Electric energy loss analysis method and system based on spatio-temporal data fusion model acquired by intelligent terminal, and medium

Abstract

The invention relates to the technical field of electric energy loss detection, and provides an electric energy loss analysis method and system based on a spatio-temporal data fusion model acquired by an intelligent terminal, and a medium, and the method comprises the following steps: S1, selecting a configuration mode of an electric energy loss model according to a user demand, and importing the electric energy loss model; s2, adjusting a node architecture of the electric energy loss model; s3, checking and analyzing whether an electric meter is omitted or configured wrongly in the electric energy loss model; s4, selecting an electric energy loss model according to a user demand, obtaining power consumption data of an electric meter corresponding to the electric energy loss model, and performing space-time multi-dimensional analysis on the power consumption data of the electric meter; and S5, calculating the lost electric quantity in the electric energy loss model of the transformer area or the line.

Description

Technical Field

[0001] This invention belongs to the field of power loss detection technology, and particularly relates to a power loss analysis method, system and medium based on a spatiotemporal data fusion model collected by a smart terminal. Background Technology

[0002] Currently, for power companies, effectively managing distribution area and line energy losses is fundamental to lean energy loss management. The traditional approach involves directly defining the main meter for a distribution area or line and its corresponding sub-meters in the meter records. The energy loss rate is calculated daily using the formula: (Total meter power consumption - Sum of all sub-meter consumption) / Total meter power consumption × 100%. However, this method is inefficient. Specifying the main meter or its associated sub-meters in the records and then searching for the specific meter in an MDM system that manages large-scale meter data is inefficient. Furthermore, this method lacks flexibility; a single meter's data can only be used for one type of energy loss calculation. For example, once a main meter for a distribution area is used to calculate distribution area energy loss, it cannot be used for line energy loss calculation. This lack of flexibility in handling different energy loss types can lead to situations where the power company cannot comprehensively manage the energy losses across its entire jurisdiction.

[0003] To address the aforementioned technical problems, this invention presents a method, system, and medium for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal. Summary of the Invention

[0004] This invention provides a method, system, and medium for analyzing power loss using a spatiotemporal data fusion model collected by a smart terminal, aiming to solve the problems of low configuration efficiency and poor flexibility of power loss models.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing power loss based on a spatiotemporal data fusion model collected by a smart terminal, comprising the following steps: S1, Select the configuration method of the power loss model according to user needs, and import the power loss model; S2, Adjust the node architecture of the power loss model; S3, check and analyze whether there are any missing or incorrectly configured electricity meters in the energy loss model; S4. Select an energy loss model according to user needs, obtain the electricity consumption data of the meter corresponding to the energy loss model, and perform spatiotemporal multi-dimensional analysis on the electricity consumption data of the meter. S5 calculates the lost electricity in the electrical energy loss model.

[0006] Based on the above technical solution, step S1 includes the following steps: S1.1 Select the configuration method of the power loss model according to user needs; S1.2 When users need to configure a large number of energy loss models, they can select the batch import configuration method to import the energy loss models. S1.3 When the user only needs to configure one power loss model, select the separate configuration method and import the power loss model.

[0007] Based on the above technical solution, step S1.2 includes the following steps: A1. Determine whether the user's required large-scale energy loss model has been assigned to the transformer area or line. A2. When the ownership of the transformer area or line is not determined, select the batch import configuration method to import the power loss model in batches. A3. When the ownership of the transformer area or line has been determined, select the partial batch import configuration method. First, select the required transformer area or line, and then import the power loss model in batches.

[0008] Further, step S1.3 includes the following steps: B1. Determine whether the system's model display list contains the energy loss model required by the user. B2. When the power loss model required by the user is available in the model display list, select the corresponding power loss model for configuration. B3. When the user's required energy loss model is not found in the model display list, a custom energy loss model is configured, specifying the name, indicators, and substation to which the energy loss model belongs, and configuring the electricity meters involved under the energy loss model and the relationship between the main and sub-meters.

