A method for abnormal warning of electric energy consumption data

Abstract

The present invention provides a method for early warning of abnormal power consumption data, including: establishing a spatial association relationship for the electric meters within an enterprise; obtaining the real-time data of each electric meter through an intelligent collector and reporting it to a platform; judging whether the real-time data is abnormal power consumption data according to a preset index threshold; if so, taking the difference between the electric energy indication of the electric meter in this period and the electric energy indication of the electric meter in the previous period to obtain the electricity consumption of the electric meter in this period; obtaining the abnormal probability of non-fixed services according to the spatial association relationship of the electric meter and the electricity consumption in this period; and performing hierarchical abnormal warning on the power consumption data according to the abnormal probability of non-fixed services. Compared with the prior art, this method improves the accuracy of abnormal determination, reduces misjudgment, and can remind enterprise users to check for abnormalities in time and correct the data.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a method for abnormal warning of power consumption data. Background Art

[0002] With the refined management of enterprise power consumption, the accuracy of data is particularly important in the process of collecting power consumption data. Large enterprise organizations are widely distributed. If unified management of enterprise energy consumption is required, the operation stability of smart meters needs to be ensured first. The traditional method of regularly overhauling smart meters takes a lot of time and has a high operation and maintenance cost for unified operation and maintenance management of enterprises. Therefore, there is an urgent need for a method that can remotely and real-time analyze power consumption data collected by smart meters and determine abnormalities. Summary of the Invention

[0003] Object of the Invention: The technical problem to be solved by the present invention is to provide a method for abnormal warning of power consumption data to overcome the deficiency in the method for determining abnormalities in power consumption data reported by smart meters, including the following steps:

[0004] Step 1, establish a spatial association relationship for the electric meters within the enterprise, where the spatial association relationship includes the enterprise location, building, floor, and room to which each electric meter belongs;

[0005] Step 2, obtain the real-time data of each electric meter through a smart collector and report the real-time data to the platform according to a preset reporting frequency; specifically, in the present invention, the preset period for reporting the real-time data can be set to 1 hour or can be flexibly adjusted according to actual situations.

[0006] Step 3, after the platform receives the real-time data, determine whether the real-time data is abnormal power consumption data according to a preset index threshold; if so, store the abnormal information corresponding to the abnormal power consumption data and end the determination; if not, execute Step 4;

[0007] Step 4, the platform subtracts the power indication of the electric meter in this period from the power indication of the electric meter in the previous period to obtain the electricity consumption of this period of the electric meter;

[0008] Step 5, obtain the abnormal probability of non-fixed services according to the spatial association relationship and the electricity consumption of this period of the electric meter;

[0009] Step 6, perform hierarchical abnormal warning on the power consumption data according to the abnormal probability of non-fixed services.

[0010] Further, in one implementation, Step 3 includes:

[0011] Step 3.1, obtain the current, voltage, and power of the electric meter in the real-time data;

[0012] Step 3.2, determine whether the current, voltage, and electric energy of the electric meter are respectively within the preset index thresholds. In the platform, each electric meter corresponds to a preset index threshold of the electric meter one by one. The preset index threshold of the electric meter includes a preset voltage index threshold, a preset current index threshold, and a preset electric energy index threshold;

[0013] Step 3.3, if any one of the current, voltage, and electric energy in the real-time data exceeds the preset index threshold, determine that the real-time data is abnormal electric energy consumption data, store the abnormal information corresponding to the abnormal electric energy consumption data, and end the judgment; specifically, in the present invention, if any one of the current, voltage, and electric energy in the real-time data is greater than the preset index threshold, it is impossible to determine that the real-time data reported in this period is accurate data, so it belongs to abnormal electric energy consumption data. Specifically, in this step, the threshold can be an upper limit or a lower limit. Therefore, when comparing the real-time data with the preset index threshold, it is possible that the real-time data is abnormal electric energy consumption data when it is greater than the preset index threshold, and it is also possible that the real-time data is abnormal electric energy consumption data when it is less than the preset index threshold.

[0014] Step 3.4, if each of the current, voltage, and electric energy in the real-time data does not exceed the preset index threshold, execute step 4.

