Remote management system of household energy storage power supply based on Internet of Things

Abstract

The invention discloses a household energy storage power supply remote management system based on the Internet of Things, relates to the technical field of power supply management, and is used to improve the problem of insufficient voltage supply in household areas caused by resistance amplification on different lines. The management system comprises dividing household power consumption areas, monitoring the power grid load of each power consumption area, screening high power flow areas for marking, setting multiple screening times, detecting the actual regional power, regional temperature and line volume of the marked area within the screening time, constructing a ridge regression model based on the actual regional power, regional temperature and line volume of the marked area to determine the experimental time of each marked area within the screening time, performing a mechanical stress test on each marked area according to the experimental time, calculating the resistance amplification coefficient by using a polynomial regression algorithm, generating a recommended electric energy amplification of the marked area according to the resistance amplification coefficient, and transmitting the recommended electric energy amplification to a user end for the user to regulate the household energy storage power supply to adjust the input electric energy of the marked area.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply management, and more specifically, to a household energy storage power supply remote management system based on the Internet of Things. Background Art

[0002] Power supply management technology refers to a series of technologies and methods used to ensure the safe, stable and efficient operation of the power system. Applying power supply management technology to the remote management of home energy storage power supplies can receive the electrical parameters of various areas in the home in real time and improve the safety of home electricity use.

[0003] The prior art has the following deficiencies:

[0004] In the past, when household energy storage power supplies were managed for household power supply, they distributed electricity to each line according to the grid load of different lines, ignoring the changes in electricity utilization efficiency caused by different resistance increases on different lines, resulting in deviations in voltage supply on different lines, affecting the use of electrical appliances. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a remote management system for household energy storage power supply based on the Internet of Things, which marks the high power flow areas by screening, analyzes the resistance increase of the marked areas from both the electrical appliance resistance increase and the wire resistance increase, and generates supply power for the corresponding marked areas to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The remote management system of household energy storage power supply based on the Internet of Things includes a data acquisition module, a regional screening module, a stress test module and a remote control module, and the signal connection between each module;

[0008] The data acquisition module is used to monitor the grid load of each power consumption area and transmit it to the regional screening module. According to the screening time, the actual regional power, regional temperature and line volume of the marked area are detected and transmitted to the stress testing module. After the experimental time transmitted to the stress testing module is detected, the number of nodes of the wire bending points and the number of nodes of the wire connection points in the marked area and the node temperature are obtained and transmitted to the stress testing module.

[0009] The area screening module is used to receive the grid load of each power consumption area transmitted by the data acquisition module, classify each power consumption area into a low power flow area and a high power flow area according to the grid load, screen out the high power flow area for marking, set the screening time, and transmit the marked area and the screening time to the data acquisition module and the stress test module;

[0010] The stress test module receives the screening time, actual power of the area, temperature of the area and line volume of the marked area, calculates the first energy coefficient and the second energy coefficient, constructs a ridge regression model based on the first energy coefficient and the second energy coefficient to determine the experimental time in the screening time, sends the experimental time to the data acquisition module, calculates the resistance amplification coefficient based on the number of nodes at the bending points of the wires, the number of nodes at the connection points of the wires and the node temperature in each marked area using a polynomial regression algorithm, and transmits the resistance amplification coefficient to the remote control module;

[0011] The remote control module receives the resistance amplification coefficient transmitted by the stress test module and multiplies it with the preset electric energy conversion coefficient to generate a recommended electric energy increase for the home energy storage power supply, and sends the recommended electric energy increase to the user end for the user to adjust the input electric energy of the home energy storage power supply to adjust the marked area.

[0012] In a preferred embodiment, the intelligent monitoring system is a software set for real-time monitoring and management of the power system, including storage of connection information of wires and electrical appliances, for calling the number of nodes at the connection points of the wires and the number of nodes at the bending points of the wires;

[0013] The wire bending point is the node where the wire bends, and the wire connection point is the node where the wire is connected to the electrical appliance.

[0014] In a preferred embodiment, when the data acquisition module divides the household power consumption area according to the line, the lines in the household energy storage power supply connection area are counted, and each line is regarded as a power consumption area. When the electrical appliances are connected to the same line, the electrical appliances are regarded as being in the same power consumption area;

[0015] The data acquisition module automatically calculates the grid load of each power consumption area by connecting to the power monitor and transmits it to the regional screening module;

[0016] A power monitor is a device that measures and monitors power consumption in real time and is used to calculate the grid load for each power consumption area.