[0009] Based on the above technical solution, step S2 includes the following steps: S2.1 When selecting the batch import configuration method in step S1, obtain the organizational hierarchy and hierarchical relationship between multiple power loss models; S2.2, Adjust the architecture of the corresponding power loss model according to the subordinate and hierarchical relationships.

[0010] Based on the above technical solution, step S3 includes the following steps: S3.1, Based on the geographical information of the electricity meters involved in the power loss model, determine whether all the electricity meters involved belong to the transformer area or line to which the power loss model belongs; S3.2, when there is an electricity meter that does not belong to the electricity loss model, obtain the original file of the electricity meter when it was installed, and determine whether the electricity meter belongs to the transformer area or line to which the electricity loss model belongs by combining the organizational relationship recorded in the original file. S3.3 When the organizational relationship indicates that the electricity meter belongs to the transformer area or line to which the power loss model belongs, the electricity meter is included in the power loss model; when the organizational relationship indicates that the electricity meter does not belong to the transformer area or line to which the power loss model belongs, the corresponding power loss model is obtained and included according to the organizational relationship.

[0011] Based on the above technical solution, in step S4, the electricity consumption data of the electricity meter is obtained from the smart terminal database, which is constructed through intelligent collection by the concentrator and periodic calls from the main station.

[0012] Furthermore, in step S4, the spatiotemporal multidimensional analysis can be energy loss analysis of different time types, energy loss analysis of different range types, energy loss rate distribution and ranking, GIS spatial visualization, configuration level visualization, and in-depth analysis of the causes of energy loss.

[0013] Based on the above technical solution, step S5 includes the following steps: S5.1, specify the data type and time range of the power loss, obtain the total power consumption of the corresponding meter and the power consumption of each sub-meter in the power loss model, and calculate the power loss based on the total power consumption and the power consumption of each sub-meter; S5.2 Determine whether there is missing or abnormal data when calculating the power loss. If there is no missing or abnormal data, the power loss data is correct. If there is missing or abnormal data, re-collect the missing or abnormal data, regenerate the power consumption of each sub-meter, and recalculate the power loss.

[0014] Secondly, the present invention provides a power loss analysis system based on a spatiotemporal data fusion model collected by a smart terminal, including a model configuration module, an architecture adjustment module, a troubleshooting and analysis module, a data analysis module, and a calculation module; The model configuration module is used to select the configuration method of the power loss model according to user needs and import the power loss model; The architecture adjustment module is used to adjust the node architecture of the power loss model; The investigation and analysis module is used to investigate and analyze whether there are any missing or incorrectly configured electricity meters in the energy loss model. The data analysis module is used to select an energy loss model according to user needs, obtain the electricity consumption data of the electricity meter corresponding to the energy loss model, and perform spatiotemporal multi-dimensional analysis on the electricity consumption data of the electricity meter. The calculation module is used to calculate the lost electricity in the electrical energy loss model.

[0015] Thirdly, the present invention provides a computer-readable storage medium storing program instructions, which, when executed, cause a computer to perform a power loss analysis method based on a spatiotemporal data fusion model collected by a smart terminal as described in any of the above embodiments.

