Power supply control method and device based on user data, and storage medium

Abstract

The application discloses a power supply control method and device based on user data and a storage medium, and belongs to the technical field of power control. When it is detected that a first power supply fails, first power consumption information and maximum load output of a backup power supply at the current time are acquired; a plurality of device groups are determined according to the first power consumption information and operation correlation data of devices in a target area; operation marks corresponding to each device group are determined according to load data corresponding to each device group and the maximum load output; and the control state of the devices is controlled according to the operation marks, so that the beneficial effect of reducing the waste of the power supply capacity of the backup power supply is achieved.

Description

Technical Field

[0001] This invention relates to the field of power control, and in particular to a power supply control method, device and storage medium based on user data. Background Technology

[0002] With the development of industrial parks in various regions, the electricity demand of enterprises within the parks is also constantly increasing. In order to ensure the stability of enterprise power supply, the power supply mode of the parks is usually a dual power supply and emergency generator set, so that power supply can be switched in the event of a power outage. At present, when switching power supply, load unloading is achieved through priority, so as to ensure that the capacity of the backup power supply can meet the power supply of important loads. However, in reality, this method may lead to a situation where the backup power capacity is excessive and some low-priority equipment cannot work because the high-priority equipment is not in operation. This situation indicates that the automatic load unloading system has a low level of intelligence.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a power supply control method, device, and storage medium based on user data, which aims to reduce the waste of backup power supply capacity.

[0005] To achieve the above objectives, the present invention provides a power supply control method based on user data, the power supply control method based on user data comprising the following steps:

[0006] When the failure of the first power supply is detected, the current power consumption information and the maximum load output of the backup power supply are obtained.

[0007] Multiple device groups are determined based on the first electricity consumption information and the operational correlation data of devices in the target area;

[0008] The running flag for each device group is determined based on the load data corresponding to each device group and the maximum load output.

[0009] The control state of the device is controlled according to the operation flag.

[0010] Optionally, before the step of determining multiple device groups based on the first electricity consumption information and the operational correlation data of devices in the target area, the method further includes:

[0011] Obtain the second power consumption information prior to the current moment, the second power consumption information including: the second power consumption data corresponding to each electrical device;

[0012] Extract the features of the second electricity consumption data as the second electricity consumption features to obtain multiple second electricity consumption features;

[0013] The operational correlation data is determined based on multiple secondary electricity consumption characteristics.

[0014] Optionally, the step of determining the operational correlation data based on multiple second electricity consumption characteristics includes:

[0015] Determine the device type identifier corresponding to the second power consumption feature based on the information of each of the aforementioned electrical devices;

[0016] Employee work information is determined based on the equipment type identifier;

[0017] The operational correlation data is determined based on multiple second electricity consumption characteristics and the employee work information.

[0018] Optionally, the employee work information includes: the number of employees corresponding to the location of each device, and the step of determining the operation-related data based on multiple second power consumption characteristics and the employee work information includes:

[0019] Determine the corresponding correlation coefficient based on any two of the aforementioned electricity consumption characteristics;

[0020] The operational relationship and characteristic correlation coefficient between the two devices are determined based on the correlation coefficient and the number of employees. The operational relationship includes one of the following: sequential operation relationship, synchronous operation relationship, and mutual exclusion relationship. The characteristic correlation coefficient includes the operating power ratio coefficient of the two devices.

[0021] The operational relationship and the feature correlation coefficient are used as the operational correlation data.

[0022] Optionally, the second electricity consumption data includes: second voltage data and second current data, and the step of extracting features from the second electricity consumption data as second electricity consumption features includes:

[0023] Extract the voltage fluctuation characteristics of the second voltage data and the current fluctuation characteristics of the second current data;

[0024] The second power consumption is determined based on the second voltage data and the second current data, and the power fluctuation characteristics corresponding to the second power consumption are extracted.

