Object determination method and device, storage medium and electronic equipment

Abstract

The invention discloses an object determination method and device, a storage medium and electronic equipment. The method relates to the technical field of data processing, and comprises the following steps: determining a potential object according to historical power consumption data, the potential object being an object of which the probability that the power consumption is abnormal is greater than a preset value; obtaining a target power factor of the potential object and an actual power generation capacity in a target time period; calculating according to the target power factor and the actual power generation capacity to obtain a target power utilization deviation value of the potential object; and judging whether the potential object is a risk object with abnormal electricity consumption or not according to the target electricity consumption deviation value, and obtaining a judgment result. According to the method and the device, the problem that the accuracy of determining the risk user is relatively low because the risk user with the abnormal electricity consumption is determined through the electricity consumption data at a certain time point in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for determining an object, a storage medium, and an electronic device. Background Art

[0002] Electricity theft refers to the act of occupying electric energy by means not in compliance with relevant regulations, with the purpose of not paying or paying less electricity bills, and not measuring or under-measuring electricity consumption by means not in compliance with relevant regulations. This not only causes huge economic losses to power supply companies, but also poses great harm to the safety of the power grid. Traditional methods usually make abnormal judgments on multiple indicators of special transformers, such as reverse active meter code, current imbalance, current reverse, power factor, total forward active power, active power, and total sub-meter current and power. As long as one of them is abnormal, the special transformer measurement is considered abnormal.

[0003] On the one hand, the traditional method does not take into account the metering data anomalies caused by the metering errors of the metering device itself. On the other hand, electricity theft is usually a continuous behavior. The traditional method only determines that the user has committed electricity theft based on the abnormal electricity consumption data at a certain point in time or in a very short period of time. It is easy to ignore the occasional changes in the user's electricity demand, resulting in the misjudgment of electricity theft.

[0004] Currently, no effective solution has been proposed to address the problem that the accuracy of determining risk users with abnormal electricity consumption is relatively low in related technologies by using electricity consumption data at a certain point in time. Summary of the invention

[0005] The main purpose of the present application is to provide a method and device for determining an object, a storage medium and an electronic device, so as to solve the problem in the related art of determining risk users with abnormal electricity consumption through electricity consumption data at a certain point in time, resulting in low accuracy in determining risk users.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining an object is provided. The method comprises: determining a potential object based on historical power consumption data, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value; obtaining the target power factor of the potential object and the actual power generation capacity in the target time period; performing calculation based on the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object; judging whether the potential object is a risk object with abnormal power consumption based on the target power consumption deviation value, and obtaining a judgment result.

[0007] Furthermore, calculating according to the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object includes: determining the target power consumption type of the potential object, and obtaining the target power consumption type value corresponding to the target power consumption type; obtaining a target linear regression model corresponding to the target power consumption type, wherein the target linear regression model is fitted based on the power consumption type value, the historical power factor, the historical adaptive capacity and the historical power consumption deviation value; and obtaining the target power consumption deviation value by calculating through the target linear regression model based on the target power consumption type value, the target power factor and the actual power generation capacity.

[0008] Furthermore, before obtaining the target linear regression model corresponding to the target electricity usage type, the method also includes: determining multiple electricity usage types, and digitizing each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; obtaining historical data corresponding to each electricity usage type, wherein the historical data at least includes the electricity usage type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value corresponding to each electricity usage type; and constructing a linear regression model corresponding to each electricity usage type based on the historical data corresponding to each electricity usage type.

[0009] Furthermore, the target linear regression model is used to perform calculations based on the target electricity type value, the target power factor and the actual power generation capacity to obtain the target electricity deviation value, including: obtaining the preset electricity type value, preset power factor and preset power generation capacity of the potential object within the target time period; calculating the difference between the preset electricity type value and the target electricity type value to obtain a first difference; calculating the difference between the preset power factor and the target power factor to obtain a second difference; calculating the difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; and performing calculations based on the first difference, the second difference and the third difference through the target linear regression model to obtain the target electricity deviation value.

[0010] Furthermore, based on the historical electricity consumption data, determining the potential object includes: obtaining the historical electricity consumption data of the initial object, and fitting based on the historical electricity consumption data to obtain the predicted electricity consumption of the initial object within the target time period; obtaining the rated capacity of the initial object within the target time period; and determining the potential object from the initial object based on the predicted electricity consumption and the rated capacity.

[0011] Furthermore, obtaining the rated capacity of the initial object within the target time period includes: obtaining the unit operating capacity of the power system in which the initial object is located; determining a day value corresponding to the target time period; and calculating based on the unit operating capacity and the day value to obtain the rated capacity.

[0012] Furthermore, based on the predicted power consumption and the rated capacity, determining the potential object from the initial object includes: obtaining a preset multiplier coefficient; performing calculations based on the multiplier coefficient and the predicted power consumption to obtain a target value; and determining the potential object from the initial object based on a size relationship between the target value and the rated capacity.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for determining an object is provided. The device includes: a first determination unit, used to determine a potential object based on historical power consumption data, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value; a first acquisition unit, used to obtain the target power factor of the potential object and the actual power generation capacity in the target time period; a calculation unit, used to calculate based on the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object; a judgment unit, used to judge whether the potential object is a risk object with abnormal power consumption based on the target power consumption deviation value, and obtain a judgment result.