[0016] Compared with related technologies, the beneficial effects of the present invention are as follows: This invention significantly reduces the workload of configuring energy loss models and improves their efficiency by allowing for selective batch import and batch adjustment of model architectures. It also enhances the flexibility of energy loss model configuration by allowing for individual configuration of models to meet specific needs. Through spatiotemporal multi-dimensional analysis of the electricity consumption data corresponding to the energy loss model, it can quickly obtain the spatiotemporal distribution of energy loss and the causes of energy loss, improving the relevance and comprehensiveness of data analysis. Furthermore, it can avoid user losses caused by excessive energy loss, thereby improving user satisfaction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For those skilled in the art, other embodiments can be derived from the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart of the power loss analysis method based on a spatiotemporal data fusion model collected by a smart terminal, provided by the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and examples: Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0022] Combination Figure 1 As shown, this disclosure provides a method for analyzing power loss based on a spatiotemporal data fusion model collected by a smart terminal, including the following steps: S1, Select the configuration method of the power loss model according to user needs, and import the power loss model; S2, Adjust the node architecture of the power loss model; S3, check and analyze whether there are any missing or incorrectly configured electricity meters in the energy loss model; S4. Select an energy loss model according to user needs, obtain the electricity consumption data of the meter corresponding to the energy loss model, and perform spatiotemporal multi-dimensional analysis on the electricity consumption data of the meter. S5 calculates the lost electricity in the electrical energy loss model.

[0023] The energy loss analysis method based on a spatiotemporal data fusion model collected by a smart terminal, as provided in this disclosure, significantly reduces the workload of configuring energy loss models and improves the efficiency of energy loss model configuration by allowing for selective batch import and batch adjustment of model architecture. It also enhances the flexibility of energy loss model configuration by allowing for individual configuration of models to meet specific needs. Through spatiotemporal multi-dimensional analysis of the electricity consumption data corresponding to the energy loss model, the spatiotemporal distribution of energy loss and the causes of energy loss can be quickly obtained, improving the relevance and comprehensiveness of data analysis. Furthermore, it can avoid user losses caused by excessive energy loss and improve user satisfaction.

[0024] Based on the above technical solution, step S1 includes the following steps: S1.1 Select the configuration method of the power loss model according to user needs; S1.2 When users need to configure a large number of energy loss models, they can select the batch import configuration method to import the energy loss models. S1.3 When the user only needs to configure one power loss model, select the separate configuration method and import the power loss model.

[0025] Further, step S1.2 includes the following steps: A1. Determine whether the user's required large-scale energy loss model has been assigned to the transformer area or line. A2. When the ownership of the transformer area or line is not determined, select the batch import configuration method to import the power loss model in batches. A3. When the ownership of the transformer area or line has been determined, select the partial batch import configuration method. First, select the required transformer area or line, and then import the power loss model in batches.

[0026] Specifically, in practical applications, configuring the power loss model for a transformer area or line involves hundreds or thousands of meters. Manual configuration is inefficient and prone to errors. By creating an import template that specifies the power loss model to be configured and the meters involved, a large number of power loss models can be imported at once, which can greatly improve the configuration efficiency of the power loss model.

[0027] The batch import configuration method is divided into full batch import and partial batch import. When the user has determined that the power loss model to be imported belongs to a specific transformer area or line, the partial batch import configuration method can be selected. The corresponding transformer area or line channel can be selected in the system first, and then the corresponding power loss model can be imported. This method is mainly suitable for the case of adding a new power loss model in a targeted manner, which makes it easier for operation and maintenance personnel to quickly complete the configuration of some new power loss models.

[0028] Further, step S1.3 includes the following steps: B1. Determine whether the system's model display list contains the energy loss model required by the user. B2. When the power loss model required by the user is available in the model display list, select the corresponding power loss model for configuration. B3. When the user's required energy loss model is not found in the model display list, a custom energy loss model is configured, specifying the name, indicators, and substation to which the energy loss model belongs, and configuring the electricity meters involved under the energy loss model and the relationship between the main and sub-meters.

[0029] Specifically, in step B1, when determining whether the system's model display list contains the energy loss model required by the user, this can be done through keyword search. The keywords can be the name of the energy loss model, which is typically the name of the transformer substation or line loss. After finding a suspected suitable energy loss model, detailed information about that model can be viewed, such as the type of meter involved, the metering method, and its distribution. In step S2.3, the index of the energy loss model refers to the parameter range of the energy loss rate.

[0030] Based on the above technical solution, step S2 includes the following steps: S2.1 When selecting the batch import configuration method in step S1, obtain the organizational hierarchy and hierarchical relationship between multiple power loss models; S2.2, Adjust the architecture of the corresponding power loss model according to the subordinate and hierarchical relationships.