[0025] The voltage fluctuation characteristics, current fluctuation characteristics, and power fluctuation characteristics are used as the second power consumption characteristics.

[0026] Optionally, the step of determining multiple device groups based on the first electricity consumption information and the operational correlation data of devices in the target area includes:

[0027] Based on the first power consumption information, determine the target equipment currently in operation;

[0028] Based on the target device and the operational association data, a corresponding device group is determined, resulting in multiple device groups.

[0029] Optionally, the step of determining the running flag corresponding to each device group based on the load data corresponding to each device group and the maximum load output includes:

[0030] Multiple control schemes are generated based on the load data corresponding to all device groups and the maximum load output.

[0031] The control scheme is scored to obtain the scoring results;

[0032] Based on the scoring results, a target control scheme is determined from the multiple control schemes, and each device is grouped and marked with a corresponding operation tag according to the target control scheme.

[0033] Furthermore, to achieve the above objectives, the present invention also provides a power supply control device based on user data, the power supply control device based on user data comprising:

[0034] The acquisition module is used to acquire the current power consumption information and the maximum load output of the backup power supply when the failure of the first power supply is detected.

[0035] The grouping module is used to determine multiple device groups based on the first power consumption information and the operation association data of the devices in the target area;

[0036] The tagging module is used to determine the running tag corresponding to each device group based on the load data corresponding to each device group and the maximum load output;

[0037] An execution module is used to control the control state of the device according to the running flag.

[0038] Furthermore, to achieve the above objectives, the present invention also provides a power supply control device based on user data, the power supply control device based on user data comprising: a memory, a processor, and a power supply control program based on user data stored in the memory and executable on the processor, the power supply control program based on user data being configured to implement the steps of the power supply control method based on user data described in any of the above claims.

[0039] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a power supply control program based on user data, wherein when the power supply control program based on user data is executed by a processor, it implements the steps of the power supply control method based on user data described in any of the above claims.

[0040] This invention proposes a power supply control method based on user data. This method obtains second power consumption information prior to the current time, which yields a large amount of historical data. It extracts features from the second power consumption data as second power consumption features, resulting in multiple second power consumption features. These second power consumption features reflect the relationship between the operation of various devices. Based on these multiple second power consumption features, the operation association data is determined, thereby obtaining operation association data that reflects the operation association of devices, which improves the accuracy of subsequent grouping of the devices. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of a user data-based power supply control device in the hardware operating environment involved in the embodiments of the present invention;

[0042] Figure 2 This is a flowchart illustrating the first embodiment of the power supply control method based on user data of the present invention.

[0043] Figure 3 This is a flowchart illustrating a second embodiment of the power supply control method based on user data of the present invention.

[0044] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0045] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0046] Reference Figure 1 , Figure 1 This is a schematic diagram of the power supply control device structure based on user data in the hardware operating environment involved in the embodiments of the present invention.

[0047] like Figure 1As shown, the user data-based power supply control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interactive device 1003 may include a display screen and an input unit such as a keyboard. The interactive device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0048] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on power supply control devices based on user data and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0049] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a power supply control program based on user data.

[0050] exist Figure 1 In the user data-based power supply control device shown, the network interface 1004 is mainly used for data communication with other devices; the interaction device 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the user data-based power supply control device of the present invention can be set in the user data-based power supply control device, and the user data-based power supply control device calls the user data-based power supply control program stored in the memory 1005 through the processor 1001 and executes the user data-based power supply control method provided in the embodiment of the present invention.

[0051] This invention provides a power supply control method based on user data, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a power supply control method based on user data according to the present invention.

[0052] In this embodiment, the power supply control method based on user data includes:

[0053] Step S1: When the failure of the first power supply is detected, obtain the current power consumption information and the maximum load output of the backup power supply.

