Method and device for generating electricity consumption portrait of single-phase intelligent electric energy meter

Abstract

The invention provides a method and device for generating a power consumption portrait of a single-phase intelligent electric energy meter, and the method comprises the steps: carrying out the shape comparison of the power consumption condition of a target single-phase intelligent electric energy meter and the power consumption condition of each preset power consumption portrait, reducing the impact on the similarity from a numerical value, and improving the accuracy of the power consumption portrait generation. Therefore, the preset power consumption portrait corresponding to the highest similarity in the change similarities corresponding to the target single-phase intelligent electric energy meter and the preset power consumption portraits is used as the power consumption portrait of the target single-phase intelligent electric energy meter, the power consumption portrait of the user can be relatively accurately determined through the method, and a recommendation basis is provided for subsequent product recommendation.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a method and apparatus for generating an electricity consumption profile of a single-phase smart energy meter. Background Technology

[0002] In the power industry's journey towards digitalization and refinement, user electricity consumption profiles are core assets for achieving precise marketing and in-depth data insights. Faced with an increasingly diversified product system (such as peak-valley electricity pricing, electric vehicle charging discounts, and smart home packages), various business scenarios place differentiated demands on the dimensions and accuracy of user profiles. Therefore, building accurate user profiles that can dynamically adapt to different business needs has become a key challenge for the industry to improve operational efficiency and service quality. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method and apparatus for generating an electricity consumption profile of a single-phase smart energy meter, so as to accurately obtain the user's electricity consumption profile.

[0004] In a first aspect, embodiments of this application provide a method for generating an electricity consumption profile of a single-phase smart energy meter, the method comprising: Obtain the power collected by the target single-phase smart energy meter according to the preset collection period and the timestamp corresponding to that power; After determining the power for the preset number of days based on the timestamp, the first power average for each hour within the preset number of days is calculated for that hour. Calculate the first difference between the first power mean values ​​of adjacent hourly time intervals; Based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile in each hourly time period, the similarity of the changes between the first difference and the second difference is determined; After obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, the preset electricity consumption profile corresponding to the highest similarity is determined, so as to use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter.

[0005] Secondly, embodiments of this application provide an apparatus for generating an electricity consumption profile of a single-phase smart energy meter, the apparatus comprising: The acquisition unit is used to acquire the power collected by the target single-phase smart energy meter according to a preset acquisition cycle and the timestamp corresponding to the power. The first calculation unit is used to calculate the first power average value of the preset number of days for each hourly time period within the preset number of days after determining the power obtained according to the timestamp. The second calculation unit is used to calculate the first difference between the first power average values ​​of adjacent hourly time periods; The third calculation unit is used to determine the similarity of changes between the first difference and the second difference based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile in each hourly time period. The determining unit is used to determine the preset electricity consumption profile corresponding to the highest similarity after obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, so as to use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter.

[0006] The technical solution provided in this application includes, but is not limited to, the following beneficial effects: In this application, after obtaining the power of the target single-phase smart energy meter for a preset number of days, the first power average value is calculated for each hourly time period during the day. The first power average value can reduce data fluctuations caused by sudden changes in user electricity consumption. Then, the first difference between the first power average values ​​of adjacent hourly time periods is calculated. The first difference can characterize the slope change of the user's electricity consumption curve, which facilitates subsequent shape comparison and reduces the influence of numerical values ​​on similarity. Then, the change between the first difference and the second difference corresponding to the slope change of the electricity consumption curve corresponding to the target preset electricity consumption profile is used to determine the change similarity between the first difference and the second difference. The preset electricity consumption profile corresponding to the highest similarity among the change similarities between the target single-phase smart energy meter and each preset electricity consumption profile is taken as the electricity consumption profile of the target single-phase smart energy meter. Through the above method, the user's electricity consumption profile can be determined relatively accurately, thereby providing a recommendation basis for subsequent product recommendations.

[0007] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart illustrating a method for generating an electricity consumption profile for a single-phase smart energy meter, as provided in an embodiment of this application. Figure 2 A flowchart illustrating another method for generating an electricity consumption profile for a single-phase smart energy meter provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a device for generating an electricity consumption profile of a single-phase smart energy meter, as provided in an embodiment of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0011] It should be noted in advance that single-phase smart meters are generally used in households, so one single-phase smart meter represents one household. Since single-phase smart meters are bound to users, one single-phase smart meter also represents one user. Therefore, single-phase smart meters, households, and users are equivalent descriptions.