[0014] Furthermore, the calculation unit includes: a first determination subunit, used to determine the target electricity consumption type of the potential object, and obtain the target electricity consumption type value corresponding to the target electricity consumption type; a first acquisition subunit, used to obtain a target linear regression model corresponding to the target electricity consumption type, wherein the target linear regression model is fitted based on the electricity consumption type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value; a calculation subunit, used to calculate through the target linear regression model based on the target electricity consumption type value, the target power factor and the actual power generation capacity to obtain the target electricity consumption deviation value.

[0015] Furthermore, the device also includes: a second determination unit, used to determine multiple electricity usage types before obtaining the target linear regression model corresponding to the target electricity usage type, and perform numerical processing on each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; a second acquisition unit, used to obtain historical data corresponding to each electricity usage type, wherein the historical data at least includes the electricity usage type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value corresponding to each electricity usage type; a construction unit, used to construct a linear regression model corresponding to each electricity usage type based on the historical data corresponding to each electricity usage type.

[0016] Furthermore, the calculation subunit includes: a first acquisition module, used to obtain the preset electricity consumption type value, preset power factor and preset power generation capacity of the potential object within the target time period; a first calculation module, used to calculate the difference between the preset electricity consumption type value and the target electricity consumption type value to obtain a first difference; a second calculation module, used to calculate the difference between the preset power factor and the target power factor to obtain a second difference; a third calculation module, used to calculate the difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; a fourth calculation module, used to calculate based on the first difference, the second difference and the third difference through the target linear regression model to obtain the target electricity consumption deviation value.

[0017] Furthermore, the first determination unit includes: a second acquisition subunit, used to acquire historical power consumption data of the initial object, and to perform fitting based on the historical power consumption data to obtain predicted power consumption of the initial object within the target time period; a third acquisition subunit, used to acquire the rated capacity of the initial object within the target time period; and a second determination subunit, used to determine the potential object from the initial object based on the predicted power consumption and the rated capacity.

[0018] Furthermore, the third acquisition subunit includes: a second acquisition module, used to acquire the unit operating capacity of the power system in which the initial object is located; a first determination module, used to determine the day value corresponding to the target time period; and a fifth calculation module, used to calculate based on the unit operating capacity and the day value to obtain the rated capacity.

[0019] Furthermore, the second determination subunit includes: a third acquisition module, used to obtain a preset multiplier coefficient; a sixth calculation module, used to calculate based on the multiplier coefficient and the predicted power consumption to obtain a target value; and a second determination module, used to determine the potential object from the initial object based on the size relationship between the target value and the rated capacity.

[0020] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned object determination methods.

[0021] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is further provided, the electronic device includes one or more processors and a memory, and the memory is used to store the method for determining an object described in any one of the above-mentioned methods implemented by the one or more processors.

[0022] Through this application, the following steps are adopted: based on historical electricity consumption data, determine the potential object, wherein the potential object is an object whose probability of abnormal electricity consumption is greater than a preset value; obtain the target power factor of the potential object and the actual power generation capacity in the target time period; calculate based on the target power factor and the actual power generation capacity to obtain the target electricity consumption deviation value of the potential object; based on the target electricity consumption deviation value, judge whether the potential object is a risk object with abnormal electricity consumption, and obtain the judgment result, which solves the problem of low accuracy in determining risk users by using electricity consumption data at a certain point in time in the related technology. In this scheme, first determine the potential object that may have abnormal electricity consumption based on historical electricity consumption data, then calculate based on the target power factor and the actual power generation capacity to obtain the target electricity consumption deviation value of the potential object, and then determine whether the potential object is a risk object with abnormal electricity consumption based on the target electricity consumption deviation value. Characterize the electricity consumption deviation of the special transformer user by the target power factor and the actual power generation capacity, which can effectively reduce the misjudgment rate, thereby achieving the effect of improving the accuracy of determining risk users. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 The process of the method for determining an object provided in the embodiment of the present application is as follows Figure 1 ;

[0025] Figure 2 The process of the method for determining an object provided in the embodiment of the present application is as follows Figure 2 ;

[0026] Figure 3 is a schematic diagram of a device for determining an object provided in an embodiment of the present application;

[0027] Figure 4 is a schematic diagram of a system for determining an object provided in an embodiment of the present application;

[0028] Figure 5 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0033] The present invention is described below in conjunction with preferred implementation steps. Figure 1 The process of the method for determining an object provided in the embodiment of the present application is as follows Figure 1 ,like Figure 1 As shown, the method comprises the following steps:

[0034] Step S101, determining a potential object based on historical power consumption data, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value;

[0035] Optionally, historical electricity consumption data of dedicated transformer users is obtained. It should be noted that dedicated transformer users, that is, dedicated transformer users, usually refer to users who own their own transformers and use the transformers for power supply.

