After determining the data required for the advance power consumption control of the power users in the specified future time period, the power consumption control of the power users of the new energy power supply system in the specified future time period can be carried out in advance based on the determined data. The advance power consumption control can be: based on the determined data, predicting the power charge value produced by the new energy power supply system in the specified future time period, predicting the power charge value required by the power users in the specified future time period, and based on these two power charge values, performing advance power consumption control on the power users of the new energy power supply system in the specified future time period to ensure that the power users can use electricity normally in the specified future time period.

[0083] The data processing method provided by the present disclosure, in response to determining to conduct advance electricity consumption control on electricity users of the new energy power supply system in a specified future time period, collects target data related to the electricity generation of the new energy power supply system in the specified future time period, and then, based on the characteristic data used to describe the electricity generation characteristics of the new energy power supply system, cleans the target data. Finally, the cleaned target data is determined as the data required for the advance electricity consumption control of the electricity users in the specified future time period. It can be seen that the scheme provided by the present disclosure determines the cleaned target data as the data required for the advance electricity consumption control of the electricity users in the specified future time period. Since the cleaned target data has excluded abnormal situations, the advance electricity consumption control of the electricity users in the specified future time period based on such data can improve the accuracy of electricity consumption control.

[0084] In some disclosed embodiments, considering that if low-quality target data is obtained, then even if the target data is processed, data that can accurately control the electricity consumption of electricity users in a specified future time period cannot be obtained. Therefore, in order to avoid unnecessary data processing, the data processing method provided by the embodiments of the present disclosure may also include the following steps: scoring the target data to obtain a scoring score, where the scoring score is used to reflect the degree of support of the target data for the advance electricity consumption control of electricity users of the new energy power supply system in a specified future time period; if the scoring score reaches the score threshold, executing step 102 to clean the target data based on the characteristic data used to describe the electricity production characteristics of the new energy power supply system; if the scoring score does not reach the score threshold, issuing a prompt that the target data cannot support the advance electricity consumption control of electricity users of the new energy power supply system in a specified future time period.

[0085] In this embodiment, a scoring model corresponding to the new energy power supply system may be used to score the target data to obtain a scoring value.

[0086] If the score reaches the score threshold, it means that the target data can support accurate advance power consumption control of the power user in the specified future time period, so the above step 102 can continue to be executed.

[0087] If the score does not reach the score threshold, it means that the target data cannot accurately support the advance control of electricity consumption of electricity users in the specified future time period. Therefore, in order to avoid unnecessary data processing, a prompt is issued that the target data cannot support the advance control of electricity consumption of electricity users in the new energy power supply system in the specified future time period, so that business personnel are aware of the fact that the target data cannot support the advance control of electricity consumption of electricity users in the new energy power supply system in the specified future time period. Furthermore, while issuing the prompt, the data source can also be replaced, and the target data can be re-collected from the replaced data source.

[0088] In some disclosed embodiments, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. After the target data is cleaned in the above step 102 based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, there may be some missing parameter values ​​at some time points in the cleared target data, and the missing parameter values ​​will reduce the accuracy of power consumption management and control. Therefore, the data processing method provided by the embodiment of the present disclosure may also include the following two processes before determining the cleaned target data as the data required for early power consumption management and control of the power user in the specified future time period in step 103:

[0089] One is to detect, for each target parameter included in the target data after cleaning, whether there is a missing parameter value, and whether there is a first time point with corresponding parameter values ​​at both adjacent time points on both sides; if there is, determine the first parameter value based on the parameter values ​​corresponding to the adjacent time points on both sides, and add the first parameter value to the first time point.

[0090] The missing parameter value at the first time point may be caused by the following two reasons: first, the parameter value corresponding to the first time point is an abnormal value, which is eliminated during the cleaning process; second, the parameter value corresponding to the first time point is not collected when the target data is collected.

[0091] When there is a first time point, in order to determine a more accurate parameter value for the first time point, the parameter values ​​corresponding to the time points adjacent to the first time point are obtained, and based on the parameter values ​​corresponding to the time points adjacent to the first time point, the first parameter value corresponding to the first time point is determined through a linear interpolation method, and the corresponding first parameter value is added to the target data after cleaning to make the data more complete.

[0092] When the first time point does not exist, it indicates that the target data after the cleaning process does not have a situation where parameter values ​​are missing at individual time points.

[0093] Another is, for each target parameter included in the target data after cleaning processing, detect whether there are multiple second time points with consecutive missing parameter values; if so, based on the time points where the target parameter has parameter values, determine the second parameter value corresponding to each second time point through a parameter value determination model based on a time series, and add the corresponding second parameter value for each second time point.