[0015] Further, in one implementation, step 5 includes:

[0016] Step 5.1, find the space window where the electric meter is located. The space window is all the rooms determined according to the spatial association relationship of the electric meters. Obtain the current period electricity consumption of all the electric meters in the space window, and calculate the spatial association dispersion degree of the current period electricity consumption of all the electric meters in the space window, that is, obtain the first abnormal probability parameter p1;

[0017] Step 5.2, select a time window, obtain the electricity consumption of each period of the electric meter within a continuous period of time corresponding to the time window. Assume that the total number of calculated periods is N, and calculate the change trend fluctuation ratio, that is, obtain the second abnormal probability parameter p2; specifically, in the present invention, the total number of calculated periods N is a value calculated according to the time window length and the electric meter reporting frequency. For example, if the time window length is set to 12 hours and the electric meter reporting frequency is once an hour, then the total number of calculated periods N is 12. In addition, the time window can be set to 12 hours or adjusted flexibly according to the actual situation.

[0018] Step 5.3: Obtain a number of historical time series, that is, the historical time series data of the previous day, previous week, previous month, and previous year of the electricity meter, which are consecutive N periods in the same time period as the time window in Step 5.2. Calculate the similarity between each piece of historical time series data and the time series data in Step 5.2, and sort each calculated similarity to obtain the highest similarity, that is, obtain the third abnormal probability parameter p3.

[0019] Step 5.4: Define a predicted value, compare the electricity value of this period of the electricity meter, that is, the electricity value in the real-time data, with the predicted value, calculate the fluctuation ratio, and obtain the fourth abnormal probability parameter p4.

[0020] Step 5.5: Add the abnormal probability parameter p1, the second abnormal probability parameter p2, the third abnormal probability parameter p3, and the fourth abnormal probability parameter p4 weighted to obtain the abnormal probability of the non-fixed service.

[0021] Further, in one implementation, the spatial window of the electricity meter is determined according to the spatial association relationship of the electricity meter. The spatial window includes:

[0022] The spatial window determined by the building, that is, the spatial window is all the rooms in the same building in the location of the same enterprise to which the electricity meter belongs.

[0023] The spatial window determined by the floor, that is, the spatial window is all the rooms on the same floor in the same building in the location of the same enterprise to which the electricity meter belongs.

[0024] Other spatial windows, that is, in addition to the spatial window determined by the building and the spatial window determined by the floor, the spatial window is flexibly determined according to the location of the enterprise to which the electricity meter belongs, the building, the floor, and the room.

[0025] Further, in one implementation, the calculation method of the spatial association dispersion degree in Step 5.1 is as follows:

[0026] Spatial association dispersion degree = Math.abs(this electricity meter's electricity consumption in this period - median of electricity consumption of all electricity meters in the spatial window) / (maximum electricity consumption of all electricity meters in the spatial window - minimum electricity consumption of all electricity meters in the spatial window).

[0027] Further, in one implementation, the calculation method of the change trend fluctuation ratio in Step 5.2 is as follows:

[0028] Change trend fluctuation ratio = Math.abs (this meter's electricity consumption in this period - this meter's electricity consumption in the previous period) / this meter's electricity consumption in the previous period - the median of this meter's electricity growth rate within the time window) / (the maximum electricity growth rate of this meter within the time window - the minimum electricity growth rate of this meter within the time window).

[0029] Furthermore, in one implementation, the calculation method of the fluctuation ratio in step 5.4 is:

[0030] Fluctuation ratio = Math.abs (current period electricity consumption - predicted value) / Math.max (predicted value, current period electricity consumption). Specifically, in the present invention, the predicted value is currently obtained from the existing software platform configuration, and the source and calculation of the predicted value are not limited by this method.