[0036] The objects whose power consumption is abnormal with a probability greater than a preset value are determined from multiple dedicated transformer users through historical power consumption data, i.e., the above-mentioned potential objects. For example, the monthly power consumption data of the corresponding months of the historical years are fitted, and the monthly maximum power demand of the dedicated transformer users is predicted based on the fitting results. Based on the predicted monthly maximum power demand, potential objects suspected of abnormality are determined from multiple dedicated transformer users.

[0037] Step S102, obtaining a target power factor of a potential object and an actual power generation capacity within a target time period;

[0038] Optionally, the target power factor of the potential object and the adaptation capacity (ie, the actual power generation capacity) within the target time period are obtained. For example, the monthly adaptation capacity of the potential object is obtained.

[0039] Step S103, calculating according to the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object;

[0040] Optionally, the power consumption deviation value of the potential object is obtained according to the target power factor of the potential object and the actual power generation capacity in the target time period. For example, the power consumption deviation amount can be predicted based on the target power factor and the actual power generation capacity by a deep learning model to obtain the target power consumption deviation value. For example, the monthly power consumption deviation value is calculated using the power factor of the potential object and the monthly adaptive capacity.

[0041] Step S104: judging whether the potential object is a risk object with abnormal power consumption according to the target power consumption deviation value, and obtaining a judgment result.

[0042] Optionally, the target power consumption deviation value is used to determine whether the potential object is a risk object with abnormal power consumption. For example, if the monthly power consumption deviation is greater than or equal to the monthly power consumption deviation threshold, the potential object is determined to be a risk object with abnormal power consumption.

[0043] To summarize, we first determine potential objects that may have abnormal electricity consumption based on historical electricity consumption data, then calculate based on the target power factor and actual power generation capacity to obtain the target electricity consumption deviation value of the potential object, and then determine whether the potential object is a risk object with abnormal electricity consumption based on the target electricity consumption deviation value. Characterizing the electricity consumption deviation of special transformer users by target power factor and actual power generation capacity can effectively reduce the misjudgment rate, thereby achieving the effect of improving the accuracy of identifying risky users.

[0044] Optionally, in the object determination method provided in the embodiment of the present application, calculation is performed based on the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object, including: determining the target power consumption type of the potential object, and obtaining the target power consumption type value corresponding to the target power consumption type; obtaining a target linear regression model corresponding to the target power consumption type, wherein the target linear regression model is fitted based on the power consumption type value, historical power factor, historical adaptive capacity and historical power consumption deviation value; and calculating based on the target power consumption type value, target power factor and actual power generation capacity through the target linear regression model to obtain the target power consumption deviation value.

[0045] In an optional embodiment, the target power consumption deviation value of the potential object is obtained by calculation based on the target power factor and the actual power generation capacity, including the following steps: clarifying the target power consumption type of the potential object, for example, the power consumption type includes large industry, non-general industry, commerce, wholesale, residential life, non-residential, agricultural production, agricultural irrigation and drainage, and other power consumption types. According to the target power consumption type, determine the target power consumption type value corresponding to the target power consumption type and the corresponding target linear regression model.

[0046] It should be noted that the target linear regression model is fitted based on the power consumption type value, historical power factor, historical adaptive capacity and historical power consumption deviation value.

[0047] The target power consumption deviation value is obtained by calculating the target power consumption type value, target power factor and actual power generation capacity through the target linear regression model.

[0048] The power consumption deviation of dedicated transformer users is characterized by associating the power consumption type, power factor and monthly adaptive capacity, and the changes in multiple indicators are used to prompt metering anomalies, thereby improving the accuracy of anomaly judgment.

[0049] Optionally, in the object determination method provided in the embodiment of the present application, before obtaining the target linear regression model corresponding to the target electricity usage type, the method also includes: determining multiple electricity usage types, and digitizing each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; obtaining historical data corresponding to each electricity usage type, wherein the historical data at least includes the electricity usage type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value corresponding to each electricity usage type; and constructing a linear regression model corresponding to each electricity usage type based on the historical data corresponding to each electricity usage type.

[0050] In an optional embodiment, the following steps are used to construct a linear regression model corresponding to each electricity consumption type: multiple electricity consumption types are identified, for example, electricity consumption types include large industry, non-general industry, commerce, wholesale, residential life, non-residential, agricultural production, agricultural irrigation and drainage, and others, and these electricity consumption types are numerically represented to obtain the electricity consumption type value corresponding to each electricity consumption type, and then, the historical data corresponding to each electricity consumption type is obtained, wherein the historical data at least includes the electricity consumption type value corresponding to each electricity consumption type, the historical power factor, the historical adaptive capacity, and the historical power consumption deviation value. Finally, the linear regression model corresponding to each electricity consumption type is constructed through the historical data corresponding to each electricity consumption type.

[0051] In an optional embodiment, a linear regression model is constructed by the difference between the preset power type value and the target power type value, the difference between the preset power factor and the target power factor, and the difference between the actual power generation capacity and the preset power generation capacity.