[0012] Figure 1 This is a flowchart illustrating a method for generating an electricity consumption profile for a single-phase smart energy meter, as provided in an embodiment of this application. Figure 1 As shown, the generation method includes the following steps: Step 101: Obtain the power collected by the target single-phase smart energy meter according to the preset collection cycle and the timestamp corresponding to the power.

[0013] Step 102: After determining the power for the preset number of days based on the timestamp, calculate the first power average for each hour within the preset number of days.

[0014] Step 103: Calculate the first difference between the first power mean values ​​of adjacent hourly time periods.

[0015] Step 104: Determine the similarity of changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile at each hourly time period.

[0016] Step 105: After obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, determine the preset electricity consumption profile corresponding to the highest similarity, and use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter.

[0017] Specifically, in order to reduce the impact of excessive or insufficient power consumption by users during a certain period and to mitigate the effects of power fluctuations, it is necessary to acquire a certain amount of power. Each power data point is timestamped during acquisition. Therefore, power data for a preset number of days (e.g., three days) can be selected based on the timestamps. Then, the average power value for each hour within the preset number of days can be calculated. For example, to acquire power data for three days, since there are 24 hours in a day, the average power values ​​for the first day (0:00-1:00), the second day (0:00-1:00), and the third day (0:00-1:00) can be calculated to obtain the average power value for the hourly time period (0:00-1:00). Similarly, the average power value for the hourly time period (1:00-2:00) can be obtained, and so on, to obtain the average power value for 24 hourly time periods.

[0018] After obtaining the first power mean for each hourly time period within a day, the first difference between the first power mean of adjacent hourly time periods is calculated. This allows us to determine the slope of the change between adjacent hourly time periods. Furthermore, the first difference can represent the slope of the power curve between adjacent hourly time periods in the power curve formed by the first power mean of each hourly time period.

[0019] This application also requires pre-setting the second power average value of each electricity consumption profile for each hourly time period. For each pre-set electricity consumption profile, the second difference between adjacent hourly time periods is determined. The pre-set multiple electricity consumption profiles include, but are not limited to, electricity consumption profiles of peak-hour working households, intermittent low-consumption households, stable-staying households, night-shift households, vacant houses, and households with abnormal electricity consumption. Taking the electricity consumption profile of peak-hour working households as an example, the second power average value for each hourly time period includes: the second power average value from 0:00 to 1:00, the second power average value from 1:00 to 2:00, up to the second power average value from 23:00 to 24:00.

[0020] Then, the curves of the sequences corresponding to the first and second differences are compared to determine the changes between them, i.e., the similarity of the curve shape changes. This yields the similarity between the first and second differences. Once the similarity between the target single-phase smart energy meter and each preset electricity consumption profile is obtained, the preset electricity consumption profile corresponding to the highest similarity can be determined. This indicates that the electricity consumption pattern of the preset electricity consumption profile corresponding to the highest similarity is the same as the electricity consumption pattern of the user corresponding to the target single-phase smart energy meter, avoiding the influence of the magnitude of the electricity consumption value on the similarity. For example, if a user's electricity consumption is much higher than that of a preset electricity consumption profile, but the shapes of their electricity consumption curves are similar, the method compares the slope changes of the curves, not the original electricity consumption values. The focus on the slope changes of the curves is when the electricity consumption starts to rise and when it reaches its peak and begins to decline, etc. It is not sensitive to the absolute value of the electricity consumption and has strong noise resistance. Finally, the preset electricity consumption profile most similar to the user's electricity consumption curve change can be determined, thus obtaining the electricity consumption profile of the target single-phase smart energy meter.

[0021] For example, the user corresponding to the target single-phase smart meter is user A, and the preset electricity consumption profile is the electricity consumption profile of a peak-hour working family, hereinafter referred to as user B. The second power average of user B over 24 hourly time periods is known. After obtaining the first power average of user A over 24 hourly time periods, the first difference is calculated for the first power average of user A in adjacent hourly time periods, resulting in 23 first difference sequences. Similarly, the second difference is calculated for the second power average of user B in adjacent hourly time periods, resulting in 23 second difference sequences. Then, the changes in these 23 first difference sequences and 23 second difference sequences are compared and analyzed to determine the similarity of changes between user A and user B. After obtaining the similarity of changes between user A and multiple user Bs, the electricity consumption profile of user B with the highest similarity is determined as the electricity consumption profile of user A.