[0054] In this embodiment, an industrial park equipped with dual power supply lines and a backup generator is used as an example. The first power supply is mains power, which is connected to the industrial park's distribution cabinet via an external independent cable. Specifically, when the first power supply is detected to be unable to provide power, the current power consumption information is obtained. This information is typically obtained by receiving monitoring data from various monitoring devices via a network. The type of backup power supply is not limited here; it can be a second power supply on a separate power supply line from the first power supply, or it can be a generator as a backup power supply. Different backup power supplies have different maximum load outputs. Therefore, the amount of load shedding should also be based on the maximum load output.

[0055] Step S2: Determine multiple device groups based on the first power consumption information and the operation association data of devices in the target area;

[0056] The primary power consumption information here includes: currently operating devices. The operational correlation data can determine the operational relationship between two or more devices. Based on the operational correlation data and the currently operating devices, all devices are grouped. It should be noted that even devices not currently operating need to participate in the grouping. Optionally, devices that need to operate simultaneously or devices that need to operate sequentially are grouped into the same group, thus obtaining the multiple device groups.

[0057] Step S3: Determine the running flag corresponding to each device group based on the load data corresponding to each device group and the maximum load output;

[0058] In this embodiment, since each device group often corresponds to different load data, and it should be noted that this load data is not a fixed value, the load data corresponding to each device group is dynamically changing due to the sequential operation of the devices within the device group. The runnable device group is determined based on the maximum load output, and this runnable device group is marked as a first running state identifier. The remaining device groups are marked as second running state identifiers. The first and second running state identifiers are not the same, and the second running state identifier indicates that the device is stopped. Optionally, each device in the device group marked with the first running state identifier can be marked with a corresponding running time, prompt, etc.

[0059] Step S4: Control the control state of the device according to the operation flag.

[0060] In this embodiment, the device is controlled to stop or start operating based on the operation flag, thereby achieving dynamic device control.

[0061] In this embodiment, when the failure of the first power supply is detected, the first power consumption information and the maximum load output of the backup power supply at the current moment are obtained. Multiple device groups are determined based on the first power consumption information and the operation association data of the devices in the target area. The operation flag corresponding to each device group is determined based on the load data and the maximum load output of each device group. The control state of the device is controlled according to the operation flag. Compared with the method of simply protecting the operation of specific devices by priority, this method can accurately unload the load and realize dynamic control of device operation. This can effectively avoid the waste of the output power of the backup power supply and improve the efficiency of power use through intelligent identification.

[0062] Furthermore, based on the first embodiment, a second embodiment of the power supply control method based on user data of the present invention is proposed. In this embodiment, reference is made to... Figure 3 Before the step of determining multiple device groups based on the first electricity consumption information and the operation association data of devices in the target area, the method further includes:

[0063] Step S201: Obtain the second power consumption information before the current time. The second power consumption information includes: the second power consumption data corresponding to each power-consuming device.

[0064] The electrical equipment here can be industrial equipment, such as machine tools, stamping equipment, and testing equipment, or employee-related equipment, such as air conditioners, ventilators, and light sources. Data on voltage, current, and power at at least one moment during the operation of each electrical device prior to the current moment is obtained as the second electrical data. In this embodiment, preferably, the second electrical data includes voltage, current, and power at multiple moments during the operation of each electrical device.

[0065] Step S202: Extract the features of the second electricity consumption data as the second electricity consumption features, and obtain multiple second electricity consumption features;

[0066] Specifically, since the second power consumption data includes voltage, current, and power at multiple moments during the operation of each electrical device, the fluctuation characteristics of the above data can be obtained through Fourier transform, feature extraction, and other methods. In this embodiment, common features can include current variation characteristics, average power, peak power, and the corresponding time.

[0067] Step S203: Determine the operation-related data based on multiple second power consumption characteristics.

[0068] Optionally, multiple second power consumption characteristics can be associated to determine the correlation between each second power consumption characteristic. For example, if different devices have the same power characteristics, the relationship that these two devices need to operate synchronously can be determined, thereby obtaining the operation association data.