[0052] For example, a linear regression model is constructed between the electricity type deviation, power factor deviation and monthly capacity deviation and the monthly electricity consumption deviation of the special transformer user, wherein the linear regression model is obtained by linear regression of the historical electricity type deviation, the historical power factor deviation, the historical monthly capacity deviation and the historical monthly electricity consumption deviation of the special transformer user, the historical electricity type deviation is the absolute value of the difference between the historical actual electricity type value and the preset electricity type value, the historical power factor deviation is the absolute value of the difference between the historical actual power factor and the preset power factor, the historical monthly capacity deviation is the absolute value of the difference between the historical monthly actual capacity and the preset monthly adaptive capacity, and the historical monthly electricity consumption deviation of the special transformer user is the absolute value of the difference between the historical monthly actual electricity consumption and the preset monthly electricity consumption.

[0053] For example, construct a linear regression model corresponding to each electricity consumption type as Y C1 =a1*(U1-u1)+a2*(V1-v1)+a3*(W1-w1).

[0054] Among them, Y C1 is the historical monthly electricity consumption deviation, U1 is the historical actual electricity consumption type value, u1 is the preset electricity consumption type value, V1 is the historical actual power factor, v1 is the preset power factor, W1 is the historical monthly actual capacity, w1 is the preset monthly adaptation capacity, and a1, a2, and a3 are the corresponding regression coefficients.

[0055] By constructing a linear regression model, the power consumption deviation value can be more accurately inferred from different indicators, thereby improving the accuracy of abnormal user judgment.

[0056] Optionally, in the object determination method provided in the embodiment of the present application, the target power consumption deviation value is obtained by calculating based on the target power consumption type value, the target power factor and the actual power generation capacity through the target linear regression model, including: obtaining the preset power consumption type value, the preset power factor and the preset power generation capacity of the potential object within the target time period; calculating the difference between the preset power consumption type value and the target power consumption type value to obtain a first difference; calculating the difference between the preset power factor and the target power factor to obtain a second difference; calculating the difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; calculating based on the first difference, the second difference and the third difference through the target linear regression model to obtain the target power consumption deviation value.

[0057] In an optional embodiment, the target power consumption deviation value is calculated by the following steps: obtaining the preset power consumption type value, preset power factor and preset power generation capacity of the potential object within the target time period, and then calculating the difference between the preset power consumption type value and the target power consumption type value, calculating the difference between the actual power generation capacity and the preset power generation capacity, and calculating the difference between the preset power factor and the target power factor; finally, the target linear regression model is calculated based on the first difference, the second difference and the third difference to obtain the target power consumption deviation value.

[0058] Optionally, in the object determination method provided in the embodiment of the present application, determining potential objects based on historical electricity consumption data includes: obtaining the historical electricity consumption data of the initial object, and fitting based on the historical electricity consumption data to obtain the predicted electricity consumption of the initial object within the target time period; obtaining the rated capacity of the initial object within the target time period; and determining potential objects from the initial objects based on the predicted electricity consumption and the rated capacity.

[0059] In an optional embodiment, the following steps are used to determine the potential objects mentioned above: historical electricity consumption data of the initial object (i.e., the dedicated transformer user) is collected, and then fitting is performed based on the historical electricity consumption data to obtain the predicted electricity consumption of the initial object within the target time period. For example, N historical years are selected, such as 5 historical years, and the same month as the current month in the 5 years is assumed to be April. That is, based on the fitting of the user's monthly electricity consumption K1 in April of the first year, K2 in April of the second year, K3 in April of the third year, K4 in April of the fourth year, and K5 in April of the fifth year, the maximum demand value KS=F(K1, K2, K3, K4, K5) for the current April is predicted.

[0060] Then, the rated capacity of the initial object in the target time period is obtained, for example, the variable monthly rated capacity of the initial object is obtained. It should be noted that the variable monthly rated capacity can be obtained by hourly operating capacity*12h*number of days in the month.

[0061] Finally, potential objects are identified from the initial objects based on the predicted power consumption and rated capacity.

[0062] The maximum demand value of the month is predicted by historical electricity consumption data, and the impact of historical indicators on current indicators is considered vertically, which can effectively improve the accuracy of abnormal judgment.

[0063] Optionally, in the object determination method provided in an embodiment of the present application, obtaining the rated capacity of the initial object within the target time period includes: obtaining the unit operating capacity of the power system in which the initial object is located; determining a day value corresponding to the target time period; and calculating based on the unit operating capacity and the day value to obtain the rated capacity.

[0064] In an optional embodiment, the rated capacity of the initial object in the target time period is calculated by the following steps: obtaining the unit operating capacity of the power system where the initial object is located and calculating the day value corresponding to the target time period, and finally calculating according to the unit operating capacity and the day value to obtain the rated capacity. For example, the rated capacity = 12M*D, M is the operating capacity per hour, and N is the number of days in the month.

[0065] Optionally, in the object determination method provided in the embodiment of the present application, determining potential objects from initial objects based on predicted power consumption and rated capacity includes: obtaining a preset multiplier coefficient; calculating based on the multiplier coefficient and predicted power consumption to obtain a target value; and determining potential objects from initial objects based on the relationship between the target value and the rated capacity.

[0066] In an optional embodiment, a preset multiplier coefficient corresponding to the initial object is obtained, and a target value is obtained by calculating the multiplier coefficient and the predicted power consumption. Finally, a potential object is determined from the initial object according to the size relationship between the target value and the rated capacity. For example, if the product of the monthly maximum demand value and the multiplier coefficient is greater than the monthly rated capacity of the special transformer, it is determined that the current user to be evaluated is suspected of metering abnormality.