[0022] In this application, after obtaining the power of the target single-phase smart energy meter for a preset number of days, the first power average value is calculated for each hourly time period during the day. The first power average value can reduce data fluctuations caused by sudden changes in user electricity consumption. Then, the first difference between the first power average values ​​of adjacent hourly time periods is calculated. The first difference can characterize the slope change of the user's electricity consumption curve, which facilitates subsequent shape comparison and reduces the influence of numerical values ​​on similarity. Then, the change between the first difference and the second difference corresponding to the slope change of the electricity consumption curve corresponding to the target preset electricity consumption profile is used to determine the change similarity between the first difference and the second difference. The preset electricity consumption profile corresponding to the highest similarity among the change similarities between the target single-phase smart energy meter and each preset electricity consumption profile is taken as the electricity consumption profile of the target single-phase smart energy meter. Through the above method, the user's electricity consumption profile can be determined relatively accurately, thereby providing a recommendation basis for subsequent product recommendations.

[0023] In a feasible implementation plan Figure 2 This is a flowchart illustrating another method for generating an electricity consumption profile for a single-phase smart energy meter provided in this application embodiment. During step 104, as follows... Figure 2 As shown, the method includes the following steps: Step 201: Based on the Euclidean distance between each first difference and each second difference, construct a distance matrix in chronological order.

[0024] Step 202: Calculate the cost matrix according to Formula 1: Formula 1 in, Indicates the position of the distance matrix at the th position. Line number The first element of the column, This represents the second element in the distance matrix that is directly above the first element. This represents the third element in the distance matrix that is adjacent to the left of the first element. The fourth element in the distance matrix is ​​located directly above the third element. The boundary conditions of the cost matrix are: , , .

[0025] Step 203: Based on the position of each element in the cost matrix, determine each cost path from the last element to the first element in the cost matrix.

[0026] Step 204: For each cost path, calculate the sum of the elements on that cost path.

[0027] Step 205: Determine the first minimum sum from the sums corresponding to each cost path, and use the first minimum sum as the change similarity.

[0028] For example, when the first difference includes A1 and A2, and the second difference includes B1, B2, and B3, we calculate the Euclidean distance between A1 and B1, B2, and B3, and the Euclidean distance between A2 and B1, B2, and B3, respectively. This results in a 2x3 distance matrix. Each Euclidean distance can be used as an element in the distance matrix, so the distance matrix contains 6 elements, as follows:

[0029] The distance matrix is ​​as follows:

[0030] Then, calculate the cost matrix according to Formula 1, where, , , The other elements in the cost matrix are determined according to Formula 1. During the calculation, we can start with the distance matrix... Initially, the cost matrix is ​​gradually filled in, thus obtaining a cost matrix with the same number of rows and columns as the distance matrix.

[0031] Any element in the cost matrix This represents the minimum cumulative distance (cost) obtained after optimal alignment from the first i differences in the sequence of the first difference to the first j differences in the sequence of the second difference. For example, the sequence of the first difference is A', which includes 3 and 2, and the sequence of the second difference is B', which includes 2, 1, and 4. The resulting cost matrix is ​​as follows:

[0032] D(1,1)=1; The corresponding subsequences are: the first point of A': [3], the first point of B': [2]; D(1,1) represents the minimum cost of aligning the two single nodes [3] and [2]. Since there is only one pair of nodes, the cost is their local distance d(1,1) = 1. This path is unique and deterministic.

[0033] D(1,2)=5; The corresponding subsequences: the first point of A': [3], the first two points of B': [2,1]; D(1,2) represents the minimum cost of aligning [3] with [2,1]. Since A' has only one point, it must match multiple points of B'. The path can only be (1,1)->(1,2). The total cost = cost of matching (1,1) + cost of matching (1,2) = d(1,1) + d(1,2) = 1 + 4 = 5. This cost is high (5) because a point 3 of A' must simultaneously explain the changing trend of two points of B' (2->1), which is difficult to match perfectly.