[0031] It can be seen from the above technical scheme that the present invention provides a method for abnormal warning of electric energy consumption data, including: step 1, establishing a spatial association relationship for electric meters within an enterprise, the spatial association relationship including the location of the enterprise, building, floor and room to which each electric meter belongs; step 2, obtaining real-time data of each electric meter through an intelligent collector, and reporting the real-time data to a platform according to a preset reporting frequency; step 3, after the platform receives the real-time data, judging whether the real-time data is abnormal electric energy consumption data according to a preset indicator threshold; if so, storing the abnormal information corresponding to the abnormal electric energy consumption data and ending the judgment; if not, executing step 4; step 4, the platform subtracts the electric energy indication of the electric meter in this cycle from the electric energy indication of the previous cycle to obtain the current electricity consumption of the electric meter; step 5, obtaining the probability of abnormal non-fixed business according to the spatial association relationship and the current electricity consumption of the electric meter; step 6, performing a graded abnormal warning of electric energy consumption data according to the abnormal probability of non-fixed business.

[0032] In the prior art, the traditional method of regular maintenance of smart meters is time-consuming, and the operation and maintenance costs are also high for the unified operation and maintenance management of enterprises. However, the above method is adopted. On the premise of spatial classification of meters, after obtaining the multi-index data reported by smart meters, it is judged by basic business rules. Before the data is persisted, it is based on the spatial discreteness of the current electricity consumption of the meter, the fluctuation ratio of the electricity consumption trend in continuous time, and the similarity of historical time series data. The predicted value is introduced to calculate the fluctuation ratio and abnormal probability to improve the accuracy of abnormal judgment, reduce misjudgment, and remind enterprise users to check abnormalities in time and correct data. Therefore, compared with the prior art, the present invention improves the efficiency of abnormal judgment of electric energy consumption data and reduces the operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solution of the present invention, the following will briefly introduce the attached drawings required in the embodiments. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is a schematic diagram of the working process of a method for early warning of abnormal power consumption data provided in part of the embodiments of the present invention. Specific embodiments

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0036] The embodiments of the present invention disclose a method for early warning of abnormal power consumption data, and this method is applied to internal energy consumption monitoring and metering in enterprises. Enterprises need to carry out refined management of power consumption. In the case of data failure and abnormality, it will lead to incorrect judgments on energy consumption analysis and control. Through this solution, the abnormality of power consumption can be detected in a timely manner, and the accuracy of enterprise energy consumption monitoring can be improved.

[0037] This embodiment provides a method for early warning of abnormal power consumption data, including the following steps:

[0038] Step 1, establish a spatial association relationship for the electric meters within the enterprise, and the spatial association relationship includes the enterprise location, building, floor, and room to which each electric meter belongs;

[0039] Step 2, obtain the real-time data of each electric meter through an intelligent collector and report the real-time data to the platform according to a preset reporting frequency; specifically, in this embodiment, the model of the specific intelligent collector is not limited, as long as it can achieve the functions required by the present invention. And the preset period for reporting the real-time data can be set to 1 hour, or it can be flexibly adjusted according to the actual situation.

[0040] Step 3, after the platform receives the real-time data, judge whether the real-time data is abnormal power consumption data according to the preset index threshold; if so, store the abnormal information corresponding to the abnormal power consumption data and end the judgment; if not, execute Step 4;

[0041] Step 4, the platform subtracts the electric energy reading of this period of the electric meter from the electric energy reading of the previous period of the electric meter to obtain the electricity consumption of this period of the electric meter;

[0042] Step 5, obtain the abnormal probability of non-fixed services according to the spatial association relationship and the electricity consumption of this period of the electric meter;

[0043] Step 6, perform hierarchical abnormal early warning of power consumption data according to the abnormal probability of non-fixed services.

[0044] In a method for determining abnormal power consumption data provided in this embodiment, step 3 includes:

[0045] Step 3.1: Obtain the current, voltage, and electric energy of the electric meter in the real-time data.

[0046] Step 3.2: Determine whether the current, voltage, and electric energy of the electric meter are respectively within the preset index thresholds. In the platform, each electric meter corresponds to a preset index threshold of the electric meter one by one. The preset index threshold of the electric meter includes a preset voltage index threshold, a preset current index threshold, and a preset electric energy index threshold.

[0047] Step 3.3: If any one of the current, voltage, and electric energy in the real-time data exceeds the preset index threshold, determine that the real-time data is abnormal power consumption data, store the abnormal information corresponding to the abnormal power consumption data, and end the judgment. Specifically, in this embodiment, if any one of the current, voltage, and electric energy in the real-time data exceeds the preset index threshold, it is impossible to determine that the real-time data reported in this period is accurate data. Therefore, it belongs to abnormal power consumption data.

