Load type detection method based on ripple characteristic curve

Abstract

The invention provides a load type detection method based on a ripple characteristic curve, and the method comprises the steps: collecting the voltage and current data of a standard load and a to-be-detected load, respectively forming a ripple characteristic curve and a current and voltage phase difference, and enabling the ripple characteristic curve and the current and voltage phase difference of the standard load to construct a standard load database, the type of the to-be-detected load can be quickly determined by comparing the similarity of the ripple characteristic curve and the current and voltage phase difference of the to-be-detected load and the standard data so as to meet the load adding requirement of daily life, and after the type of the to-be-detected load is determined, the scene DIKW model is constructed based on the power consumption scene, and the load adding requirement of daily life is met. The scene DIKW model can process to obtain the peripheral equipment configuration, after the installation position of the to-be-detected load is evaluated in combination with the power consumption scene, the peripheral equipment configuration added into the power consumption scene together with the to-be-detected load can be determined, and the influence of adding of the to-be-detected load on other equipment in the power consumption scene is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power consumption management, and particularly relates to a method for detecting load types based on ripple characteristic curves. Background Art

[0002] With the continuous influx of various electrical equipment into the market, the loads of power users show highly diverse characteristics. From traditional resistive loads, such as incandescent lamps and electric water heaters, which simply consume electrical energy by heating through resistance and have a basic consistency between current and voltage phases; to a large number of inductive loads, like motors and transformers, due to the existence of inductive elements, the current lags behind the voltage, and during operation, it not only consumes active power but also generates reactive power, which has a negative impact on the power factor of the power grid; then to capacitive loads, commonly found in filter capacitors in some electronic circuits and capacitor banks for reactive power compensation, where the current leads the voltage and also changes the power characteristics of the power grid. In addition, there are also hybrid loads containing multiple components, such as complex electronic devices and comprehensive power consumption devices in industrial automation production lines, whose electrical characteristics are more complex, and the current-voltage relationship changes dynamically with the working conditions.

[0003] Traditional load identification methods have many limitations. On the one hand, the method of judging load types based on equipment nameplates or user declaration information has low accuracy because the actual operating equipment may deviate from the nominal information due to factors such as modification, aging, or working condition changes, and cannot truly reflect the real-time characteristics of the load. On the other hand, some simple electrical parameter measurement methods, such as only measuring single indicators like power factor or current harmonic content, are difficult to comprehensively and accurately distinguish complex load types. For some loads with similar non-linear degrees but different natures, single indicator measurement cannot effectively distinguish them, easily leading to misjudgment. At the same time, after judging the load type, the load needs to be added to the actual use scenario, and different loads require different peripheral devices. Currently, after identifying the load type, there is no recommendation for selecting the peripheral devices required by the load. Summary of the Invention

[0004] In view of this, the present invention proposes a method for detecting load types based on ripple characteristic curves, which can accurately identify load types and recommend the selection of peripheral device configurations.

[0005] The technical solution of the present invention is realized as follows:

[0006] A method for detecting load types based on ripple characteristic curves includes the following steps:

[0007] Step S1: Collect the voltage and current data of the standard load. After processing the voltage and current data, obtain the standard ripple characteristic curve and the standard current-voltage phase difference, and store the standard load, its standard ripple characteristic curve, and the standard current-voltage phase difference in the constructed standard load database;

[0008] Step S2: Obtain the voltage and current data of the load to be detected, and process them to obtain the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference. Calculate the similarity between the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference and the standard ripple characteristic curve and the standard current-voltage phase difference in the standard load database, and determine the type of the load to be detected according to the calculation result;

[0009] Step S3: Obtain the user's power consumption scenario, and construct a scenario DIKW model based on the power consumption scenario;

[0010] Step S4: Process the type of the load to be detected by the scenario DIKW model, and obtain the peripheral device configuration;

[0011] Step S5: Evaluate the installation location of the load to be detected in combination with the power consumption scenario, and determine the selection of the peripheral device configuration based on the installation location.