[0034] D(1,3)=6; The corresponding subsequences: the first point of A': [3], the first 3 points of B': [2,1,4]; D(1,3) represents the minimum cost of aligning [3] with [2,1,4], with the path (1,1)->(1,2)->(1,3). The total cost is d(1,1)+d(1,2)+d(1,3)=1+4+1=6. The cost is higher (6) because it is more difficult to match the trend of three points with one point.

[0035] D(2, 2) = 2; The corresponding subsequences are: the first two points of A': [3,2], the first two points of B': [2,1]; This indicates that this is the first cell with a selection. It calculates the minimum cost among all possible ways to align [3,2] and [2,1].

[0036] Path 1 (↖): (1,1)->(2,2), cost = D(1,1)+d(2,2)=1+1= 2; Path 2 (↑): (1,1)->(1,2)->(2,2), cost = D(1,2)+d(2,2)=5+1=6; Path 3(←):(1,1)->(2,1)->(2,2), cost = D(2,1)+d(2,2)=1+1=2; min(2,6,2)=2; The cost of the optimal path is 2. This means that the sequences [3,2] and [2,1] have some similarity in shape, but are not completely identical.

[0037] D(2,3)=6; The corresponding subsequences: the first 2 points of A': [3,2] (the entire sequence A'), the first 3 points of B': [2,1,4] (the entire sequence B'); This indicates that this is the ultimate goal, which represents the minimum cumulative cost after optimal dynamic regularization of the entire sequence A' and the entire sequence B'. It is calculated by D(2,3)= d(2,3)+ min( D(1,3),D(2,2), D(1,2))=4+min(6,2,5)=4+2=6.

[0038] The value 6 represents the final distance between two complete sequences. It is composed of the optimal solution to a previous subproblem (D(2,2)=2) plus the matching cost of the current point (d(2,3)=4). The magnitude of this value itself is not important; what matters is which template calculates this value smaller when comparing multiple templates.

[0039] The cost matrix filling process solves all subproblems from the bottom up. The value of each cell D(i,j) depends solely on the optimal solutions of the three subproblems to its left (i,j-1), above it (i-1,j), and to its top-left (i-1,j-1). The final answer sought by the entire algorithm is the value of the bottom-right element of the cumulative cost matrix D. It contains information about the optimal solutions of all previous subproblems, ultimately converging into the global optimum.

[0040] After obtaining the cost matrix, it is necessary to backtrack to find the optimal normalization path. Specifically, backtracking is performed from the last element in the cost matrix (usually located in the lower right corner of the matrix) to the first element in the distance matrix (usually located in the upper left corner of the matrix) to find a path with the minimum total cumulative cost. The smaller the cumulative cost of the path, the minimum total cost required to optimally align the shapes of the two original curves. The smaller the value, the more similar the shapes of the two curves are. Therefore, the first minimum sum value, i.e., the change similarity, can be determined by the above method.

[0041] In a feasible implementation, when performing step 105, after obtaining the first minimum sum value corresponding to each preset electricity consumption profile of the target single-phase smart energy meter, a second minimum sum value can be selected from the first minimum sum value corresponding to each preset electricity consumption profile, so that the preset electricity consumption profile corresponding to the second minimum sum value is used as the electricity consumption profile of the target single-phase smart energy meter.

[0042] Specifically, for each preset electricity consumption profile, the first minimum sum of the preset electricity consumption profile and the target single-phase smart meter represents the similarity between the electricity consumption of the target single-phase smart meter and the preset profile. The second minimum sum determined by comparing each first minimum sum represents the electricity consumption of the target single-phase smart meter that is most similar to the electricity consumption of the user profile corresponding to the second minimum sum. Therefore, the preset electricity consumption profile corresponding to the second minimum sum can be determined as the electricity consumption profile of the target single-phase smart meter. In this way, the preset electricity consumption profile that is most similar to the electricity consumption of the target single-phase smart meter can be selected from multiple preset electricity consumption profiles, so that the obtained electricity consumption profile of the target single-phase smart meter has high accuracy.

[0043] In a feasible implementation, when performing the step of constructing the distance matrix according to the Euclidean distance between each first difference and each second difference in chronological order, the distance matrix can first be calculated according to the following formula 2. Line number Column elements: Formula 2 in, The first difference, This is the second difference; Then, the obtained elements are arranged in chronological order to obtain the distance matrix.