[0031] Organizational hierarchy refers to the subordinate relationship between a superior organization and a subordinate organization, such as province-city-district / county, while hierarchical relationship refers to a substation or line.

[0032] When importing multiple energy loss models in large quantities, these models are all distributed in the same hierarchy. By batch adjusting the organizational affiliation and hierarchical relationship of all energy loss models, the user's management efficiency of energy loss models can be improved.

[0033] Based on the above technical solution, step S3 includes the following steps: S3.1, Based on the geographical information of the electricity meters involved in the power loss model, determine whether all the electricity meters involved belong to the transformer area or line to which the power loss model belongs; S3.2, when there is a meter that is suspected of not belonging to the power loss model, obtain the original file of the meter when it was installed, and determine whether the meter belongs to the transformer area or line to which the power loss model belongs by combining the organizational relationship recorded in the original file. S3.3 When the organizational relationship indicates that the electricity meter belongs to the transformer area or line to which the power loss model belongs, the electricity meter is included in the power loss model; when the organizational relationship indicates that the electricity meter does not belong to the transformer area or line to which the power loss model belongs, the corresponding power loss model is obtained and included according to the organizational relationship.

[0034] Specifically, by investigating and analyzing the meter coverage of the energy loss model, it is possible to quickly identify meters and their distribution that are not included in the energy loss model under the same concentrator or line. Alternatively, it can quickly identify abnormal situations such as the vast majority of meters under the same concentrator or line not being included in the energy analysis model, with only a few scattered meters included. This provides an efficient and practical means to quickly verify whether the meter configuration of the model is complete or mismatched, locate the meters, and quickly find the cause.

[0035] Based on the above technical solution, in step S4, the electricity consumption data of the electricity meter is obtained from the smart terminal database, which is constructed through intelligent collection by the concentrator and periodic calls from the main station.

[0036] During intelligent data collection, the concentrator gathers and aggregates large volumes of meter data, storing it centrally for convenient, periodic collection by the main collection station, significantly improving collection efficiency. It collects various data types from the meters: curve data, daily frozen data, monthly frozen data, and event data. Curve data is collected hourly, daily frozen data daily, and monthly frozen data monthly, with all data collected. For data not read due to network issues or power outages, the concentrator intelligently supplements the data through virtual data collection tasks, ensuring successful collection of all meter data.

[0037] During regular data collection, the main station periodically gathers data from smart terminals and smart meters via scheduled tasks. If data is missed due to network or communication quality issues, the main station can supplement the data collection. For the collected electricity consumption data, the main station is configured with a data verification task to periodically check the data quality, such as verifying data accuracy, missing data, large numbers, and user pattern matching. For abnormal data, the system uses interpolation algorithms to ensure its validity and legitimacy. The system also provides manual editing capabilities to ensure the final accuracy of the data. Through these verification, interpolation, and editing methods, the system ensures the integrity, accuracy, and consistency of the data used for energy loss analysis, providing a big data foundation for energy loss analysis based on spatiotemporal data fusion.

[0038] In step S4, when selecting an energy loss model according to user needs, users can search for the desired energy loss model in the model display list using keywords.

[0039] Furthermore, in step S4, the spatiotemporal multidimensional analysis can be energy loss analysis of different time types, energy loss analysis of different range types, energy loss rate distribution and ranking, GIS spatial visualization, configuration level visualization, and in-depth analysis of the causes of energy loss.