[0012] Preferably, the specific steps of step S1 are as follows:

[0013] Step S11: Determine the standard load for which data is to be collected, and operate the standard load in a stable working environment;

[0014] Step S12: Use a voltage sensor and a current sensor to collect the voltage and current data of the standard load, and perform denoising, calibration, and synchronization on the voltage and current data;

[0015] Step S13: Analyze the voltage and current data by using Fourier transform, extract the ripple characteristics, and construct a standard ripple characteristic curve;

[0016] Step S14: Use the zero-crossing detection method to calculate the phase difference between the voltage and the current, and obtain the standard current-voltage phase difference;

[0017] Step S15: Construct a standard load database, and send the standard ripple characteristic curve and the standard current-voltage phase difference to the standard load database for storage.

[0018] Preferably, the specific steps of step S2 are as follows:

[0019] Step S21: Use a voltage sensor and a current sensor to collect the voltage and current data of the load to be detected in the powered-on state, and process the voltage and current data to obtain the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference;

[0020] Step S22: Use the cosine similarity algorithm to calculate the similarity between the measured ripple feature curve and the measured current-voltage phase difference of the load to be detected and the standard ripple feature curve and the standard current-voltage phase difference of the standard load in the standard database;

[0021] Step S23: When the similarity between the measured ripple feature curve and the standard ripple feature curve and the similarity between the measured current-voltage phase difference and the standard current-voltage phase difference are both greater than the set threshold, output the type of the standard load corresponding to the standard ripple feature curve and the standard current-voltage phase difference as the type of the load to be detected.

[0022] Preferably, the threshold in step S23 is adjusted according to the actual application scenario.

[0023] Preferably, the specific steps of step S3 are as follows:

[0024] Step S31: Obtain all the user's electrical devices and their locations, and divide the user's electrical devices into load devices and peripheral devices;

[0025] Step S32: Obtain the connection relationship between the load devices and the peripheral devices, and at the same time collect the power consumption information of the load devices and the peripheral devices;

[0026] Step S33: Collect the standard peripheral device information supporting the standard load;

[0027] Step S34: Map the location of the electrical device, the power consumption information of the load device and the peripheral device, the connection relationship between the load device and the peripheral device, and the standard peripheral device information into typed resources, and construct a scenario DIKW model based on the typed resources.

[0028] Preferably, the typed resources include data resources, information resources, knowledge resources, and wisdom resources, and the data resources, information resources, knowledge resources, and wisdom resources can be converted into each other.

[0029] Preferably, the specific steps of step S4 are as follows:

[0030] Step S41: Input the type of the load to be detected into the scenario DIKW model;

[0031] Step S42: The scenario DIKW model obtains the standard peripheral device information of the standard load of the same type as the load to be detected;

[0032] Step S43: The scenario DIKW model obtains the peripheral devices connected to the load devices of the same type as the load to be detected in the user scenario.

[0033] Preferably, the specific steps of step S5 include:

[0034] Step S51: Obtain the installation requirements of the user, and determine whether the load to be detected has a fixed installation position from the installation requirements.

[0035] Step S52: When the load to be detected has a fixed installation position, determine whether there are existing peripheral devices around the installation position. If there are existing peripheral devices, connect the load to be detected to the existing peripheral devices. If there are no existing peripheral devices, introduce standard peripheral devices to connect to the load to be detected.

[0036] Preferably, the specific steps of step S5 further include:

[0037] Step S53: When the load to be detected has no fixed installation position, determine the load device of the same type as the load to be detected from the user's power consumption scenario.

[0038] Step S54: Install the load to be detected at the load device, and connect the peripheral device connected to the load device to the load to be detected.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] A load type detection method based on a ripple feature curve according to the present invention can, after processing the voltage and power data of a standard load to obtain a ripple feature curve and a current-voltage phase difference, be stored in a standard load database. Then, the ripple feature curve and the current-voltage phase difference generated from the voltage and current data of the load to be detected can be compared with those in the standard load database. Through the comparison results, the type of the load to be detected can be determined. The established standard load database can be widely applied to quickly determine the type of the load to be detected and meet the needs of daily life.