[0044] In one feasible implementation, when there are multiple recommended contents, the recommended contents configured for the user profile of the target single-phase smart meter are sent to the mobile device bound to the target single-phase smart meter, which can improve the recommendation hit rate.

[0045] Figure 3 This is a schematic diagram of the structure of a device for generating an electricity consumption profile of a single-phase smart energy meter, as provided in an embodiment of this application. Figure 3 As shown, the device includes: Acquisition unit 31 is used to acquire the power collected by the target single-phase smart energy meter according to a preset acquisition cycle and the timestamp corresponding to the power; The first calculation unit 32 is used to calculate the first power average value of the preset number of days for each hourly time period within the preset number of days after determining the power obtained according to the timestamp. The second calculation unit 33 is used to calculate the first difference between the first power average values ​​of adjacent hourly time periods; The third calculation unit 34 is used to determine the similarity of changes between the first difference and the second difference based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile in each hourly time period. The determining unit 35 is used to determine the preset electricity consumption profile corresponding to the highest similarity after obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, so as to use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter.

[0046] In a feasible implementation, when the third calculation unit determines the similarity of changes between the first difference and the second difference based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile at each hourly time period, the calculation unit includes: Based on the Euclidean distance between each first difference and each second difference, construct a distance matrix in chronological order. Calculate the cost matrix using the following formula: ; in, Indicates the position of the distance matrix at the th position. Line number The first element of the column, This represents the second element in the distance matrix that is directly above the first element. This represents the third element in the distance matrix that is adjacent to the left of the first element. The fourth element in the distance matrix is ​​located directly above the third element. The boundary conditions of the cost matrix are: , , ; Based on the position of each element in the cost matrix, determine each cost path starting from the last element and ending at the first element in the cost matrix; For each cost path, calculate the sum of the elements on that cost path; The first minimum sum is determined from the sums corresponding to each cost path, and the first minimum sum is used as the change similarity.

[0047] In a feasible implementation, the third calculation unit is used to determine the preset electricity consumption profile corresponding to the highest similarity after obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, so that when using the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter, the following is included: After obtaining the first minimum sum value corresponding to each preset electricity consumption profile of the target single-phase smart energy meter, a second minimum sum value is selected from the first minimum sum value corresponding to each preset electricity consumption profile, so that the preset electricity consumption profile corresponding to the second minimum sum value is used as the electricity consumption profile of the target single-phase smart energy meter.

[0048] In one feasible implementation, the third calculation unit, when constructing a distance matrix based on the Euclidean distances between the first and second differences in chronological order, includes: The distance matrix is ​​calculated using the following formula. Line number Column elements: ; in, The first difference, This is the second difference; The elements are arranged in chronological order to obtain the distance matrix.

[0049] In one feasible implementation, the device further includes: The recommendation unit is used to send recommended content configured for the user profile of the target single-phase smart energy meter to the mobile device bound to the target single-phase smart energy meter.

[0050] about Figure 3 For explanations of the principles behind the relevant content, please refer to... Figure 1 and Figure 2 The relevant explanations will not be elaborated here.

[0051] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0052] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0054] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0056] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for generating an electricity consumption profile for a single-phase smart energy meter, characterized in that, The method includes: Obtain the power collected by the target single-phase smart energy meter according to the preset collection period and the timestamp corresponding to that power; After determining the power for the preset number of days based on the timestamp, the first power average for each hour within the preset number of days is calculated for that hour. Calculate the first difference between the first power mean values ​​of adjacent hourly time intervals; Based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile in each hourly time period, the similarity of the changes between the first difference and the second difference is determined; After obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, the preset electricity consumption profile corresponding to the highest similarity is determined, so as to use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter.

2. The generation method as described in claim 1, characterized in that, The step of determining the similarity of changes between the first difference and the second difference based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile at each hourly time period includes: Based on the Euclidean distance between each first difference and each second difference, construct a distance matrix in chronological order. Calculate the cost matrix using the following formula: ； in, Indicates the position of the distance matrix at the th position. Line number The first element of the column, This represents the second element in the distance matrix that is directly above the first element. This represents the third element in the distance matrix that is adjacent to the left of the first element. The fourth element in the distance matrix is ​​located directly above the third element. The boundary conditions of the cost matrix are: , , ; Based on the position of each element in the cost matrix, determine each cost path starting from the last element and ending at the first element in the cost matrix; For each cost path, calculate the sum of the elements on that cost path; The first minimum sum is determined from the sums corresponding to each cost path, and the first minimum sum is used as the change similarity.