[0069] In this embodiment, obtaining the second power consumption information prior to the current moment yields a large amount of historical data. The features of the second power consumption data are extracted as second power consumption features, resulting in multiple second power consumption features. These second power consumption features reflect the relationship between the operation of various devices. Based on these multiple second power consumption features, the operation association data is determined, thereby obtaining operation association data that reflects the operation association of the devices, which improves the accuracy of subsequent grouping of the devices.

[0070] Furthermore, based on the first or second embodiment, a third embodiment of the power supply control method based on user data of the present invention is proposed. In this embodiment, the second power consumption data includes: second voltage data and second current data. The step of extracting features of the second power consumption data as second power consumption features includes:

[0071] Extract the voltage fluctuation characteristics of the second voltage data and the current fluctuation characteristics of the second current data;

[0072] In this embodiment, outlier data is removed through data cleaning, and missing values ​​are filled in using linear fitting. To ensure the accuracy of the corresponding data, fluctuation characteristics can be determined through filtering. Common methods include setting filters to limit the frequencies to be acquired, such as bandpass filtering, high-pass filtering, and low-pass filtering. After obtaining the filtered data, amplitude-related features, shape-related features, and frequency features can be extracted.

[0073] The second power consumption is determined based on the second voltage data and the second current data, and the power fluctuation characteristics corresponding to the second power consumption are extracted.

[0074] In this embodiment, the second voltage data and the second current data are generally acquired simultaneously, thus allowing for an accurate second power consumption. In some embodiments,

[0075] The voltage fluctuation characteristics, current fluctuation characteristics, and power fluctuation characteristics are used as the second power consumption characteristics.

[0076] It should be noted that, in some embodiments, the desired features can be selected as the second power consumption feature through principal component analysis or correlation analysis.

[0077] In this embodiment, voltage fluctuation characteristics of the second voltage data and current fluctuation characteristics of the second current data are extracted. The second power consumption is determined based on the second voltage data and the second current data, and the power fluctuation characteristics corresponding to the second power consumption are extracted. The voltage fluctuation characteristics, current fluctuation characteristics, and power fluctuation characteristics are used as the second power consumption characteristics, thereby improving the accuracy of the second power consumption characteristics.

[0078] Furthermore, based on any of the above embodiments, a fourth embodiment of the power supply control method based on user data of the present invention is proposed. In this embodiment, the step of determining the operation-related data according to multiple second power consumption characteristics includes:

[0079] Determine the device type identifier corresponding to the second power consumption feature based on the information of each of the aforementioned electrical devices;

[0080] Employee work information is determined based on the equipment type identifier;

[0081] Specifically, for example, if the current of the ventilation equipment is zero in the first period of time, it can be determined that the employee is not working in that period of time. The equipment type identification of the ventilation equipment often represents the employee's working status. By using the equipment type identification and the corresponding second power consumption characteristic, the employee's working information can be determined. This employee working information can include: the number of employees working in each area.

[0082] The operational correlation data is determined based on multiple second electricity consumption characteristics and the employee work information.

[0083] It should be noted that even if the fluctuation characteristics of the second power consumption of the two devices are the same, it cannot be determined that there is a definite correlation between the two devices. However, identifying that employees are working in the areas of both devices simultaneously can improve the management of the two devices. Specifically, for example, if employees are working in the areas of both devices during normal working hours, but during overtime, employees are only present in the areas of either device one or device two, it can be determined that device one and device two do not need to operate synchronously. Whether an employee is in the areas of device one or device two can be determined by the power consumption of the ventilation equipment in those areas.

[0084] In this embodiment, the device type identifier corresponding to the second power consumption feature is determined based on the information of each of the power-consuming devices, and the employee work information is determined based on the device type identifier. In fact, the actual work situation of the employees is determined from some devices that serve the employees. Then, the operation association data is determined based on multiple second power consumption features and the employee work information. In addition, the existence of a specific correlation between devices is judged by the employee's work situation. Compared with the correlation of power consumption identified from data features, the employee's work situation can better reflect the relationship between devices.