[0041] After determining the type of the load to be detected, a scenario DIKW model can be constructed according to the user's power consumption scenario. Through the constructed scenario DIKW model, the peripheral device configuration required by the load to be detected can be obtained. Then, according to the installation position of the load to be detected, the selection of the peripheral device configuration can be determined, so as to add the load to be detected and the peripheral devices to the user's power consumption scenario at the same time and ensure the smooth addition of the load to be detected. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only the preferred embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1Flowchart of a load type detection method based on a ripple feature curve according to the present invention;

[0044] Figure 2 Flowchart of step S1 of a load type detection method based on a ripple feature curve according to the present invention;

[0045] Figure 3 Flowchart of step S2 of a load type detection method based on a ripple feature curve according to the present invention;

[0046] Figure 4 Flowchart of step S3 of a load type detection method based on a ripple feature curve according to the present invention;

[0047] Figure 5 Flowchart of step S4 of a load type detection method based on a ripple feature curve according to the present invention;

[0048] Figure 6 Flowchart of step S5 of a load type detection method based on a ripple feature curve according to the present invention; Detailed implementation manner

[0049] To better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0050] See Figures 1 to 6 , a load type detection method based on a ripple feature curve provided by the present invention includes the following steps:

[0051] Step S1, collect voltage and current data of a standard load, process the voltage and current data to obtain a standard ripple feature curve and a standard current-voltage phase difference, and store the standard load, its standard ripple feature curve, and the standard current-voltage phase difference in a constructed standard load database;

[0052] Step S2, obtain voltage and current data of the load to be detected, and process them to obtain a to-be-detected ripple feature curve and a to-be-detected current-voltage phase difference. Calculate the similarity between the to-be-detected ripple feature curve and the to-be-detected current-voltage phase difference and the standard ripple feature curve and the standard current-voltage phase difference in the standard load database, and judge the type of the load to be detected according to the calculation result;

[0053] Step S3, obtain the user's power consumption scenario, and construct a scenario DIKW model based on the power consumption scenario;

[0054] Step S4, the scenario DIKW model processes the type of the load to be detected and obtains the peripheral device configuration;

[0055] Step S5: Evaluate the installation location of the load to be detected in combination with the electricity usage scenario, and determine the selection of peripheral device configuration based on the installation location.

[0056] A method for detecting the type of load based on the ripple characteristic curve of the present invention is to detect the type of the load to be detected to be added to the electricity usage scenario, so as to determine the location where the load to be detected is added and the required supporting equipment. The electricity loads on the market at present can be divided into resistive loads, capacitive loads, inductive loads and mixed loads. Different loads require different peripheral devices. Therefore, it is necessary to judge the type of the load to be detected. First, obtain the conventional standard loads on the market, then obtain the voltage and current data of the standard loads, and then the voltage and current data of the standard loads can be processed to obtain the standard ripple characteristic curve and the standard current-voltage phase difference. The standard ripple characteristic curve and the standard current-voltage phase difference can be transmitted to the standard load database for storage. Secondly, after obtaining the voltage and current data of the load to be detected, the same processing can be carried out to obtain the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference. Compare the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference of the load to be detected with the content in the standard database one by one, and calculate the similarity. When the similarity reaches a certain value, it can be judged that the type of the load to be detected is the same as that of the standard load, and thus the type of the load to be detected can be obtained. By establishing the standard load database, the types of all loads to be added can be detected, meeting the needs of daily life.

[0057] After determining the type of the load to be detected, first obtain the user's electricity usage scenario, which is the electricity usage scenario where the load to be detected needs to be added. After constructing the scenario DIKW model based on the electricity usage scenario, the scenario DIKW model can process and obtain the peripheral device configuration based on the type of the load to be detected, and the peripheral device configuration is the maximum peripheral device configuration required for this type of load. Finally, after evaluating the installation location of the load to be detected in combination with the electricity usage scenario, the final peripheral device configuration can be determined according to the installation location. After adding the load to be detected and the peripheral device configuration to the electricity usage scenario together, it can avoid affecting the user's electricity usage scenario and ensure the stable use of the load to be detected in the electricity usage scenario.