[0017] In a preferred embodiment, the area screening module calculates the average value of the grid load of each power consumption area by obtaining the grid load of each power consumption area, and uses the average value of the grid load as the power flow threshold;

[0018] When the grid load in the power consumption area exceeds the power flow threshold, the power consumption area is marked as the first power flow area, a plurality of time periods with different time intervals are selected as screening time, and the marked area and screening time are transmitted to the data acquisition module;

[0019] When detecting the regional temperature of each power consumption area, the wire is divided into N sections, the temperature of each section of the wire is detected, and the average value of the temperature of each section of the wire is calculated as the regional temperature.

[0020] In a preferred embodiment, the stress test module calculates the first energy coefficient through the actual power of the region and the corresponding screening time, and uses the ratio of the time interval between the actual power of the region and the corresponding screening time as the first energy coefficient of the corresponding marked region;

[0021] The stress testing module calculates the second energy coefficient according to the regional temperature and the circuit volume of the corresponding marked area, and uses the ratio of the regional temperature to the circuit volume of the corresponding area as the second energy coefficient of the corresponding marked area.

[0022] In a preferred embodiment, the stress test module sums the first energy coefficients of each marked area at each screening time, and uses the summed result as the electrical energy flow of the appliance at the corresponding screening time; sums the second energy coefficients of each marked area at each screening time, and uses the summed result as the conductor electrical energy flow at the corresponding screening time;

[0023] The electric energy flows of electrical appliances at each screening time are merged into an electric energy data set of electrical appliances, and the electric energy flows of conductors at each screening time are merged into a conductor electric energy data set.

[0024] In a preferred embodiment, the stress test module constructs a ridge regression model based on the electrical energy dataset of the electrical appliances and the electrical energy dataset of the conductors to determine the experimental time of the mechanical stress experiment. The specific steps are as follows:

[0025] Data preparation: The data in the electrical energy dataset of electrical appliances and the electrical energy dataset of conductors are used as input, and the electrical energy flow of electrical appliances and the electrical energy flow of conductors at each screening time are used as an analysis sample to establish a feature vector;

[0026] Add regularization terms: Set the weights of the electrical energy flow of the appliance and the wire energy flow on the resistance increase according to the eigenvector, and construct a loss function;

[0027] Preprocessing: Initialize the influence weight vector and initialize the influence weight vector to 1;

[0028] Solve the optimal influence weight: Derivative the loss function with respect to the influence weight vector, use the derivative result as the loss function gradient, converge the influence weight vector according to the loss function gradient, and use the minimum value obtained by convergence as the optimal influence weight;

[0029] Prediction results: Calculate the resistance increase value at each screening time according to the optimal influence weight;

[0030] Determine the experimental time: select the maximum value of the resistance increase value as the maximum resistance increase, and use the screening time corresponding to the maximum resistance increase as the experimental time.

[0031] In a preferred embodiment, the data acquisition module performs a power-on test on each marked area according to the time interval corresponding to the calculated experimental time, and detects the number of nodes at the wire connection points in the marked area and the node temperature transmitted to the stress test module;

[0032] The stress test module calculates the average value of the node temperature of the wire connection point in each marked area as the connection point temperature coefficient in the corresponding marked area, and selects the median of the connection point temperature coefficients in all marked areas as the node temperature threshold;

[0033] The number of wire connection points exceeding the node temperature threshold in each marked area is counted as the number of over-temperature connection points in the corresponding marked area. The number of over-temperature connection points in the marked area is divided by the number of nodes of the wire connection points, and the calculated result is used as the over-temperature coefficient of the corresponding marked area.

[0034] The number of nodes of the wire bending points in the marked area is divided by the total number of wire bending points in all marked areas, and the calculated result is used as the bending coefficient of the corresponding marked area.

[0035] In a preferred embodiment, the stress testing module calculates the resistance amplification coefficient using a polynomial regression algorithm based on the over-temperature coefficient and the bending coefficient of each marked area: , where D is the resistance amplification coefficient, c is the constant term, a is the over-temperature coefficient, and b is the bending coefficient;

[0036] The stress test module calculates the resistance amplification coefficient of each marked area through a polynomial regression algorithm and transmits it to the remote control module;

[0037] The remote control module sends the calculated recommended power increase and marked area to the user end. After receiving the recommended power increase through the user end, the user selects the recommended power increase of the corresponding marked area and uses the household energy storage power supply to increase the power supply of the corresponding marked area to stabilize the regional voltage; randomly selects a period of time as the update time, resets the resistance increase coefficient according to the update time, and sends the reset signal to the data acquisition module, so that the data acquisition module re-detects various types of data.