[0048] Step 3.4: If each of the current, voltage, and electric energy in the real-time data does not exceed the preset index threshold, then execute step 4.

[0049] In a method for determining abnormal power consumption data provided in this embodiment, step 5 includes:

[0050] Step 5.1: Locate the space window where the electric meter is located. The space window is all the rooms determined according to the spatial association relationship of the electric meters. Obtain the current period electricity consumption of all the electric meters within the space window, and calculate the spatial association dispersion degree of the current period electricity consumption of all the electric meters within the space window, that is, obtain the first abnormal probability parameter p1.

[0051] Step 5.2: Select a time window, obtain the electricity consumption of each period of the electric meter within a continuous period of time corresponding to the time window. Assume that the total number of calculated periods is N, and calculate the change trend fluctuation ratio, that is, obtain the second abnormal probability parameter p2. Specifically, in this embodiment, the total number of calculated periods N is a value calculated according to the time window length and the electric meter reporting frequency. For example, if the time window length is set to 12 hours and the electric meter reporting frequency is once per hour, then the total number of calculated periods N is 12. In addition, the time window can be set to 12 hours or can be flexibly adjusted according to the actual situation.

[0052] Step 5.3: Obtain several historical time series, that is, the historical time series data of the previous day, previous week, previous month, and previous year of the electricity meter, which are consecutive N periods in the same time period as the time window in Step 5.2. Calculate the similarity between each piece of historical time series data and the time series data in Step 5.2, and sort each calculated similarity to obtain the highest similarity, that is, obtain the third abnormal probability parameter p3.

[0053] Step 5.4: Define the predicted value, compare the electricity energy value of this period of the electricity meter, that is, the electricity energy value in the real-time data, with the predicted value, calculate the fluctuation ratio, and obtain the fourth abnormal probability parameter p4.

[0054] Step 5.5: Add the abnormal probability parameters p1, the second abnormal probability parameter p2, the third abnormal probability parameter p3, and the fourth abnormal probability parameter p4 by weighting to obtain the abnormal probability of the non-fixed service. Specifically, in this embodiment, the abnormal probability P of the non-fixed service = 0.1 * p1 + 0.35 * p2 + 0.35 * p3 + 0.2 * p4.

[0055] In the method for determining abnormal electricity consumption data provided in this embodiment, the spatial window of the electricity meter is determined according to the spatial association relationship of the electricity meter, and the spatial window includes:

[0056] The spatial window determined by the building, that is, the spatial window is all the rooms in the same building in the same location of the enterprise to which the electricity meter belongs.

[0057] The spatial window determined by the floor, that is, the spatial window is all the rooms on the same floor in the same building in the same location of the enterprise to which the electricity meter belongs.

[0058] Other spatial windows, that is, in addition to the spatial window determined by the building and the spatial window determined by the floor, the spatial window is flexibly determined according to the location of the enterprise, building, floor, and room to which the electricity meter belongs.

[0059] In the method for determining abnormal electricity consumption data provided in this embodiment, the calculation method of the spatial association discreteness in Step 5.1 is as follows:

[0060] Spatial association discreteness = Math.abs (electricity consumption of this period of this electricity meter - median of electricity consumption of all electricity meters in the spatial window) / (maximum electricity consumption of all electricity meters in the spatial window - minimum electricity consumption of all electricity meters in the spatial window).

[0061] In the method for determining abnormal electricity consumption data provided in this embodiment, the calculation method of the change trend fluctuation ratio in Step 5.2 is as follows:

[0062] Change trend fluctuation ratio = Math.abs(this electricity meter's current electricity consumption - this electricity meter's last cycle electricity consumption) / this electricity meter's last cycle electricity consumption - median growth rate of this electricity meter's electricity consumption within the time window) / (maximum growth rate of this electricity meter's electricity consumption within the time window - minimum growth rate of this electricity meter's electricity consumption within the time window).