[0067] In an optional embodiment, the Figure 2 The flowchart shown realizes abnormal user identification: Step 1: Fit the monthly electricity consumption data of the corresponding months in the historical year, and predict the monthly maximum demand value of the current user to be evaluated based on the fitting results;

[0068] Step 2: If the product of the monthly maximum demand value and the multiplication factor is greater than the monthly rated capacity of the dedicated transformer, it is determined that the current user to be evaluated has a suspected metering anomaly. The monthly rated capacity of the dedicated transformer can be obtained by hourly operating capacity*12h*number of days in the month;

[0069] Step 3: The electricity consumption type is numerically represented. Each electricity consumption type has a corresponding power factor and monthly adaptive capacity. The electricity consumption types include large industry, non-general industry, commerce, wholesale, residential life, non-residential, agricultural production, agricultural irrigation and drainage, and others.

[0070] Step 4: construct a linear regression model between the power type deviation, power factor deviation and monthly capacity deviation and the monthly power consumption deviation of the dedicated transformer user, wherein the linear regression model is obtained by linear regression based on the historical power type deviation, historical power factor deviation, historical monthly capacity deviation and the historical monthly power consumption deviation of the dedicated transformer user, the historical power type deviation is the absolute value of the difference between the historical actual power type value and the preset power type value, the historical power factor deviation is the absolute value of the difference between the historical actual power factor and the preset power factor, the historical monthly capacity deviation is the absolute value of the difference between the historical monthly actual capacity and the preset monthly adaptive capacity, and the historical monthly power consumption deviation of the dedicated transformer user is the absolute value of the difference between the historical monthly actual power consumption and the preset monthly power consumption;

[0071] Step 5: Input the power consumption type value, power factor and monthly actual capacity of the current user to be evaluated into the linear regression model to obtain the monthly power consumption deviation of the current user to be evaluated;

[0072] Step 6: If the monthly electricity consumption deviation is greater than or equal to the monthly electricity consumption deviation threshold, the current user to be evaluated is determined as a metering abnormal user.

[0073] For example, select N years of history, such as 5 years of history, and the same month as the current month in the 5 years is assumed to be April, that is, based on the fitting of the user's monthly electricity consumption K1 in April of the first year, K2 in April of the second year, K3 in April of the third year, K4 in April of the fourth year, and K5 in April of the fifth year, predict the user's maximum demand value KS=F(K1, K2, K3, K4, K5) in the current April; if the product of KS and the preset multiplier coefficient b is greater than the monthly rated capacity of the special transformer 12M*D, it is determined that the current user to be evaluated is suspected of metering abnormality, that is, KS*b>12M*D, where M is the operating capacity per hour and N is the number of days in the month. The large industry, non-general industry, commerce, wholesale, residential life, non-residential, agricultural production, agricultural irrigation and drainage, and others are numerically represented, and each type of electricity consumption is associated with the corresponding power factor and monthly adaptive capacity; Step 4: Construct a linear regression model corresponding to each type of electricity consumption, for example: Y C1 =a1*(U1-u1)+a2*(V1-v1)+a3*(W1-w1) where Y C1Historical monthly electricity consumption deviation, U1 is the historical actual electricity consumption type value, u1 is the preset electricity consumption type value, V1 is the historical actual power factor, v1 is the preset power factor, W1 is the historical monthly actual capacity, w1 is the preset monthly adaptive capacity, a1, a2, a3 are the corresponding regression coefficients; the electricity consumption type value U2, power factor V2 and monthly actual capacity W2 of the current user to be evaluated are input into the model of step 2 to obtain the monthly electricity consumption deviation Y of the current user to be evaluated. C2 , that is, Y C2 =a1*(U2-u1)+a2*(V2-v1)+a3*(W2-w1); if the monthly power consumption deviation is Y C2 Greater than or equal to the monthly power consumption deviation threshold YH, that is, Y C2 ≥YH, the current user to be evaluated is determined to have measurement abnormality.

[0074] The object determination method provided in the embodiment of the present application determines the potential object based on the historical power consumption data, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value; obtains the target power factor of the potential object and the actual power generation capacity in the target time period; calculates based on the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object; and judges whether the potential object is a risk object with abnormal power consumption based on the target power consumption deviation value to obtain a judgment result, which solves the problem of low accuracy in determining risk users by determining risk users with abnormal power consumption based on power consumption data at a certain point in time in the related technology. In this scheme, the potential object that may have abnormal power consumption is first determined based on the historical power consumption data, and then the target power consumption deviation value of the potential object is calculated based on the target power factor and the actual power generation capacity, and then the target power consumption deviation value is determined based on the target power consumption deviation value. Characterizing the power consumption deviation of the special transformer user by the target power factor and the actual power generation capacity can effectively reduce the misjudgment rate, thereby achieving the effect of improving the accuracy of determining risk users.

[0075] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0076] The present application also provides an object determination device. It should be noted that the object determination device of the present application can be used to execute the object determination method provided by the present application. The object determination device provided by the present application is introduced below.