[0038] The technical effects and advantages of the household energy storage power remote management system based on the Internet of Things of the present invention are as follows:

[0039] The present invention divides household electricity consumption areas, monitors the grid load of each electricity consumption area, screens out high power flow areas according to the grid load of each electricity consumption area for marking, narrows the control direction, reduces costs, sets multiple time intervals as screening time, detects the actual regional power, regional temperature and line volume of the marked area within the screening time, determines the experimental time of each marked area within the screening time based on the actual regional power, regional temperature and line volume of the marked area, calculates the experimental time to improve the data accuracy of resistance amplification analysis, performs mechanical stress testing on each marked area according to the experimental time, obtains the number of nodes at the bending points of the wires in the marked area, the number of nodes at the connecting points of the wires and the node temperature, calculates the resistance amplification coefficient, generates the recommended electric energy amplification of the marked area according to the resistance amplification coefficient, transmits the recommended electric energy amplification to the user end for the user to regulate the household energy storage power supply to adjust the input electric energy of the marked area, and reduces the low voltage risk in different areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of a household energy storage power supply remote management system based on the Internet of Things of the present invention.

[0041] Figure 2 This is a flow chart of the household energy storage power supply remote management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The present invention divides household electricity consumption areas, monitors the grid load of each electricity consumption area, screens out high power flow areas for marking according to the grid load of each electricity consumption area, sets multiple time intervals as screening time, detects the actual regional power, regional temperature and line volume of the marked area within the screening time, determines the experimental time of each marked area within the screening time based on the actual regional power, regional temperature and line volume of the marked area, performs mechanical stress testing on each marked area according to the experimental time, obtains the number of nodes at the bending points of the wires in the marked area, the number of nodes at the connecting points of the wires and the node temperature, calculates the resistance amplification coefficient, generates the recommended electric energy amplification of the marked area according to the resistance amplification coefficient, transmits the recommended electric energy amplification to the user end for the user to regulate the household energy storage power supply to adjust the input electric energy of the marked area, and reduces the low voltage risk in different areas.

[0044] Embodiment, a remote management system for household energy storage power supply based on the Internet of Things, such as Figure 1 and Figure 2 As shown, it includes a data acquisition module, a regional screening module, a stress test module and a remote control module, and the signals between the modules are connected;

[0045] The functions of each module are as follows:

[0046] The data acquisition module is used to divide household electricity consumption areas according to the lines, monitor the power grid load of each electricity consumption area and transmit it to the regional screening module. After receiving the marked area and screening time transmitted by the regional screening module, the actual regional power, regional temperature and line volume of the marked area are detected according to the screening time and transmitted to the stress testing module. After detecting the experimental time of the stress testing module, it is connected to the intelligent monitoring system to obtain the number of nodes at the bending points of the wires in the marked area, energize the marked area according to the experimental time, detect the number of nodes and node temperature of the connection points of the wires in the marked area, and transmit the number of nodes at the bending points of the wires in the marked area, the number of nodes at the connection points of the wires and the node temperature to the stress testing module;

[0047] The area screening module is used to receive the grid load of each power consumption area transmitted by the data acquisition module, classify each power consumption area into a low power flow area and a high power flow area according to the grid load, screen out the high power flow area for marking, set multiple time intervals as screening time, and transmit the marked area and screening time to the data acquisition module and the stress test module;

[0048] The stress test module receives the screening time, actual power of the region, temperature of the region and line volume of the marked area, calculates the first energy coefficient according to the actual power of the region and the screening time, calculates the second energy coefficient according to the temperature of the region and the line volume, constructs a ridge regression model based on the first energy coefficient and the second energy coefficient to determine the experimental time of each marked area during the screening time, sends the experimental time to the data acquisition module, receives the number of nodes at the bending points of the wires, the number of nodes at the connection points of the wires and the node temperature in each marked area, calculates the resistance amplification coefficient using a polynomial regression algorithm, and transmits the resistance amplification coefficient to the remote control module;

[0049] The remote control module receives the resistance amplification coefficient transmitted by the stress test module and multiplies it with the preset electric energy conversion coefficient to obtain the recommended electric energy increase for generating the home energy storage power supply, and sends the recommended electric energy increase to the user end for the user to adjust the input electric energy of the home energy storage power supply to adjust the marked area.