[0040] Specifically, the analysis of energy loss by time type refers to the ability to view energy loss by hour, day, or month; the analysis of energy loss by range type refers to the ability to view energy loss by transformer area or line; the distribution and ranking of energy loss rate refers to providing the distribution of energy loss rate within intervals, viewing the distribution of different energy loss rate levels, and ranking the energy loss rate from high to low. GIS spatial visualization refers to the ability to display the distribution of energy loss model areas and meters using GIS methods, for example, using different color blocks to show the high and low energy loss rates; configuration hierarchy visualization refers to the system's ability to automatically generate configuration diagrams, intuitively displaying the configuration distribution and hierarchical relationships of the energy loss model. In-depth analysis of energy loss refers to displaying the reasons for high energy loss from multiple dimensions, such as low power factor, incorrect data ratio, clock drift exceeding limits, and suspected electricity theft.

[0041] In this way, by simultaneously analyzing the electricity consumption data of the electricity meters involved in the user's required energy loss model in both time and spatial dimensions, the reasons for the high energy loss rate can be quickly and conveniently determined, the recurrence of system errors can be avoided, similar suspected electricity theft behaviors can be detected in a timely manner, unnecessary losses can be avoided, and users can be helped to comprehensively assess the energy loss situation.

[0042] Based on the above technical solution, step S5 includes the following steps: S5.1, specify the data type and time range of the power loss, obtain the total power consumption of the corresponding meter and the power consumption of each sub-meter in the power loss model, and calculate the power loss based on the total power consumption and the power consumption of each sub-meter; S5.2 Determine whether there is missing or abnormal data when calculating the power loss. If there is no missing or abnormal data, the power loss data is correct. If there is missing or abnormal data, re-collect the missing or abnormal data, regenerate the power consumption of each sub-meter, and recalculate the power loss.

[0043] Specifically, based on the specified time period and data type (such as curves, daily freeze, monthly freeze, etc.), the specified data type and time range of energy loss, and the specified energy loss model, a line energy loss analysis task is executed. Missing data is filled in, and abnormal data is corrected. Based on the re-collected and read data, the electricity consumption of each meter within the selected time period is regenerated. Combined with the energy loss model, the total meter consumption and the electricity consumption of each sub-meter are recalculated, and the lost electricity is re-statistically calculated. This allows for the recalculation of data in the energy loss model and the correction of energy loss data.

[0044] This disclosure provides a power loss analysis system based on a spatiotemporal data fusion model collected by a smart terminal, including a model configuration module, an architecture adjustment module, a troubleshooting and analysis module, a data analysis module, and a calculation module; The model configuration module is used to select the configuration method of the power loss model according to user needs and import the power loss model; The architecture adjustment module is used to adjust the node architecture of the power loss model; The investigation and analysis module is used to investigate and analyze whether there are any missing or incorrectly configured electricity meters in the energy loss model. The data analysis module is used to select an energy loss model according to user needs, obtain the electricity consumption data of the electricity meter corresponding to the energy loss model, and perform spatiotemporal multi-dimensional analysis on the electricity consumption data of the electricity meter. The calculation module is used to calculate the lost electricity in the electrical energy loss model.

[0045] This disclosure provides a computer-readable storage medium storing program instructions that, when executed, cause a computer to perform a power loss analysis method based on a spatiotemporal data fusion model collected by a smart terminal, as described in any of the above embodiments.

[0046] The present invention has been described above by way of example, but the present invention is not limited to the specific embodiments described above. Any modifications or variations made based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A method for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal, characterized in that, Includes the following steps: S1, Select the configuration method of the power loss model according to user needs, and import the power loss model; S2, Adjust the node architecture of the power loss model; S3, check and analyze whether there are any missing or incorrectly configured electricity meters in the energy loss model; S4. Select an energy loss model according to user needs, obtain the electricity consumption data of the meter corresponding to the energy loss model, and perform spatiotemporal multi-dimensional analysis on the electricity consumption data of the meter. S5 calculates the lost electricity in the electrical energy loss model.

2. The method for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 1, characterized in that, Step S1 includes the following steps: S1.1 Select the configuration method of the power loss model according to user needs; S1.2 When users need to configure a large number of energy loss models, they can select the batch import configuration method to import the energy loss models. S1.3 When the user only needs to configure one power loss model, select the separate configuration method and import the power loss model.