[0094] The second time points with multiple consecutive missing parameter values ​​may be caused by the following two reasons: first, the parameter value corresponding to the second time point is an abnormal value, which is eliminated during the cleaning process; second, the parameter value corresponding to the second time point is not collected when the target data is collected.

[0095] When there are multiple consecutive second time points with missing parameter values, in order to determine a more accurate parameter value for each second time point, the time points at which the target parameter has a parameter value are obtained, and based on the time points at which the target parameter has a parameter value, a parameter value determination model based on a time series is used to determine the second parameter value corresponding to each second time point, and the corresponding second parameter value is added for each second time point, thereby making the target data more complete.

[0096] When the second time point does not exist, it indicates that the target data after the cleaning process does not have a situation where parameter values ​​are missing at multiple consecutive time points.

[0097] The above two processes can be used alone or in combination, and the embodiments of the present disclosure do not specifically limit this. When used in combination, more complete and higher quality data can be obtained for subsequent power consumption control.

[0098] Some embodiments of the present disclosure also provide a data processing device, such as Figure 2 As shown, the data processing device may include:

[0099] A collection module 21 is used for collecting target data related to power generation of the new energy power supply system in the specified future time period in response to determining to perform advance power consumption control on power users of the new energy power supply system in the specified future time period;

[0100] A processing module 22, used for cleaning the target data collected by the collection module 21 based on the characteristic data used to describe the power generation characteristics of the new energy power supply system;

[0101] The determination module 23 is used to determine the target data cleaned and processed by the processing module 22 as data required for early power consumption control of the power user in a specified future time period.

[0102] The data processing device provided by the present disclosure, in response to determining to conduct advance electricity consumption control on electricity users of the new energy power supply system in a specified future time period, collects target data related to the electricity generation of the new energy power supply system in the specified future time period, and then, based on the characteristic data used to describe the characteristics of the electricity generation of the new energy power supply system, cleans the target data. Finally, the cleaned target data is determined as the data required for the advance electricity consumption control of the electricity users in the specified future time period. It can be seen that the scheme provided by the present disclosure determines the cleaned target data as the data required for the advance electricity consumption control of the electricity users in the specified future time period. Since the cleaned target data has excluded abnormal situations, the advance electricity consumption control of the electricity users in the specified future time period based on such data can improve the accuracy of electricity consumption control.

[0103] In some embodiments, Figure 3 As shown, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in the specified future time period. The characteristic data includes a target value range corresponding to each target parameter, and the target value range is a value range corresponding to normal power generation of the new energy power supply system. Then, the processing module 22 includes:

[0104] The first processing unit 221 is used to execute the parameter value corresponding to each target parameter at each time point respectively, and determine whether the parameter value corresponding to the time point is within the target value range corresponding to the target parameter. If so, the parameter value corresponding to the time point is retained; if not, the parameter value corresponding to the time point is discarded.

[0105] In some embodiments, Figure 3 As shown, the processing module 22 may also include:

[0106] The first prompt unit 222 is used to issue a rejection inquiry for the parameter value corresponding to the time point before the first processing unit 221 rejects the parameter value corresponding to the time point; if a confirmation rejection feedback for the rejection inquiry is received, the first processing unit 221 is triggered to execute the step of rejecting the parameter value corresponding to the time point; if a rejection rejection feedback for the rejection inquiry is received, the first processing unit 221 is triggered to execute the step of retaining the parameter value corresponding to the time point.

[0107] In some embodiments, Figure 3 As shown, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in the specified future time period. The characteristic data includes the mean value range and standard deviation value range corresponding to each target parameter, and the mean value range and the standard deviation value range are both value ranges corresponding to normal power generation of the new energy power supply system. Then, the processing module 22 includes:

[0108] A first determining unit 223 is used to determine a parameter value mean and a parameter value standard deviation corresponding to each target parameter based on the parameter value corresponding to each target parameter at each time point;

[0109] The second processing unit 224 is used to execute for each target parameter separately, and determine whether the parameter value mean and parameter value standard deviation corresponding to the target parameter are respectively within the corresponding value range and standard deviation value range. If not, an abnormal prompt is issued for the target parameter. If so, based on the parameter value mean and parameter value standard deviation corresponding to the target parameter, the parameter values ​​corresponding to the target parameter at multiple time points are cleaned.

[0110] In some embodiments, Figure 3 As shown, the second processing unit 224 is specifically used to execute the parameter value corresponding to the target parameter at each time point respectively, and determine whether the absolute value of the difference between the parameter value corresponding to the time point and the mean of the parameter value is greater than three times the standard deviation of the parameter value. If it is greater, the parameter value corresponding to the time point is eliminated; if it is not greater, the parameter value corresponding to the time point is retained.