[0050] It should be noted that the intelligent monitoring system is a software collection that monitors and manages the power system in real time, including storage of connection information of wires and electrical appliances. In this example, it is used to call the number of nodes of wire connection points and the number of nodes of wire bending points; the wire bending point is the node where the wire bends, and the wire connection point is the node where the wire is connected to the electrical appliance.

[0051] When the data acquisition module divides the household electricity consumption area according to the line, it counts the lines in the household energy storage power supply connection area and regards each line as a power consumption area. When the electrical appliances are connected to the same line, the electrical appliances are regarded as being in the same power consumption area.

[0052] The data acquisition module automatically calculates the grid load of each power consumption area by connecting to the power monitor and transmits it to the regional screening module.

[0053] It should be noted that a power monitor is a device that measures and monitors power consumption in real time and is used to calculate the grid load in each power consumption area.

[0054] The regional screening module calculates the average value of the grid load of each power consumption area by obtaining the grid load of each power consumption area, and uses the average value of the grid load as the power flow threshold;

[0055] When the grid load in the power consumption area exceeds the power flow threshold, the power consumption area is marked as the first power flow area, and multiple time periods with different time intervals are selected as screening time, and the marked area and screening time are transmitted to the data acquisition module.

[0056] The data acquisition module detects the actual regional power, regional temperature and line volume of each marked area within each screening time according to the screening time input by the regional screening module;

[0057] The actual power of the area refers to the actual power consumed in the marked area under screening;

[0058] The data acquisition module obtains the actual power of each marked area through the SCADA system; when detecting the regional temperature of each power consumption area, the wire is divided into N sections, and the temperature of each section of the wire is detected by a temperature sensor, and the average temperature of each section of the wire is calculated as the regional temperature; the line volume of the wire in each marked area in the maintenance management system is called;

[0059] The data acquisition module transmits the detected actual regional power, regional temperature and line volume of each marked area to the stress test module.

[0060] It should be noted that the SCADA system is a computer system used in industrial automation and power management, which can detect and calculate the actual power of each marked area; the temperature sensor is a device used to measure the temperature of an object or environment, and in this case it is used to detect the temperature of each section of wire; the maintenance management system is a software tool for managing and optimizing equipment maintenance, and is used to call the line volume of the wires in each marked area.

[0061] The stress test module calculates the first energy coefficient and the second energy coefficient after receiving the actual regional power, regional temperature and line volume of each marked region at different screening times;

[0062] The stress test module calculates the first energy coefficient through the actual power of the area and the corresponding screening time, and takes the ratio of the time interval between the actual power of the area and the corresponding screening time as the first energy coefficient of the corresponding marked area;

[0063] The first energy coefficient reflects the energy efficiency of the marked area, and can be used to evaluate the power usage of the marked area. The more power is used in the area, the greater the loss to the electrical appliances, resulting in an increase in the resistance of the electrical appliances.

[0064] The stress testing module calculates the second energy coefficient according to the regional temperature and the line volume of the corresponding marked area, and takes the ratio of the regional temperature to the line volume of the corresponding area as the second energy coefficient of the corresponding marked area;

[0065] The second energy coefficient reflects the heat generation efficiency of the marked area, and can be used to evaluate the heat generation of the marked area. Household electrical wires are usually made of copper or aluminum. The higher the temperature, the higher the resistance of the wire.

[0066] The stress test module sums the first energy coefficients of each marked area at each screening time, and uses the sum result as the electrical energy flow of the electrical appliance at the corresponding screening time; sums the second energy coefficients of each marked area at each screening time, and uses the sum result as the conductor electrical energy flow at the corresponding screening time;

[0067] The electric energy flows of the electrical appliances at each screening time are combined into an electric energy data set of the electrical appliances, and the electric energy flows of the conductors at each screening time are combined into a conductor electric energy data set;

[0068] The stress test module integrates the electrical energy dataset of electrical appliances and the electrical energy dataset of conductors to construct a ridge regression model to determine the experimental time of the mechanical stress experiment. The specific steps are as follows:

[0069] Data preparation: The data in the electrical energy dataset and the wire energy dataset are standardized and used as input. The electrical energy flow of the electrical appliances and the wire energy flow at each screening time are used as an analysis sample to establish a feature vector. Each screening time is marked with a serial number in the order of the time interval from small to large.