3. The generation method as described in claim 2, characterized in that, The step of determining the preset electricity consumption profile corresponding to the highest similarity after obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, and using the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter, includes: After obtaining the first minimum sum value corresponding to each preset electricity consumption profile of the target single-phase smart energy meter, a second minimum sum value is selected from the first minimum sum value corresponding to each preset electricity consumption profile, so that the preset electricity consumption profile corresponding to the second minimum sum value is used as the electricity consumption profile of the target single-phase smart energy meter.

4. The generation method as described in claim 2, characterized in that, The step of constructing a distance matrix based on the Euclidean distance between each first difference and each second difference, in chronological order, includes: The distance matrix is ​​calculated using the following formula. Line number Column elements: ； in, The first difference, This is the second difference; The elements are arranged in chronological order to obtain the distance matrix.

5. The generation method as described in claim 1, characterized in that, The method further includes: Send recommended content configured for the user profile of the target single-phase smart energy meter to the mobile device bound to the target single-phase smart energy meter.

6. A device for generating an electricity consumption profile for a single-phase smart energy meter, characterized in that, The device includes: The acquisition unit is used to acquire the power collected by the target single-phase smart energy meter according to a preset acquisition cycle and the timestamp corresponding to the power. The first calculation unit is used to calculate the first power average value of the preset number of days for each hourly time period within the preset number of days after determining the power obtained according to the timestamp. The second calculation unit is used to calculate the first difference between the first power average values ​​of adjacent hourly time periods; The third calculation unit is used to determine the similarity of changes between the first difference and the second difference based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile in each hourly time period. The determining unit is used to determine the preset electricity consumption profile corresponding to the highest similarity after obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, so as to use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter.

7. The generating apparatus as described in claim 6, characterized in that, The third calculation unit is used to determine the similarity of changes between the first difference and the second difference based on the changes between the first difference and the second difference corresponding to the second power average of the target preset electricity consumption profile in each hourly time period, including: Based on the Euclidean distance between each first difference and each second difference, construct a distance matrix in chronological order. Calculate the cost matrix using the following formula: ； in, Indicates the position of the distance matrix at the th position. Line number The first element of the column, This represents the second element in the distance matrix that is directly above the first element. This represents the third element in the distance matrix that is adjacent to the left of the first element. The fourth element in the distance matrix is ​​located directly above the third element. The boundary conditions of the cost matrix are: , , ; Based on the position of each element in the cost matrix, determine each cost path starting from the last element and ending at the first element in the cost matrix; For each cost path, calculate the sum of the elements on that cost path; The first minimum sum is determined from the sums corresponding to each cost path, and the first minimum sum is used as the change similarity.

8. The generating apparatus as claimed in claim 7, characterized in that, The third calculation unit is used to determine the preset electricity consumption profile corresponding to the highest similarity after obtaining the change similarity between the target single-phase smart energy meter and each preset electricity consumption profile, so as to use the preset electricity consumption profile corresponding to the highest similarity as the electricity consumption profile of the target single-phase smart energy meter, including: After obtaining the first minimum sum value corresponding to each preset electricity consumption profile of the target single-phase smart energy meter, a second minimum sum value is selected from the first minimum sum value corresponding to each preset electricity consumption profile, so that the preset electricity consumption profile corresponding to the second minimum sum value is used as the electricity consumption profile of the target single-phase smart energy meter.

9. The generating apparatus as claimed in claim 7, characterized in that, The third calculation unit, when constructing a distance matrix based on the Euclidean distances between each first difference and each second difference in chronological order, includes: The distance matrix is ​​calculated using the following formula. Line number Column elements: ； in, The first difference, This is the second difference; The elements are arranged in chronological order to obtain the distance matrix.

10. The generating apparatus as claimed in claim 6, characterized in that, The device further includes: The recommendation unit is used to send recommended content configured for the user profile of the target single-phase smart energy meter to the mobile device bound to the target single-phase smart energy meter.

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