[0058] Preferably, the specific steps of step S1 are as follows:

[0059] Step S11: Determine the standard load for the data to be collected, and operate the standard load in a stable working environment;

[0060] Step S12: Use a voltage sensor and a current sensor to collect the voltage and current data of the standard load, and perform denoising, calibration and synchronization on the voltage and current data;

[0061] Step S13: Analyze the voltage and current data using Fourier transform, extract the ripple features, and construct a standard ripple feature curve;

[0062] Step S14: Use the zero-crossing detection method to calculate the phase difference between the voltage and current, and obtain the standard current-voltage phase difference;

[0063] Step S15: Construct a standard load database, and send the standard ripple feature curve and the standard current-voltage phase difference to the standard load database for storage.

[0064] After powering on the standard load, collect the voltage and current data, and then preprocess the voltage and current data to ensure the accuracy of the data during subsequent processing. Then, use Fourier transform and the zero-crossing detection method for the voltage and current data respectively, and the standard ripple feature curve and the standard current-voltage phase difference can be obtained respectively. Send the standard ripple feature curve and the standard current-voltage phase difference of each standard load to the standard load database for storage, so as to identify the load to be detected.

[0065] Preferably, the specific steps of step S2 are as follows:

[0066] Step S21: Use a voltage sensor and a current sensor to collect the voltage and current data of the load to be detected in the powered-on state, and process the voltage and current data to obtain the measured ripple feature curve and the measured current-voltage phase difference;

[0067] Step S22: Use the cosine similarity algorithm to calculate the similarity between the measured ripple feature curve and the measured current-voltage phase difference of the load to be detected and the standard ripple feature curve and the standard current-voltage phase difference of the standard load in the standard database;

[0068] Step S23: When the similarity between the measured ripple feature curve and the standard ripple feature curve and the similarity between the measured current-voltage phase difference and the standard current-voltage phase difference are both greater than the set threshold, output the type of the standard load corresponding to the standard ripple feature curve and the standard current-voltage phase difference as the type of the load to be detected, where the threshold is adjusted according to the actual application scenario.

[0069] When a load needs to be connected to the user's power consumption scenario, the load is regarded as the load to be detected. Similarly, after collecting the voltage and current data of the load to be detected under the powered-on state, the same method is used to process to obtain the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference. Then, the to-be-detected ripple characteristic curve and the to-be-detected current-voltage phase difference are compared with the content in the standard load database, and the similarity calculation is carried out. Among them, the to-be-detected ripple characteristic curve is compared with all the standard ripple characteristic curves, and the to-be-detected current-voltage phase difference is compared with all the standard current-voltage phase differences, and the similarity calculations are carried out respectively. The present invention uses the cosine similarity calculation method to calculate. When the similarity between the to-be-detected ripple characteristic curve and all the standard ripple characteristic curves and the similarity between the to-be-detected current-voltage phase difference and all the standard current-voltage phase differences are both greater than the preset threshold, it can be determined that the type of the load to be detected is consistent with the type of the corresponding standard load, and the threshold can be set according to the actual situation. The lowest threshold set by the present invention is 90%, ensuring that the detected load type is accurate and error-free.

[0070] Preferably, the specific steps of step S3 are as follows:

[0071] Step S31: Obtain all the user's electrical devices and their locations, and divide the user's electrical devices into load devices and peripheral devices;

[0072] Step S32: Obtain the connection relationship between the load device and the peripheral device, and at the same time collect the power consumption information of the load device and the peripheral device;

[0073] Step S33: Collect the information of the standard peripheral device supporting the standard load;

[0074] Step S34: Map the location of the electrical device, the power consumption information of the load device and the peripheral device, the connection relationship between the load device and the peripheral device, and the information of the standard peripheral device into typed resources, and construct a scenario DIKW model based on the typed resources.

[0075] After determining the type of the load to be detected, it is necessary to add the load to the user's power consumption scenario. In the user's power consumption scenario, there are many existing devices that have been used. Therefore, the basic information of the power consumption scenario is obtained first, including all the power-consuming devices in use and their locations. These power-consuming devices are divided into load devices and peripheral devices. The peripheral devices are used to provide protection and stability for the load devices. After determining the load devices and peripheral devices, the connection relationship between the load devices and the peripheral devices can be determined through the user's device management system. In addition to the information in the above power consumption scenario, the information of the standard peripheral devices supporting the standard load is also obtained. After mapping all the obtained information into typed resources, a scenario DIKW model can be constructed. The scenario DIKW model contains not only all the information in the power consumption scenario, but also the information of the standard peripheral devices required by the standard load, so as to accurately obtain the peripheral devices required by the load to be detected.