[0085] It should be noted that in some embodiments, employee work information can also be determined by an image acquisition device, such as a camera, or by a time clock device, for example, by determining whether an employee is working in an area where multiple devices are located.

[0086] Furthermore, the employee work information includes: the number of employees corresponding to the location of each device, and the step of determining the operation-related data based on multiple second power consumption characteristics and the employee work information includes:

[0087] Determine the corresponding correlation coefficient based on any two of the aforementioned electricity consumption characteristics;

[0088] The operational relationship and characteristic correlation coefficient between the two devices are determined based on the correlation coefficient and the number of employees. The operational relationship includes one of the following: sequential operation relationship, synchronous operation relationship, and mutual exclusion relationship. The characteristic correlation coefficient includes the operating power ratio coefficient of the two devices.

[0089] The operational relationship and the feature correlation coefficient are used as the operational correlation data.

[0090] In this embodiment, taking the first device and the second device as examples, the first device and the second device are of different device types. The current correlation coefficient is determined based on the first average current corresponding to the first device in the first time interval and the current correlation coefficient corresponding to the second device in the first time interval, and based on the employee correlation coefficient of the number of first employees in the space where the first device is located and the number of second employees in the space where the second device is located.

[0091] When the current correlation coefficient is in the first numerical range and the employee correlation coefficient is in the first numerical range, the operating relationship is determined to be a synchronous operating relationship. Here, the first data value range is the neighborhood range of 1, which is commonly: [0.8, 1], [0.9, 1], [0.95, 1], etc.

[0092] When the current correlation coefficient is in the second numerical range, and the employee correlation coefficient is in the second numerical range, the operating relationship is determined to be mutually exclusive. Here, the second data value range is the neighborhood range of -1, which is commonly: [-1, -0.8], [-1, -0.9], [-1, -0.95], etc.

[0093] When the current correlation coefficient is in the first numerical range and the employee correlation coefficient is not in the first numerical range, the operating relationship is determined to be an asynchronous operating relationship.

[0094] It should be noted that, to determine whether the devices operate sequentially, a third correlation coefficient can be calculated by taking the first average current of the first device in the first time interval and the third average current of the second device in the second time interval. Here, the second time interval refers to the time interval following and adjacent to the first time interval. The sequential operation relationship is determined based on this third correlation coefficient. In other embodiments, the operation relationship can be determined using methods such as machine learning.

[0095] In this embodiment, the operational correlation data can be accurately obtained through the current correlation coefficient and the employee correlation coefficient.

[0096] Furthermore, based on any of the above embodiments, a fifth embodiment of the power supply control method based on user data of the present invention is proposed. In this embodiment, the step of determining multiple device groups based on the first power consumption information and the operation association data of devices in the target area includes:

[0097] Based on the first power consumption information, determine the target equipment currently in operation;

[0098] Based on the target device and the operational association data, a corresponding device group is determined, resulting in multiple device groups.

[0099] Specifically, by setting a power threshold, the target device currently in operation is determined based on the power threshold and the first power consumption information. A first associated device with a sequential or synchronous operating relationship to the target device is searched in the operation association data, and the first associated device and the target device are grouped together. Since the number of target devices is often more than one, multiple device groups can be obtained.

[0100] In this embodiment, the target device currently in operation is determined by the first power consumption information. The corresponding device group is determined according to the target device and the operation association data, resulting in multiple device groups. This allows devices that are not currently in operation but need to be in operation in the future to be grouped together with the relevant target devices, so that they can be referenced together when reducing the load later.

[0101] Furthermore, based on any of the above embodiments, a sixth embodiment of the power supply control method based on user data of the present invention is proposed. In this embodiment, the step of determining the operating flag corresponding to each device group according to the load data corresponding to each device group and the maximum load output includes:

[0102] Multiple control schemes are generated based on the load data corresponding to all device groups and the maximum load output.