[0070] Add regularization terms: Set the weight of the influence of the electrical energy flow of the electrical appliances and the electrical energy flow of the conductors on the resistance increase: ,in, is the loss function, is the influence weight vector, is the transpose of the weight vector, is the resistance increase degree in the i-th screening time, is the feature vector of the i-th analysis sample, is the regularization coefficient, is the L2 norm bisector of the influence weight vector, i.e., the sum of the squares of all influence weights in the influence weight vector;

[0071] Preprocessing: Initialize the influence weight vector. Initialize the influence weight vector to 1, and then set all influence weights in the influence weight vector to 1;

[0072] Solve for the optimal influence weight: Use the gradient descent method to solve the optimal influence weight of the loss function, derive the loss function with respect to the influence weight vector, and use the derivative result as the gradient of the loss function; update the influence weight through the weight iteration formula: ,in is the learning rate, is the gradient of the loss function. Different learning rates are set according to the degree of change of different influencing features to change the influence weight vector. The iterative process is repeated until the loss function converges to the minimum value, that is, the gradient of the loss function is close to zero, and the corresponding influence weight vector is taken as the optimal influence weight vector.

[0073] Prediction results: mark the resistance increase as z, mark the electrical energy flow in the electrical energy dataset as y, and mark the wire energy flow in the wire energy dataset as x. Use the prediction formula to calculate the resistance increase at each screening time: z=w0+w1y+w2x, where w0, w1 and w2 are the three influence weights in the optimal influence weight vector, w0 is the constant weight coefficient obtained when calculating the gradient of the loss function, and w1 and w2 are the influence weights of the corresponding data in the electrical energy dataset and the wire energy dataset, respectively.

[0074] Determine the experimental time: compare the resistance increase values ​​at each screening time, select the maximum value of the resistance increase values ​​as the maximum resistance increase, and use the screening time corresponding to the maximum resistance increase as the experimental time.

[0075] It should be noted that It is a function of the influence weight vector, which represents the prediction error of the model under the current influence weight vector. The ridge regression model calculates the optimal influence weight, determines the resistance increase of the two input features under each screening time, and screens out the screening time of the maximum resistance increase prediction value as the experimental time of the mechanical stress test to improve the accuracy of the resistance analysis. The regularization term is composed of the product of the regularization coefficient and the L2 norm of the influence weight vector. The role of the regularization term is to penalize the modulus of the influence weight vector, so that the ridge regression model is more inclined to select models with smaller weight values.

[0076] The stress test module transmits the calculated experimental temperature to the data acquisition module. The data acquisition module obtains the number of nodes at the bending points of the wire in the marked area through the intelligent monitoring system, and performs power-on test on each marked area according to the time interval corresponding to the calculated experimental time, and detects the number of nodes at the wire connection points in the marked area and the node temperature to transmit to the stress test module.

[0077] The stress test module calculates the average value of the node temperature of the wire connection point in each marked area as the connection point temperature coefficient in the corresponding marked area, and selects the median of the connection point temperature coefficients in all marked areas as the node temperature threshold;

[0078] The number of wire connection points exceeding the node temperature threshold in each marked area is counted as the number of over-temperature connection points in the corresponding marked area. The number of over-temperature connection points in the marked area is divided by the number of nodes of the wire connection points, and the calculated result is used as the over-temperature coefficient of the corresponding marked area.

[0079] The number of nodes of the wire bending points in the marked area is divided by the total number of wire bending points in all marked areas, and the calculated result is used as the bending coefficient of the corresponding marked area;

[0080] It should be noted that the higher the temperature of the wire connection point, the greater the resistance increase of the wire connection point. The over-temperature coefficient reflects the number of high-temperature wire connection points in the marked area, and can reflect the degree of resistance increase in the marked area. The higher the over-temperature coefficient, the greater the resistance increase in the marked area. The wire deforms at the wire bending point, stretching the original wire material and increasing the resistance increase. The bending coefficient reflects the number of wire bending points in the marked area, and can reflect the degree of resistance increase in the marked area. The higher the bending coefficient, the greater the resistance increase in the marked area.