[0111] In some embodiments, Figure 3 As shown, the processing module 22 may also include:

[0112] The second prompt unit 225 is used to issue a rejection inquiry for the parameter value corresponding to the time point before the second processing unit 224 rejects the parameter value corresponding to the time point; if a confirmation rejection feedback for the rejection inquiry is received, the second processing unit 224 is triggered to execute the step of rejecting the parameter value corresponding to the time point; if a rejection rejection feedback for the rejection inquiry is received, the second processing unit 224 is triggered to execute the step of retaining the parameter value corresponding to the time point.

[0113] In some embodiments, Figure 3 As shown, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. Then, the data processing device provided by the embodiment of the present disclosure may also include:

[0114] The first detection module 24 is used to detect whether there is a missing parameter value for each target parameter included in the cleaned target data, and whether there is a first time point at which both adjacent time points have corresponding parameter values ​​before the determination module 23 determines that the cleaned target data is the data required for early electricity consumption management and control of electricity users in a specified future time period. If so, determine the first parameter value based on the parameter values ​​corresponding to the adjacent time points on both sides, and add the first parameter value for the first time point.

[0115] In some embodiments, Figure 3 As shown, the target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in a specified future time period. Then, the data processing device provided by the embodiment of the present disclosure may also include:

[0116] The second detection module 25 is used to detect whether there are multiple second time points with consecutive missing parameter values ​​for each target parameter included in the cleaned target data before the determination module 23 determines that the cleaned target data is the data required for early electricity consumption management and control of electricity users in a specified future time period; if so, based on the time points where the target parameters have parameter values, determine the second parameter value corresponding to each second time point through a parameter value determination model based on a time series, and add the corresponding second parameter value for each second time point.

[0117] In some embodiments, Figure 3 As shown, the data processing device provided by the embodiment of the present disclosure may also include:

[0118] The scoring module 26 is used to score the target data collected by the collection module 21 to obtain a scoring score, which is used to reflect the degree of support of the target data for the advance electricity consumption management and control of electricity users of the new energy power supply system in a specified future time period; if the scoring score reaches the score threshold, the trigger processing module 22 executes the step of cleaning the target data based on the characteristic data used to describe the electricity production characteristics of the new energy power supply system; if the scoring score does not reach the score threshold, the trigger prompt module 27 issues a prompt that the target data cannot support the advance electricity consumption management and control of electricity users of the new energy power supply system in a specified future time period.

[0119] In some embodiments, Figure 3 As shown, the determination module 23 is specifically used to perform data smoothing on the target data after the cleaning process; the target data after the data smoothing process is determined as the data required for the advance power consumption control of the power user in the specified future time period.

[0120] In some embodiments, Figure 3 As shown, the target data collected by the collection module 21 includes at least one of the following: weather data, time data, and user behavior data, wherein the time data is used to indicate the season of the geographical location of the new energy power supply system.

[0121] In some embodiments, the new energy power supply system in the embodiments of the present disclosure includes at least one of the following: a solar power supply system, a wind power supply system, a geothermal power supply system, and a tidal power supply system.

[0122] In the data processing device provided by the embodiment of the present disclosure, the detailed explanations used in the operation of each functional module can be found in the corresponding detailed explanations of the above-mentioned data processing method embodiment, which will not be repeated here.

[0123] Furthermore, some embodiments of the present disclosure also provide a computer-readable storage medium, the storage medium including a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above-mentioned data processing method.

[0124] Furthermore, some embodiments of the present disclosure also provide an electronic device, which includes: a memory for storing a program; and a processor, coupled to the memory, for running the program to execute the above-mentioned data processing method.

[0125] So far, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0126] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present disclosure. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict.

[0127] In the above embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems may also be used together with the teachings based thereon. According to the above description, it is apparent that the structure required for constructing such systems is. In addition, the present disclosure is not directed to any particular programming language either. It should be understood that various programming languages ​​may be utilized to implement the disclosure described herein, and the description of the specific languages ​​above is for the purpose of disclosing the preferred embodiments of the present disclosure.

[0129] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0130] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data switching device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data switching device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data switching device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data transfer device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0135] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0136] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0137] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0138] It will be appreciated by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] The above are only embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the scope of the claims of the present disclosure.

Claims

1. A data processing method, characterized in that: The method comprises: In response to determining to perform advance power consumption control on power users of the new energy power supply system in a specified future time period, collecting target data related to power generation of the new energy power supply system in the specified future time period; Based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned; The cleaned target data is determined as data required for early power consumption control of the power user in a specified future time period.