[0103] The control scheme is scored to obtain the scoring results;

[0104] Based on the scoring results, a target control scheme is determined from the multiple control schemes, and each device is grouped and marked with a corresponding operation tag according to the target control scheme.

[0105] In this embodiment, multiple control schemes are generated based on the load data corresponding to all device groups and the maximum load output. Compared to generating multiple control schemes based on the device's load data and maximum load output, this ensures the integrity of the production chain and takes into account changes in load data caused by the sequential operation of device groups. Therefore, it achieves more dynamic load control and improves the efficiency of backup power supply utilization compared to a priority-based approach. In this embodiment, different scoring categories can be set, such as: the total number of operating devices during backup power supply use, average load, high load duration, and the importance of the operating devices. The scoring results of each control scheme are determined through weighted calculation. Based on the scoring results, a corresponding operating flag is determined for each device group, and the power outage status of the corresponding device is controlled according to the flag.

[0106] Furthermore, embodiments of the present invention also propose a power supply control device based on user data, the power supply control device based on user data comprising:

[0107] The acquisition module is used to acquire the current power consumption information and the maximum load output of the backup power supply when the failure of the first power supply is detected.

[0108] The grouping module is used to determine multiple device groups based on the first power consumption information and the operation association data of the devices in the target area;

[0109] The tagging module is used to determine the running tag corresponding to each device group based on the load data corresponding to each device group and the maximum load output;

[0110] An execution module is used to control the control state of the device according to the running flag.

[0111] Furthermore, this embodiment of the invention also proposes a power supply control device based on user data, the power supply control device based on user data comprising: a memory, a processor, and a power supply control program based on user data stored in the memory and executable on the processor, the power supply control program based on user data being configured to implement the steps of the power supply control method based on user data described above.

[0112] Furthermore, embodiments of the present invention also propose a storage medium storing a power supply control program based on user data, wherein when the power supply control program based on user data is executed by a processor, it implements the steps of the power supply control method based on user data described above.

[0113] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0114] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0116] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A power supply control method based on user data, characterized in that, The power supply control method based on user data includes the following steps: When the failure of the first power supply is detected, the current power consumption information and the maximum load output of the backup power supply are obtained. Multiple device groups are determined based on the first electricity consumption information and the operational correlation data of devices in the target area; The running flag for each device group is determined based on the load data corresponding to each device group and the maximum load output. Controlling the control state of the device according to the operation flag; before the step of determining multiple device groups based on the first power consumption information and the operation association data of the devices in the target area, the method further includes: Obtain the second power consumption information prior to the current moment, the second power consumption information including: the second power consumption data corresponding to each electrical device; Extract the features of the second electricity consumption data as the second electricity consumption features to obtain multiple second electricity consumption features; The operational correlation data is determined based on multiple secondary electricity consumption characteristics; The step of determining the operational correlation data based on multiple second electricity consumption characteristics includes: Determine the device type identifier corresponding to the second power consumption feature based on the information of each of the aforementioned electrical devices; Employee work information is determined based on the equipment type identifier; The operation-related data is determined based on multiple second electricity consumption characteristics and the employee work information, and the employee work information is determined through an image acquisition device; The employee work information includes: the number of employees corresponding to the location of each device; the step of determining the operation-related data based on multiple second power consumption characteristics and the employee work information includes: Determine the corresponding correlation coefficient based on any two of the aforementioned electricity consumption characteristics; The operational relationship and characteristic correlation coefficient between the two devices are determined based on the correlation coefficient and the number of employees. The operational relationship includes one of the following: sequential operation relationship, synchronous operation relationship, and mutual exclusion relationship. The characteristic correlation coefficient includes the operating power ratio coefficient of the two devices. The operational relationship and the feature correlation coefficient are used as the operational correlation data; The second power consumption data includes: second voltage data and second current data. The step of extracting features from the second power consumption data as second power consumption features includes: Extract the voltage fluctuation characteristics of the second voltage data and the current fluctuation characteristics of the second current data; The second power consumption is determined based on the second voltage data and the second current data, and the power fluctuation characteristics corresponding to the second power consumption are extracted. The voltage fluctuation characteristics, current fluctuation characteristics, and power fluctuation characteristics are used as the second power consumption characteristics.