[0081] The stress test module uses the polynomial regression algorithm to calculate the resistance amplification coefficient based on the over-temperature coefficient and bending coefficient of each marked area: , where D is the resistance amplification coefficient, c is the constant term, a is the over-temperature coefficient, and b is the bending coefficient;

[0082] The stress test module calculates the resistance amplification coefficient of each marked area through a polynomial regression algorithm and transmits it to the remote control module.

[0083] It should be noted that the above polynomial regression algorithm is not unique. In the polynomial regression algorithm given in this example, the default weights of the over-temperature coefficient and the bending coefficient are both 1. In addition, the constant term can be set according to the actual situation, for example, the constant term can be set to 0.2, etc., which will not be elaborated here.

[0084] The remote control module multiplies the resistance amplification coefficient in each marked area by the preset electric energy conversion coefficient, takes the product result as the recommended electric energy amplification of the household energy storage resistor, and randomly selects a period of time as the update time. The resistance amplification coefficient is reset according to the update time, and the reset signal is sent to the data acquisition module, so that the data acquisition module re-detects various types of data;

[0085] The remote control module sends the calculated recommended power increase and marked area to the user end. After receiving the recommended power increase through the user end, the user selects the recommended power increase of the corresponding marked area and uses the home energy storage power supply to increase the power supply of the corresponding marked area to stabilize the regional voltage.

[0086] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0087] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0088] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0090] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote management system for household energy storage power supply based on the Internet of Things, characterized in that: It includes data acquisition module, area screening module, stress test module and remote control module, and the signal connections between the modules; The data acquisition module is used to monitor the grid load of each power consumption area and transmit it to the regional screening module. According to the screening time, the actual regional power, regional temperature and line volume of the marked area are detected and transmitted to the stress testing module. After the experimental time transmitted to the stress testing module is detected, the number of nodes of the wire bending points and the number of nodes of the wire connection points in the marked area and the node temperature are obtained and transmitted to the stress testing module. The area screening module is used to receive the grid load of each power consumption area transmitted by the data acquisition module, classify each power consumption area into a low power flow area and a high power flow area according to the grid load, screen out the high power flow area for marking, set the screening time, and transmit the marked area and the screening time to the data acquisition module and the stress test module; The stress test module receives the screening time, actual power of the area, temperature of the area and line volume of the marked area, calculates the first energy coefficient and the second energy coefficient, constructs a ridge regression model based on the first energy coefficient and the second energy coefficient to determine the experimental time in the screening time, sends the experimental time to the data acquisition module, calculates the resistance amplification coefficient based on the number of nodes at the bending points of the wires, the number of nodes at the connection points of the wires and the node temperature in each marked area using a polynomial regression algorithm, and transmits the resistance amplification coefficient to the remote control module; The remote control module receives the resistance amplification coefficient transmitted by the stress test module and multiplies it with the preset electric energy conversion coefficient to generate a recommended electric energy increase for the home energy storage power supply, and sends the recommended electric energy increase to the user end for the user to adjust the input electric energy of the home energy storage power supply to adjust the marked area.

2. The household energy storage power remote management system based on the Internet of Things according to claim 1 is characterized in that: It also includes intelligent monitoring systems; The intelligent monitoring system is a collection of software for real-time monitoring and management of power systems, including storage of connection information of wires and electrical appliances, and the number of nodes for calling the number of wire connection points and the number of nodes for wire bending points; The wire bending point is the node where the wire bends, and the wire connection point is the node where the wire is connected to the electrical appliance.

3. The household energy storage power remote management system based on the Internet of Things according to claim 1 is characterized in that: When the data acquisition module divides the household electricity consumption area according to the line, it counts the lines in the household energy storage power supply connection area and regards each line as a power consumption area. When the electrical appliances are connected to the same line, the electrical appliances are regarded as being in the same power consumption area. The data acquisition module automatically calculates the grid load of each power consumption area by connecting to the power monitor and transmits it to the regional screening module; A power monitor is a device that measures and monitors power consumption in real time and is used to calculate the grid load for each power consumption area.

4. The remote management system for household energy storage power supply based on the Internet of Things according to claim 3 is characterized in that: The regional screening module calculates the average value of the grid load of each power consumption area by obtaining the grid load of each power consumption area, and uses the average value of the grid load as the power flow threshold; When the grid load in the power consumption area exceeds the power flow threshold, the power consumption area is marked as the first power flow area, a plurality of time periods with different time intervals are selected as screening time, and the marked area and screening time are transmitted to the data acquisition module; When detecting the regional temperature of each power consumption area, the wire is divided into N sections, the temperature of each section of the wire is detected, and the average value of the temperature of each section of the wire is calculated as the regional temperature.