[0077] Figure 3 is a schematic diagram of an object determination device according to an embodiment of the present application. Figure 3 As shown, the device includes: a first determining unit 301, a first acquiring unit 302, a calculating unit 303 and a judging unit 304.

[0078] The first determination unit 301 is used to determine a potential object based on historical power consumption data, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value;

[0079] A first acquisition unit 302 is used to acquire a target power factor of a potential object and an actual power generation capacity within a target time period;

[0080] A calculation unit 303 is used to calculate according to the target power factor and the actual power generation capacity to obtain a target power consumption deviation value of a potential object;

[0081] The judgment unit 304 is used to judge whether the potential object is a risk object with abnormal power consumption according to the target power consumption deviation value, and obtain a judgment result.

[0082] The object determination device provided in the embodiment of the present application determines the potential object according to the historical power consumption data through the first determination unit 301, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value; the first acquisition unit 302 acquires the target power factor of the potential object and the actual power generation capacity in the target time period; the calculation unit 303 calculates according to the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object; the judgment unit 304 judges whether the potential object is a risk object with abnormal power consumption according to the target power consumption deviation value, and obtains the judgment result, which solves the problem that the accuracy of determining the risk user with abnormal power consumption is relatively low in the related technology by determining the risk user with abnormal power consumption through the power consumption data at a certain point in time. In this scheme, the potential object that may have abnormal power consumption is first determined according to the historical power consumption data, and then the target power consumption deviation value of the potential object is calculated according to the target power consumption deviation value, and then the potential object is determined according to the target power consumption deviation value. The power consumption deviation of the special transformer user is characterized by the target power factor and the actual power generation capacity, which can effectively reduce the misjudgment rate, thereby achieving the effect of improving the accuracy of determining the risk user.

[0083] Optionally, in the object determination device provided in the embodiment of the present application, the calculation unit includes: a first determination subunit, used to determine the target electricity consumption type of the potential object, and obtain the target electricity consumption type value corresponding to the target electricity consumption type; a first acquisition subunit, used to obtain a target linear regression model corresponding to the target electricity consumption type, wherein the target linear regression model is fitted based on the electricity consumption type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value; a calculation subunit, used to calculate through the target linear regression model based on the target electricity consumption type value, target power factor and actual power generation capacity to obtain the target electricity consumption deviation value.

[0084] Optionally, in the object determination device provided in the embodiment of the present application, the device also includes: a second determination unit, used to determine multiple electricity usage types before obtaining the target linear regression model corresponding to the target electricity usage type, and digitize each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; a second acquisition unit, used to obtain historical data corresponding to each electricity usage type, wherein the historical data at least includes the electricity usage type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value corresponding to each electricity usage type; a construction unit, used to construct a linear regression model corresponding to each electricity usage type based on the historical data corresponding to each electricity usage type.

[0085] Optionally, in the object determination device provided in the embodiment of the present application, the calculation subunit includes: a first acquisition module, used to obtain a preset electricity consumption type value, a preset power factor and a preset power generation capacity of a potential object within a target time period; a first calculation module, used to calculate the difference between the preset electricity consumption type value and the target electricity consumption type value to obtain a first difference; a second calculation module, used to calculate the difference between the preset power factor and the target power factor to obtain a second difference; a third calculation module, used to calculate the difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; and a fourth calculation module, used to calculate based on the first difference, the second difference and the third difference through a target linear regression model to obtain a target electricity consumption deviation value.

[0086] Optionally, in the object determination device provided in the embodiment of the present application, the first determination unit includes: a second acquisition sub-unit, used to acquire historical power consumption data of the initial object, and fit the historical power consumption data to obtain the predicted power consumption of the initial object within the target time period; a third acquisition sub-unit, used to acquire the rated capacity of the initial object within the target time period; and a second determination sub-unit, used to determine potential objects from the initial objects based on the predicted power consumption and the rated capacity.

[0087] Optionally, in the object determination device provided in the embodiment of the present application, the third acquisition subunit includes: a second acquisition module, used to obtain the unit operating capacity of the power system in which the initial object is located; a first determination module, used to determine the day value corresponding to the target time period; and a fifth calculation module, used to calculate based on the unit operating capacity and the day value to obtain the rated capacity.

[0088] Optionally, in the object determination device provided in the embodiment of the present application, the second determination subunit includes: a third acquisition module, used to obtain a preset multiplier coefficient; a sixth calculation module, used to calculate based on the multiplier coefficient and predicted power consumption to obtain a target value; and a second determination module, used to determine potential objects from the initial objects based on the size relationship between the target value and the rated capacity.

[0089] The object determination device includes a processor and a memory. The above-mentioned first determination unit 301, first acquisition unit 302, calculation unit 303 and judgment unit 304 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0090] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and risky users with the risk of electricity theft can be determined by adjusting kernel parameters.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0092] The embodiment of the present invention also provides a system for determining an object, such as Figure 4 As shown, the object determination system includes: a historical matching module 40, a first metering anomaly judgment module 41, a linear regression model construction module 42 and a second metering anomaly judgment module 43. The current monthly maximum demand forecast value is predicted by the historical matching module 40, and the potential object with risk is determined based on the current monthly maximum demand forecast value by the first metering anomaly judgment module 40. A linear regression model is constructed based on historical data by the linear regression model construction module 42, and the monthly power consumption deviation of the current special transformer user (i.e., the potential object mentioned above) is calculated by the linear regression model construction module 42 based on the current power consumption type value, current power factor and current monthly capacity of the potential object, and whether the potential object is a risk object with the risk of electricity theft is determined based on the monthly power consumption deviation by the second metering anomaly judgment module 43.