[0076] Preferably, the typed resources include data resources, information resources, knowledge resources, and wisdom resources, and the data resources, information resources, knowledge resources, and wisdom resources can be converted into each other.

[0077] The scenario DIKW model is constructed from typed resources, and the typed resources include data resources, information resources, knowledge resources, and wisdom resources. The four resources can be processed and converted into each other to obtain sufficient information.

[0078] Preferably, the specific steps of step S4 are as follows:

[0079] Step S41: Input the type of the load to be detected into the scenario DIKW model;

[0080] Step S42: The scenario DIKW model obtains the information of the standard peripheral devices of the standard load of the same type as the load to be detected;

[0081] Step S43: The scenario DIKW model obtains the peripheral devices connected to the load devices of the same type as the load to be detected in the user scenario.

[0082] After determining the type of the load to be detected and the scenario DIKW model, the load to be detected can be input into the scenario DIKW model. The scenario DIKW model can process it and obtain two sets of peripheral devices. In the scenario DIKW model, the standard peripheral device information matching the standard load is stored, and the standard peripheral device information of the standard load with the same type as the load to be detected is output. At the same time, the scenario DIKW model will also output the peripheral devices connected to the load devices of the same type as the load to be detected in the user scenario. That is, there are two sets of peripheral devices output by the scenario DIKW model, and selection needs to be made according to different requirements.

[0083] Preferably, the specific steps of step S5 include:

[0084] Step S51: Obtain the installation requirements of the user, and determine whether the load to be detected has a fixed installation position from the installation requirements;

[0085] Step S52: When the load to be detected has a fixed installation position, judge whether there are existing peripheral devices around the installation position. If there are existing peripheral devices, connect the load to be detected to the existing peripheral devices. If there are no existing peripheral devices, introduce standard peripheral devices to connect to the load to be detected.

[0086] Step S53: When the load to be detected has no fixed installation position, determine the load devices of the same type as the load to be detected from the user's power consumption scenario;

[0087] Step S54: Install the load to be detected at the load device, and connect the peripheral devices connected to the load device to the load to be detected.

[0088] When determining whether the load to be detected can be added, first determine the installation requirements of the user, and extract from the installation requirements whether the user needs to install the load to be detected at a specified position. There may be no supporting mature peripheral devices near the specified position. Therefore, it is necessary to judge whether there are existing peripheral devices at the installation position. If there are, they can be directly used. If not, standard peripheral devices can be introduced to connect to the load to be detected.

[0089] When the load to be detected has no fixed installation position, in order to save costs, the load to be detected can be installed at the load device of the same type as it and share a set of peripheral devices with the load device. When the load to be detected is added to the power consumption scenario, it can be installed together with the corresponding peripheral devices to avoid affecting the normal use of other devices in the power consumption scenario.

[0090] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A load type detection method based on a corrugation feature curve, characterized in that, Including the following steps: Step S1: Collect the voltage and current data of the standard load. After processing the voltage and current data, obtain the standard ripple characteristic curve and the standard current-voltage phase difference. Store the standard load, its standard ripple characteristic curve, and the standard current-voltage phase difference in the constructed standard load database; Step S2: Obtain the voltage and current data of the load to be detected, and process them to obtain the ripple characteristic curve to be measured and the current-voltage phase difference to be measured. Calculate the similarity between the ripple characteristic curve to be measured and the current-voltage phase difference to be measured and the standard ripple characteristic curve and the standard current-voltage phase difference in the standard load database, and determine the type of the load to be detected according to the calculation result; Step S3: Obtain the user's power consumption scenario, and construct a scenario DIKW model based on the power consumption scenario; Step S4: The scenario DIKW model processes the type of the load to be detected and obtains the peripheral device configuration; Step S5: Evaluate the installation location of the load to be detected in combination with the power consumption scenario, and determine the selection of the peripheral device configuration based on the installation location.