[0063] In an abnormal determination method for power consumption data provided in this embodiment, the calculation method of the fluctuation ratio in step 5.4 is as follows:

[0064] Fluctuation ratio = Math.abs(current electricity consumption - predicted value) / Math.max(predicted value, current electricity consumption). Specifically, in this embodiment, the predicted value is currently obtained from the existing software platform configuration, and the source and calculation of the predicted value are not limited by this method.

[0065] Embodiment:

[0066] In an abnormal determination method for power consumption data provided in this embodiment, step 6 includes:

[0067] Judge the interval where the non-fixed service abnormal probability value obtained in step 5 is located. If it is less than or equal to 0.2, generate a level 4 early warning record in the software platform; if the non-fixed service abnormal probability obtained in step 5 is greater than 0.2 and less than or equal to 0.5, generate a level 3 early warning record in the software platform; if the non-fixed service abnormal probability obtained in step 5 is greater than 0.5 and less than or equal to 0.8, generate a level 2 early warning record in the software platform; if the non-fixed service abnormal probability obtained in step 5 is greater than 0.8 and less than or equal to 1, generate a level 1 early warning record in the software platform.

[0068] As can be seen from the above technical solutions, the embodiment of the present invention provides an abnormal early warning method for power consumption data, including: step 1, establishing a spatial association relationship for the electricity meters within the enterprise, and the spatial association relationship includes the enterprise location, building, floor, and room to which each electricity meter belongs; step 2, obtaining the real-time data of each electricity meter through an intelligent collector and reporting the real-time data to the platform according to a preset reporting frequency; step 3, after the platform receives the real-time data, judging whether the real-time data is abnormal power consumption data according to a preset index threshold; if so, storing the abnormal information corresponding to the abnormal power consumption data and ending the judgment; if not, executing step 4; step 4, the platform subtracts the electricity meter reading of this cycle of the electricity meter from the electricity meter reading of the previous cycle of the electricity meter to obtain the current electricity consumption of the electricity meter; step 5, obtaining the non-fixed service abnormal probability according to the spatial association relationship and the current electricity consumption of the electricity meter; step 6, performing hierarchical abnormal early warning on the power consumption data according to the non-fixed service abnormal probability.

[0069] In the prior art, the traditional method of regularly maintaining and inspecting smart meters is time-consuming, and for the unified operation and maintenance management of enterprises, the operation and maintenance costs are also relatively high. By adopting the foregoing method, on the premise of classifying the meters spatially, after obtaining the multi-index data reported by the smart meters, through the judgment of basic business rules, before data persistence, based on the spatial dispersion of the current electricity consumption of the meter, the fluctuation ratio of the change trend of electricity consumption within a continuous time, and the similarity calculation of historical time-series data, and introducing the prediction value to calculate the fluctuation ratio and the abnormal probability, so as to improve the accuracy of abnormal determination, reduce misjudgment, remind enterprise users to check for abnormalities in time, and correct the data. Therefore, compared with the prior art, the present invention improves the efficiency of abnormal determination of power consumption data and reduces the operation and maintenance costs.

[0070] In a specific implementation, the present invention also provides a computer storage medium, wherein the computer storage medium can store a program, and when the program is executed, it can include some or all of the steps in each embodiment of a method for abnormal warning of power consumption data provided by the present invention. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0071] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments of the present invention.

[0072] For the same and similar parts between the various embodiments in this specification, reference can be made to each other. The above-described embodiments of the present invention do not constitute a limitation to the protection scope of the present invention.