2. The method according to claim 1, characterized in that The target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, the multiple time points are included in the specified future time period, the characteristic data includes a target value range corresponding to each target parameter, and the target value range is a value range corresponding to normal power generation of the new energy power supply system. Then, based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned, including: For each target parameter, the parameter value corresponding to each time point is executed separately to determine whether the parameter value corresponding to the time point is within the target value range corresponding to the target parameter. If so, the parameter value corresponding to the time point is retained; if not, the parameter value corresponding to the time point is discarded.

3. The method according to claim 1 or 2, characterized in that: The target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, the multiple time points are included in the specified future time period, the characteristic data includes a mean value range and a standard deviation value range corresponding to each target parameter, the mean value range and the standard deviation value range are both value ranges corresponding to normal power generation of the new energy power supply system, then, based on the characteristic data used to describe the power generation characteristics of the new energy power supply system, the target data is cleaned, including: Based on the parameter value corresponding to each target parameter at each time point, respectively determine the parameter value mean and parameter value standard deviation corresponding to each target parameter; The process is executed for each target parameter separately to determine whether the parameter value mean and parameter value standard deviation corresponding to the target parameter are respectively within the corresponding value range and standard deviation value range. If not, an abnormal prompt is issued for the target parameter. If yes, the parameter values ​​corresponding to the target parameter at multiple time points are cleaned based on the parameter value mean and parameter value standard deviation corresponding to the target parameter.

4. The method according to claim 3, characterized in that Based on the parameter value mean and parameter value standard deviation corresponding to the target parameter, the parameter values ​​corresponding to the target parameter at multiple time points are cleaned, including: The parameter value corresponding to the target parameter at each time point is executed separately to determine whether the absolute value of the difference between the parameter value corresponding to the time point and the mean of the parameter value is greater than three times the standard deviation of the parameter value. If so, the parameter value corresponding to the time point is eliminated; if not, the parameter value corresponding to the time point is retained.

5. The method according to claim 2 or 4, characterized in that: Before removing the parameter value corresponding to the time point, the method further includes: issuing a rejection query for the parameter value corresponding to the time point; If a confirmation rejection feedback is received for the rejection inquiry, a step of rejecting the parameter value corresponding to the time point is executed; If a rejection feedback is received for the rejection query, the parameter value corresponding to the time point is retained.

6. The method according to claim 1, characterized in that The target data includes parameter values ​​corresponding to at least one target parameter at multiple time points, and the multiple time points are included in the specified future time period. Then, before determining the cleaned target data as data required for performing advance power consumption control on the power consumption of the power user in the specified future time period, the method further includes: For each target parameter included in the cleaned target data, detect whether there is a first time point with a missing parameter value and two adjacent time points having corresponding parameter values; if there is, determine a first parameter value based on the parameter values ​​corresponding to the two adjacent time points, and add the first parameter value to the first time point; and / or, For each target parameter included in the target data after cleaning processing, detect whether there are multiple second time points with consecutive missing parameter values; if so, based on the time points where the target parameter has parameter values, determine the second parameter value corresponding to each second time point through a parameter value determination model based on a time series, and add the corresponding second parameter value for each second time point.

7. The method according to any one of claims 1, 2, 4 and 6, characterized in that: The method further includes: scoring the target data to obtain a scoring score, wherein the scoring score is used to reflect the support degree of the target data for the advance power consumption control of the power users of the new energy power supply system in the specified future time period; if the scoring score reaches a score threshold, executing a step of cleaning the target data based on the characteristic data used to describe the power generation characteristics of the new energy power supply system; if the scoring score does not reach the score threshold, issuing a prompt that the target data cannot support the advance power consumption control of the power users of the new energy power supply system in the specified future time period; and / or, Determining the cleaned target data as data required for early power consumption control of the power consumption of the power user in a specified future time period, including: performing data smoothing on the cleaned target data; determining the smoothed target data as data required for early power consumption control of the power consumption of the power user in a specified future time period; and / or, The target data includes at least one of the following: weather data, time data, and user behavior data, wherein the time data is used to indicate the season of the geographical location of the new energy power supply system; and / or, The new energy power supply system includes at least one of the following: a solar power supply system, a wind power supply system, a geothermal power supply system, and a tidal power supply system.

8. A data processing device, characterized in that: The device comprises: A collection module, configured to collect target data related to power generation of the new energy power supply system in the specified future time period in response to determining to perform advance power consumption control on power users of the new energy power supply system in the specified future time period; A processing module, used for cleaning the target data based on the characteristic data used to describe the power generation characteristics of the new energy power supply system; The determination module is used to determine the cleaned target data as the data required for early power consumption control of the power user in a specified future time period.

9. A computer-readable storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises: Memory, used to store programs; A processor, coupled to the memory, configured to run the program to execute the data processing method according to any one of claims 1 to 7.

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