5. The remote management system for household energy storage power supply based on the Internet of Things according to claim 4 is characterized in that: The stress test module calculates the first energy coefficient through the actual power of the area and the corresponding screening time, and takes the ratio of the time interval between the actual power of the area and the corresponding screening time as the first energy coefficient of the corresponding marked area; The stress testing module calculates the second energy coefficient according to the regional temperature and the circuit volume of the corresponding marked area, and uses the ratio of the regional temperature to the circuit volume of the corresponding area as the second energy coefficient of the corresponding marked area.

6. The household energy storage power remote management system based on the Internet of Things according to claim 5 is characterized in that: The stress test module sums the first energy coefficients of each marked area at each screening time, and uses the sum result as the electrical energy flow of the electrical appliance at the corresponding screening time; At each screening time, the second energy coefficients of each marked area are summed, and the sum result is used as the conductor electric energy flow at the corresponding screening time; The electric energy flows of electrical appliances at each screening time are merged into an electric energy data set of electrical appliances, and the electric energy flows of conductors at each screening time are merged into a conductor electric energy data set.

7. The remote management system for household energy storage power supply based on the Internet of Things according to claim 6 is characterized in that: The stress test module integrates the electrical energy dataset of electrical appliances and the electrical energy dataset of conductors to construct a ridge regression model to determine the experimental time of the mechanical stress experiment. The specific steps are as follows: Data preparation: The data in the electrical energy dataset of electrical appliances and the electrical energy dataset of conductors are used as input, and the electrical energy flow of electrical appliances and the electrical energy flow of conductors at each screening time are used as an analysis sample to establish a feature vector; Add regularization terms: Set the weights of the electrical energy flow of the appliance and the wire energy flow on the resistance increase according to the eigenvector, and construct a loss function; Preprocessing: Initialize the influence weight vector and initialize the influence weight vector to 1; Solve the optimal influence weight: Derivative the loss function with respect to the influence weight vector, use the derivative result as the loss function gradient, converge the influence weight vector according to the loss function gradient, and use the minimum value obtained by convergence as the optimal influence weight; Prediction results: Calculate the resistance increase value at each screening time according to the optimal influence weight; Determine the experimental time: select the maximum value of the resistance increase value as the maximum resistance increase, and use the screening time corresponding to the maximum resistance increase as the experimental time.

8. The household energy storage power remote management system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module conducts a power-on test on each marked area according to the time interval corresponding to the calculated experimental time, and detects the number of nodes at the wire connection points in the marked area and the node temperature transmitted to the stress test module; The stress test module calculates the average value of the node temperature of the wire connection point in each marked area as the connection point temperature coefficient in the corresponding marked area, and selects the median of the connection point temperature coefficients in all marked areas as the node temperature threshold; The number of wire connection points exceeding the node temperature threshold in each marked area is counted as the number of over-temperature connection points in the corresponding marked area. The number of over-temperature connection points in the marked area is divided by the number of nodes of the wire connection points, and the calculated result is used as the over-temperature coefficient of the corresponding marked area. The number of nodes of the wire bending points in the marked area is divided by the total number of wire bending points in all marked areas, and the calculated result is used as the bending coefficient of the corresponding marked area.

9. The household energy storage power remote management system based on the Internet of Things according to claim 8 is characterized in that: The stress test module uses the polynomial regression algorithm to calculate the resistance amplification coefficient based on the over-temperature coefficient and bending coefficient of each marked area: , where D is the resistance amplification coefficient, c is the constant term, a is the over-temperature coefficient, and b is the bending coefficient; The stress test module calculates the resistance amplification coefficient of each marked area through a polynomial regression algorithm and transmits it to the remote control module; The remote control module sends the calculated recommended power increase and marked area to the user end. After receiving the recommended power increase through the user end, the user selects the recommended power increase of the corresponding marked area and uses the household energy storage power supply to increase the power supply of the corresponding marked area to stabilize the regional voltage; randomly selects a period of time as the update time, resets the resistance increase coefficient according to the update time, and sends the reset signal to the data acquisition module, so that the data acquisition module re-detects various types of data.

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