[0093] An embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, a method for determining an object is implemented.

[0094] An embodiment of the present invention provides a processor, which is used to run a program, wherein a method for determining an execution object is performed when the program is run.

[0095] like Figure 5 As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: determining a potential object based on historical electricity consumption data, wherein the potential object is an object whose probability of abnormal electricity consumption is greater than a preset value; obtaining a target power factor of the potential object and an actual power generation capacity within a target time period; calculating based on the target power factor and the actual power generation capacity to obtain a target electricity consumption deviation value for the potential object; judging whether the potential object is a risk object with abnormal electricity consumption based on the target electricity consumption deviation value, and obtaining a judgment result.

[0096] Optionally, calculating based on the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object includes: determining the target power consumption type of the potential object, and obtaining the target power consumption type value corresponding to the target power consumption type; obtaining a target linear regression model corresponding to the target power consumption type, wherein the target linear regression model is fitted based on the power consumption type value, historical power factor, historical adaptive capacity and historical power consumption deviation value; and calculating based on the target power consumption type value, target power factor and actual power generation capacity through the target linear regression model to obtain the target power consumption deviation value.

[0097] Optionally, before obtaining the target linear regression model corresponding to the target electricity usage type, the method also includes: determining multiple electricity usage types, and digitizing each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; obtaining historical data corresponding to each electricity usage type, wherein the historical data at least includes the electricity usage type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value corresponding to each electricity usage type; and constructing a linear regression model corresponding to each electricity usage type based on the historical data corresponding to each electricity usage type.

[0098] Optionally, the target linear regression model is used to perform calculations based on the target electricity type value, the target power factor and the actual power generation capacity to obtain the target electricity deviation value, including: obtaining the preset electricity type value, the preset power factor and the preset power generation capacity of the potential object within the target time period; calculating the difference between the preset electricity type value and the target electricity type value to obtain a first difference; calculating the difference between the preset power factor and the target power factor to obtain a second difference; calculating the difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; and calculating based on the first difference, the second difference and the third difference through the target linear regression model to obtain the target electricity deviation value.

[0099] Optionally, determining potential objects based on historical electricity consumption data includes: obtaining historical electricity consumption data of an initial object, and fitting based on the historical electricity consumption data to obtain predicted electricity consumption of the initial object within a target time period; obtaining the rated capacity of the initial object within the target time period; and determining potential objects from the initial objects based on the predicted electricity consumption and the rated capacity.

[0100] Optionally, obtaining the rated capacity of the initial object within the target time period includes: obtaining the unit operating capacity of the power system in which the initial object is located; determining a day value corresponding to the target time period; and calculating based on the unit operating capacity and the day value to obtain the rated capacity.

[0101] Optionally, determining potential objects from initial objects based on predicted power consumption and rated capacity includes: obtaining a preset multiplier coefficient; calculating based on the multiplier coefficient and predicted power consumption to obtain a target value; and determining potential objects from initial objects based on a size relationship between the target value and the rated capacity.

[0102] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0103] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: determining a potential object based on historical electricity consumption data, wherein the potential object is an object whose probability of abnormal electricity consumption is greater than a preset value; obtaining a target power factor of the potential object and an actual power generation capacity within a target time period; calculating based on the target power factor and the actual power generation capacity to obtain a target electricity consumption deviation value for the potential object; and judging whether the potential object is a risk object with abnormal electricity consumption based on the target electricity consumption deviation value to obtain a judgment result.

[0104] Optionally, calculating based on the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object includes: determining the target power consumption type of the potential object, and obtaining the target power consumption type value corresponding to the target power consumption type; obtaining a target linear regression model corresponding to the target power consumption type, wherein the target linear regression model is fitted based on the power consumption type value, historical power factor, historical adaptive capacity and historical power consumption deviation value; and calculating based on the target power consumption type value, target power factor and actual power generation capacity through the target linear regression model to obtain the target power consumption deviation value.

[0105] Optionally, before obtaining the target linear regression model corresponding to the target electricity usage type, the method also includes: determining multiple electricity usage types, and digitizing each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; obtaining historical data corresponding to each electricity usage type, wherein the historical data at least includes the electricity usage type value, historical power factor, historical adaptive capacity and historical electricity consumption deviation value corresponding to each electricity usage type; and constructing a linear regression model corresponding to each electricity usage type based on the historical data corresponding to each electricity usage type.