2. The load type detection method based on the corrugation feature curve according to claim 1, wherein The specific steps of the said Step S1 are: Step S11: Determine the standard load for which data is to be collected, and operate the standard load in a stable working environment; Step S12: Use a voltage sensor and a current sensor to collect the voltage and current data of the standard load, and perform denoising, calibration, and synchronization on the voltage and current data; Step S13: Use Fourier transform to analyze the voltage and current data, extract the ripple characteristics, and construct a standard ripple characteristic curve; Step S14: Use the zero-crossing detection method to calculate the phase difference between the voltage and the current, and obtain the standard current-voltage phase difference; Step S15: Construct a standard load database, and send the standard ripple characteristic curve and the standard current-voltage phase difference to the standard load database for storage.

3. A load type detection method based on a corrugation feature curve according to claim 1, characterized in that The specific steps of the said Step S2 are: Step S21: Use a voltage sensor and a current sensor to collect the voltage and current data of the load to be detected in the powered-on state, and process the voltage and current data to obtain the ripple characteristic curve to be measured and the current-voltage phase difference to be measured; Step S22: Use the cosine similarity algorithm to calculate the similarity between the ripple characteristic curve to be measured and the current-voltage phase difference to be measured of the load to be detected and the standard ripple characteristic curve and the standard current-voltage phase difference of the standard load in the standard database; Step S23: When the similarity between the ripple characteristic curve to be measured and the standard ripple characteristic curve and the similarity between the current-voltage phase difference to be measured and the standard current-voltage phase difference are both greater than the set threshold, output the type of the standard load corresponding to the standard ripple characteristic curve and the standard current-voltage phase difference as the type of the load to be detected.

4. The load type detection method based on a corrugation feature curve according to claim 3, wherein The threshold in the said Step S23 is adjusted according to the actual application scenario.

5. A load type detection method based on a corrugation feature curve according to claim 1, characterized in that The specific steps of the said Step S3 are: Step S31: Obtain all the user's electrical devices and their locations, and divide the user's electrical devices into load devices and peripheral devices; Step S32: Obtain the connection relationship between the load devices and the peripheral devices, and at the same time collect the power consumption information of the load devices and the peripheral devices; Step S33: Collect the standard peripheral device information supporting the standard load; Step S34: Map the location of the electrical equipment, the power consumption information of the load equipment and peripheral equipment, the connection relationship between the load equipment and the peripheral equipment, and the standard peripheral equipment information into typed resources, and construct a scenario DIKW model based on the typed resources.

6. The load type detection method based on a corrugation feature curve according to claim 5, characterized in that The typed resources include data resources, information resources, knowledge resources, and wisdom resources, and the data resources, information resources, knowledge resources, and wisdom resources can be converted into each other.

7. A method for detecting a load type based on a corrugation feature curve according to claim 1, characterized in that The specific steps of step S4 are as follows: Step S41: Input the type of the load to be detected into the scenario DIKW model. Step S42: The scenario DIKW model obtains the standard peripheral equipment information of the standard load of the same type as the load to be detected. Step S43: The scenario DIKW model obtains the peripheral equipment connected to the load equipment of the same type as the load to be detected in the user scenario.

8. A method for detecting a load type based on a corrugation feature curve according to claim 7, characterized in that, The specific steps of step S5 include: Step S51: Obtain the installation requirements of the user, and determine whether the load to be detected has a fixed installation location from the installation requirements. Step S52: When the load to be detected has a fixed installation location, determine whether there are existing peripheral equipment around the installation location. If there are existing peripheral equipment, connect the load to be detected to the existing peripheral equipment. If there are no existing peripheral equipment, introduce standard peripheral equipment to connect to the load to be detected.

9. A load type detection method based on a corrugation feature curve according to claim 8, characterized in that, The specific steps of step S5 also include: Step S53: When the load to be detected has no fixed installation location, determine the load equipment of the same type as the load to be detected from the user's power consumption scenario. Step S54: Install the load to be detected at the load equipment, and connect the peripheral equipment connected to the load equipment to the load to be detected.

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