3. The power loss analysis method based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 2, characterized in that, Step S1.2 includes the following steps: A1. Determine whether the user's required large-scale energy loss model has been assigned to the transformer area or line. A2. When the ownership of the transformer area or line is not determined, select the batch import configuration method to import the power loss model in batches. A3. When the ownership of the transformer area or line has been determined, select the partial batch import configuration method. First, select the required transformer area or line, and then import the power loss model in batches.

4. The power loss analysis method based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 2, characterized in that, Step S1.3 includes the following steps: B1. Determine whether the system's model display list contains the energy loss model required by the user. B2. When the power loss model required by the user is available in the model display list, select the corresponding power loss model for configuration. B3. When the user's required energy loss model is not found in the model display list, a custom energy loss model is configured, specifying the name, indicators, and substation to which the energy loss model belongs, and configuring the electricity meters involved under the energy loss model and the relationship between the main and sub-meters.

5. The method for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 2, characterized in that, Step S2 includes the following steps: S2.1 When selecting the batch import configuration method in step S1, obtain the organizational hierarchy and hierarchical relationship between multiple power loss models; S2.2, Adjust the architecture of the corresponding power loss model according to the subordinate and hierarchical relationships.

6. The method for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 1, characterized in that, Step S3 includes the following steps: S3.1, Based on the geographical information of the electricity meters involved in the power loss model, determine whether all the electricity meters involved belong to the transformer area or line to which the power loss model belongs; S3.2, when there is a meter that is suspected of not belonging to the power loss model, obtain the original file of the meter when it was installed, and determine whether the meter belongs to the transformer area or line to which the power loss model belongs by combining the organizational relationship recorded in the original file. S3.3 When the organizational relationship indicates that the electricity meter belongs to the transformer area or line to which the power loss model belongs, the electricity meter is included in the power loss model; when the organizational relationship indicates that the electricity meter does not belong to the transformer area or line to which the power loss model belongs, the corresponding power loss model is obtained and included according to the organizational relationship.

7. The method for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 1, characterized in that, In step S4, the spatiotemporal multidimensional analysis can be energy loss analysis of different time types, energy loss analysis of different range types, energy loss rate distribution and ranking, GIS spatial visualization, configuration level visualization, and in-depth analysis of the causes of energy loss.

8. The method for analyzing power loss based on a spatiotemporal data fusion model acquired by a smart terminal according to claim 1, characterized in that, Step S5 includes the following steps: S5.1, specify the data type and time range of the power loss, obtain the total power consumption of the corresponding meter and the power consumption of each sub-meter in the power loss model, and calculate the power loss based on the total power consumption and the power consumption of each sub-meter; S5.2 Determine whether there is missing or abnormal data when calculating the power loss. If there is no missing or abnormal data, the power loss data is correct. If there is missing or abnormal data, re-collect the missing or abnormal data, regenerate the power consumption of each sub-meter, and recalculate the power loss.

9. A power loss analysis system based on a spatiotemporal data fusion model acquired by a smart terminal, characterized in that, It includes a model configuration module, an architecture adjustment module, a troubleshooting and analysis module, a data analysis module, and a computing module; The model configuration module is used to select the configuration method of the power loss model according to user needs and import the power loss model; The architecture adjustment module is used to adjust the node architecture of the power loss model; The investigation and analysis module is used to investigate and analyze whether there are any missing or incorrectly configured electricity meters in the energy loss model. The data analysis module is used to select an energy loss model according to user needs, obtain the electricity consumption data of the electricity meter corresponding to the energy loss model, and perform spatiotemporal multi-dimensional analysis on the electricity consumption data of the electricity meter. The calculation module is used to calculate the lost electricity in the electrical energy loss model.

10. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the power loss analysis method based on the spatiotemporal data fusion model collected by the smart terminal as described in any one of claims 1 to 8.

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