[0106] Optionally, the target linear regression model is used to perform calculations based on the target electricity type value, the target power factor and the actual power generation capacity to obtain the target electricity deviation value, including: obtaining the preset electricity type value, the preset power factor and the preset power generation capacity of the potential object within the target time period; calculating the difference between the preset electricity type value and the target electricity type value to obtain a first difference; calculating the difference between the preset power factor and the target power factor to obtain a second difference; calculating the difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; and calculating based on the first difference, the second difference and the third difference through the target linear regression model to obtain the target electricity deviation value.

[0107] Optionally, determining potential objects based on historical electricity consumption data includes: obtaining historical electricity consumption data of an initial object, and fitting based on the historical electricity consumption data to obtain predicted electricity consumption of the initial object within a target time period; obtaining the rated capacity of the initial object within the target time period; and determining potential objects from the initial objects based on the predicted electricity consumption and the rated capacity.

[0108] Optionally, obtaining the rated capacity of the initial object within the target time period includes: obtaining the unit operating capacity of the power system in which the initial object is located; determining a day value corresponding to the target time period; and calculating based on the unit operating capacity and the day value to obtain the rated capacity.

[0109] Optionally, determining potential objects from initial objects based on predicted power consumption and rated capacity includes: obtaining a preset multiplier coefficient; calculating based on the multiplier coefficient and predicted power consumption to obtain a target value; and determining potential objects from initial objects based on a size relationship between the target value and the rated capacity.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing 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.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising 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.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing 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 implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] 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.

[0116] 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 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.

[0117] 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.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0119] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining an object, characterized in that: include: Determine a potential object based on historical electricity consumption data, wherein the potential object is an object whose probability of abnormal electricity consumption is greater than a preset value; Obtaining a target power factor of the potential object and an actual power generation capacity within a target time period; Calculating according to the target power factor and the actual power generation capacity to obtain a target power consumption deviation value of the potential object; Based on the target power consumption deviation value, it is determined whether the potential object is a risk object with abnormal power consumption, and a determination result is obtained.

2. The method according to claim 1, characterized in that Calculating according to the target power factor and the actual power generation capacity to obtain the target power consumption deviation value of the potential object includes: Determine the target electricity usage type of the potential object, and obtain the target electricity usage type value corresponding to the target electricity usage type; Obtaining a target linear regression model corresponding to the target power consumption type, wherein the target linear regression model is fitted based on the power consumption type value, the historical power factor, the historical adaptive capacity and the historical power consumption deviation value; The target power consumption deviation value is obtained by calculating through the target linear regression model based on the target power consumption type value, the target power factor and the actual power generation capacity.

3. The method according to claim 2, characterized in that Before obtaining the target linear regression model corresponding to the target electricity consumption type, the method further includes: Determine multiple electricity usage types, and perform numerical processing on each electricity usage type to obtain an electricity usage type value corresponding to each electricity usage type; Acquire historical data corresponding to each power consumption type, wherein the historical data at least includes a power consumption type value, a historical power factor, a historical adaptive capacity, and a historical power consumption deviation value corresponding to each power consumption type; Based on the historical data corresponding to each electricity consumption type, a linear regression model corresponding to each electricity consumption type is constructed.

4. The method according to claim 2, characterized in that: The target power consumption deviation value is obtained by calculating the target linear regression model based on the target power consumption type value, the target power factor and the actual power generation capacity, including: Obtaining a preset power consumption type value, a preset power factor, and a preset power generation capacity of the potential object within a target time period; Calculating a difference between the preset power usage type value and the target power usage type value to obtain a first difference; Calculating a difference between the preset power factor and the target power factor to obtain a second difference; Calculating a difference between the actual power generation capacity and the preset power generation capacity to obtain a third difference; The target power consumption deviation value is obtained by performing calculation based on the first difference, the second difference and the third difference through the target linear regression model.

5. The method according to claim 1, characterized in that Based on historical electricity consumption data, potential targets include: Obtaining historical power consumption data of the initial object, and performing fitting based on the historical power consumption data to obtain predicted power consumption of the initial object within a target time period; Obtaining a rated capacity of the initial object within a target time period; The potential object is determined from the initial objects according to the predicted power consumption and the rated capacity.

6. The method according to claim 5, characterized in that Obtaining the rated capacity of the initial object in the target time period includes: Obtaining the unit operating capacity of the power system where the initial object is located; Determine the number of days corresponding to the target time period; The rated capacity is obtained by calculating according to the unit operating capacity and the day value.

7. The method according to claim 5, characterized in that Determining the potential object from the initial objects according to the predicted power consumption and the rated capacity includes: Get the preset magnification factor; Calculating according to the multiplication factor and the predicted power consumption to obtain a target value; The potential object is determined from the initial objects according to the size relationship between the target value and the rated capacity.

8. A device for determining an object, characterized in that: include: A first determining unit is used to determine a potential object based on historical power consumption data, wherein the potential object is an object whose probability of abnormal power consumption is greater than a preset value; A first acquisition unit, configured to acquire a target power factor of the potential object and an actual power generation capacity within a target time period; a calculation unit, configured to calculate according to the target power factor and the actual power generation capacity to obtain a target power consumption deviation value of the potential object; The judgment unit is used to judge whether the potential object is a risk object with abnormal power consumption according to the target power consumption deviation value, and obtain a judgment result.

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

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the object determination method described in any one of claims 1 to 7.

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