2. The power supply control method based on user data as described in claim 1, characterized in that, The step of determining multiple device groups based on the first electricity consumption information and the operational correlation data of devices in the target area includes: Based on the first power consumption information, determine the target equipment currently in operation; Based on the target device and the operational association data, a corresponding device group is determined, resulting in multiple device groups.

3. The power supply control method based on user data as described in any one of claims 1 to 2, characterized in that, The step of determining the running flag corresponding to each device group based on the load data corresponding to each device group and the maximum load output includes: Multiple control schemes are generated based on the load data corresponding to all device groups and the maximum load output. The control scheme is scored to obtain the scoring results; Based on the scoring results, a target control scheme is determined from the multiple control schemes, and each device is grouped and marked with a corresponding operation tag according to the target control scheme.

4. A power supply control device based on user data, characterized in that, The power supply control device based on user data includes: The acquisition module is used to acquire the current power consumption information and the maximum load output of the backup power supply when the failure of the first power supply is detected. A grouping module is used to determine multiple device groups based on the first power consumption information and the operational correlation data of devices in the target area; prior to the step of determining multiple device groups based on the first power consumption information and the operational correlation data of devices in the target area, the module further includes: Obtain the second power consumption information prior to the current moment, the second power consumption information including: the second power consumption data corresponding to each electrical device; Extract the features of the second electricity consumption data as the second electricity consumption features to obtain multiple second electricity consumption features; The operational correlation data is determined based on multiple secondary electricity consumption characteristics; The step of determining the operational correlation data based on multiple second electricity consumption characteristics includes: Determine the device type identifier corresponding to the second power consumption feature based on the information of each of the aforementioned electrical devices; Employee work information is determined based on the equipment type identifier; The operation-related data is determined based on multiple second electricity consumption characteristics and the employee work information, and the employee work information is determined through an image acquisition device; The employee work information includes: the number of employees corresponding to the location of each device; the step of determining the operation-related data based on multiple second power consumption characteristics and the employee work information includes: Determine the corresponding correlation coefficient based on any two of the aforementioned electricity consumption characteristics; The operational relationship and characteristic correlation coefficient between the two devices are determined based on the correlation coefficient and the number of employees. The operational relationship includes one of the following: sequential operation relationship, synchronous operation relationship, and mutual exclusion relationship. The characteristic correlation coefficient includes the operating power ratio coefficient of the two devices. The operational relationship and the feature correlation coefficient are used as the operational correlation data; The second power consumption data includes: second voltage data and second current data. The step of extracting features from the second power consumption data as second power consumption features includes: Extract the voltage fluctuation characteristics of the second voltage data and the current fluctuation characteristics of the second current data; The second power consumption is determined based on the second voltage data and the second current data, and the power fluctuation characteristics corresponding to the second power consumption are extracted. The voltage fluctuation characteristics, current fluctuation characteristics, and power fluctuation characteristics are used as the second power consumption characteristics; The tagging module is used to determine the running tag corresponding to each device group based on the load data corresponding to each device group and the maximum load output; An execution module is used to control the control state of the device according to the running flag.

5. A power supply control device based on user data, characterized in that, The user data-based power supply control device includes: a memory, a processor, and a user data-based power supply control program stored in the memory and executable on the processor, wherein the user data-based power supply control program is configured to implement the steps of the user data-based power supply control method as described in any one of claims 1 to 3.

6. A storage medium, characterized in that, The storage medium stores a power supply control program based on user data, which, when executed by a processor, implements the steps of the power supply control method based on user data as described in any one of claims 1 to 3.

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