Claims

1. A method for abnormal warning of electric energy consumption data, characterized in that, The method includes the following steps: Step 1: Establish a spatial association relationship for the electricity meters within the enterprise. The spatial association relationship includes the location of the enterprise where each electricity meter belongs, the building, the floor, and the room. Step 2: Obtain the real-time data of each electricity meter through an intelligent collector and report the real-time data to the platform according to a preset reporting frequency. Step 3: After receiving the real-time data, the platform determines whether the real-time data is abnormal power consumption data according to a preset index threshold. If so, store the abnormal information corresponding to the abnormal power consumption data and end the judgment. If not, execute Step 4. Step 4: The platform subtracts the electricity meter reading of the current period of the electricity meter from the electricity meter reading of the previous period to obtain the electricity consumption of the current period of the electricity meter. Step 5: Obtain the abnormal probability of non-fixed services according to the spatial association relationship and the electricity consumption of the current period of the electricity meter. Step 6: Perform hierarchical abnormal warning on the power consumption data according to the abnormal probability of non-fixed services. The Step 5 includes: Step 5.1: Locate the spatial window where the electricity meter is located. The spatial window is all the rooms determined according to the spatial association relationship of the electricity meter. Obtain the electricity consumption of the current period of all the electricity meters within the spatial window, and calculate the spatial association dispersion degree of the electricity consumption of all the electricity meters within the spatial window, that is, obtain the first abnormal probability parameter p1. Step 5.2: Select a time window, obtain the electricity consumption of each period of the electricity meter within a continuous period of time corresponding to the time window. Assume that the total number of calculated periods is N, and calculate the change trend fluctuation ratio, that is, obtain the second abnormal probability parameter p2. Step 5.3: Obtain several historical time series, that is, the historical time series data of the previous day, previous week, previous month, and previous year of the electricity meter and the continuous N periods in the same time period as the time window in Step 5.

2. Calculate the similarity between each historical time series data and the time series data in Step 5.2, and sort each calculated similarity to obtain the highest similarity, that is, obtain the third abnormal probability parameter p3. Step 5.4: Define a predicted value, compare the electricity value of the current period of the electricity meter, that is, the electricity value in the real-time data, with the predicted value, and calculate the fluctuation ratio to obtain the fourth abnormal probability parameter p4. Step 5.5: Add the abnormal probability parameters p1, the second abnormal probability parameter p2, the third abnormal probability parameter p3, and the fourth abnormal probability parameter p4 weighted to obtain the abnormal probability of non-fixed services. The calculation method of the spatial association dispersion degree in the Step 5.1 is: Spatial association dispersion degree = Math.abs (electricity consumption of this electricity meter in the current period - median of electricity consumption of all electricity meters within the spatial window) / (maximum electricity consumption of all electricity meters within the spatial window - minimum electricity consumption of all electricity meters within the spatial window); The calculation method of the change trend fluctuation ratio in the Step 5.2 is: Variation trend fluctuation ratio = Math.abs((current electricity consumption of this electricity meter in this period - electricity consumption of this electricity meter in the previous period) / electricity consumption of this electricity meter in the previous period - median of electricity consumption growth rate of this electricity meter within the time window) / (maximum electricity consumption growth rate of this electricity meter within the time window - minimum electricity consumption growth rate of this electricity meter within the time window); The calculation method of the fluctuation ratio in step 5.4 is as follows: Fluctuation ratio = Math.abs((current electricity consumption - predicted value)) / Math.max(predicted value, current electricity consumption).

2. The abnormal warning method for electric energy consumption data according to claim 1, wherein, Step 3 includes: Step 3.1: Obtain the current, voltage, and electric energy of the electricity meter in the real-time data; Step 3.2: Determine whether the current, voltage, and electric energy of the electricity meter are respectively within the preset index thresholds. In the platform, each electricity meter corresponds to a preset index threshold of the electricity meter. The preset index threshold of the electricity meter includes a preset voltage index threshold, a preset current index threshold, and a preset electric energy index threshold; Step 3.3: If any one of the current, voltage, and electric energy in the real-time data exceeds the preset index threshold, determine that the real-time data is abnormal power consumption data, store the abnormal information corresponding to the abnormal power consumption data, and end the judgment; Step 3.4: If each of the current, voltage, and electric energy in the real-time data does not exceed the preset index threshold, then execute step 4.

3. The abnormal warning method for power consumption data according to claim 2, wherein The spatial window of the electricity meter is determined according to the spatial association relationship of the electricity meter. The spatial window includes: The spatial window determined by the building, that is, the spatial window is all the rooms in the same building in the same location of the enterprise to which the electricity meter belongs; The spatial window determined by the floor, that is, the spatial window is all the rooms on the same floor in the same building in the same location of the enterprise to which the electricity meter belongs; Other spatial windows, that is, in addition to the spatial window determined by the building and the spatial window determined by the floor, the spatial window is flexibly determined according to the location of the enterprise, building, floor, and room to which the electricity meter belongs.

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