Power performance detection method and device based on internet of things and storage medium

Abstract

The application discloses a power performance detection method and device based on an Internet of Things and a storage medium. The method comprises the following steps: collecting power supply consumption data of a power Internet of Things communication channel between power equipment related to the Internet of Things, determining an actual power quality early warning index and a voltage frequency adjustment control index of the power equipment according to the power supply consumption data; determining an actual voltage frequency change correlation detection coefficient according to the actual power quality early warning index and the voltage frequency adjustment control index, and determining a power performance detection result according to the actual voltage frequency change correlation detection coefficient and a preset voltage frequency change correlation detection coefficient, so that the problems of incomplete information and response lag in power performance detection in the related art are solved, the stability and reliability of a power system are improved, data support is provided for preventive maintenance of the power equipment, and the failure rate is reduced and the energy utilization efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of power testing technology, and in particular to a power performance testing method, device, and storage medium based on the Internet of Things. Background Technology

[0002] With the development of IoT technology, more and more power devices are being connected to the network, forming a complex power IoT system. Communication between these devices involves not only data transmission but also energy management and optimization.

[0003] In related technologies, power detection systems often rely on sensors at fixed locations and periodic data acquisition. Due to the complexity and dynamism of power systems, power detection methods in these technologies struggle to meet the requirements for real-time performance and accuracy, especially when dealing with power quality issues such as voltage fluctuations and frequency changes. Summary of the Invention

[0004] This invention provides a method, device, and storage medium for power performance testing based on the Internet of Things, in order to solve the problems of incomplete information and delayed response in power performance testing in related technologies.

[0005] According to one aspect of the present invention, an Internet of Things-based power performance testing method is provided, comprising:

[0006] By collecting power consumption data of the power IoT communication channel between power devices related to the Internet of Things, the actual power quality early warning index and voltage frequency regulation control index of the power devices are determined based on the power consumption data.

[0007] The actual voltage and frequency change correlation detection coefficient is determined based on the actual power quality early warning index and the voltage and frequency regulation control index. The power performance detection result is determined based on the actual voltage and frequency change correlation detection coefficient and the preset voltage and frequency change correlation detection coefficient.

[0008] According to another aspect of the present invention, an Internet of Things-based power performance testing device is provided, comprising:

[0009] The voltage and frequency regulation control index determination module is used to collect power consumption data of the power IoT communication channel between power devices related to the Internet of Things, and determine the actual power quality early warning index and voltage and frequency regulation control index of the power devices based on the power consumption data.

[0010] The power performance test result determination module is used to determine the actual voltage and frequency change correlation detection coefficient based on the actual power quality early warning index and the voltage and frequency regulation control index, and to determine the power performance test result based on the actual voltage and frequency change correlation detection coefficient and the preset voltage and frequency change correlation detection coefficient.

[0011] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the Internet of Things-based power performance testing method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the Internet of Things-based power performance detection method according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, embodiments of the present disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements the Internet of Things-based power performance detection method as described in any of the embodiments of the present disclosure.

[0017] The technical solution of this invention firstly collects power consumption data from the power IoT communication channel between power devices related to the Internet of Things (IoT). Based on the power consumption data, it determines the actual power quality early warning index and voltage frequency regulation control index of the power devices, enabling real-time determination of the actual operating status of the power devices. Next, it determines the actual voltage frequency change correlation detection coefficient based on the actual power quality early warning index and the voltage frequency regulation control index. Based on the actual voltage frequency change correlation detection coefficient and a preset voltage frequency change correlation detection coefficient, it determines the power performance detection result, allowing for timely detection of changes in power system performance. This solves the problems of incomplete information and delayed response in power performance detection in related technologies, not only improving the stability and reliability of the power system but also providing data support for preventative maintenance of power devices, helping to reduce the failure rate and improve energy utilization efficiency.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an Internet of Things-based power performance testing method according to Embodiment 1 of the present invention;

[0021] Figure 2 This is a flowchart of an Internet of Things-based power performance testing method according to Embodiment 2 of the present invention;

[0022] Figure 3 This is a flowchart of an Internet of Things-based power performance testing method according to Embodiment 3 of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of an Internet of Things-based power performance testing device according to Embodiment 4 of the present invention;

[0024] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the Internet of Things-based power performance testing method according to an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0028] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0029] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0030] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0031] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0032] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0033] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0034] Example 1

[0035] Figure 1 The flowchart of the Internet of Things (IoT) based power performance detection method is provided in Embodiment 1 of the present invention. This embodiment is applicable to the real-time detection and optimization management of power equipment in smart grids. The method can be executed by an IoT-based power performance detection device, which can be implemented in hardware and / or software. Optionally, it can be implemented through an electronic device, such as a mobile terminal, a PC, or a server.

[0036] like Figure 1 As shown, the method may specifically include:

[0037] S110. By collecting power consumption data of the power IoT communication channel between power devices related to the Internet of Things, the actual power quality early warning index and voltage frequency regulation control index of the power devices are determined based on the power consumption data.

[0038] The power IoT communication channel can be understood as a communication line or network used to connect different power devices, enabling the power devices to exchange data via Internet Protocol (IP). The power consumption data can be understood as information collected from power devices regarding, but not limited to, energy consumption, including but not limited to instantaneous power and cumulative power consumption, which can be used to assess the operating status and efficiency of the equipment. The actual power quality early warning index can be understood as an indicator measuring the quality of power supply, reflecting factors such as voltage stability and waveform distortion. The voltage frequency regulation and control index can be understood as the ability of power equipment to maintain the voltage and frequency within a specified range, ensuring the safe and stable operation of the power equipment and avoiding equipment damage and performance degradation caused by excessively high or low voltage or frequency fluctuations.

[0039] Based on the above scheme, optionally, the collection of power consumption data of power IoT communication channels between power devices related to the Internet of Things may include: collecting power consumption data by acquiring power meters or power sensors installed on the target power device; or deploying a network proxy service at the target node in the power device and collecting power consumption data at preset time intervals through the network proxy service; or pulling power consumption data from network devices and servers through network management protocols; or obtaining power consumption data by calling a preset data acquisition interface, etc.

[0040] S120. Determine the correlation detection coefficient of actual voltage and frequency change based on the actual power quality early warning index and the voltage and frequency regulation control index, and determine the power performance detection result based on the correlation detection coefficient of actual voltage and frequency change and the preset correlation detection coefficient of voltage and frequency change.

[0041] The actual voltage-frequency change correlation detection coefficient can be understood as a parameter used to quantify the relationship between voltage and frequency variations, and can be used to determine the interaction between the actual power quality early warning index and the voltage-frequency regulation control index. The preset voltage-frequency change correlation detection coefficient can be understood as a standard value set based on historical data or a theoretical model, used to compare with the actual voltage-frequency change correlation detection coefficient to determine whether the current power system operation deviates from the expected standard. The power performance detection result can be understood as the evaluation result obtained based on the actual voltage-frequency change correlation detection coefficient and the preset voltage-frequency change correlation detection coefficient, used to assess the health status and service level of power equipment.

[0042] Based on the above scheme, optionally, the correlation detection coefficient of actual voltage frequency change can be determined according to the actual power quality early warning index and the voltage frequency regulation control index using the following formula:

[0043]

[0044]

[0045] Where θ represents the correlation detection coefficient of actual voltage frequency change, and α i Let β be the actual power quality early warning index for the i-th power device. i Let Δα represent the power system voltage and frequency regulation control index of the i-th power device, Δβ represent the mean of the power quality early warning index, and n represent the number of power devices.

[0046] The technical solution of this invention firstly collects power consumption data from the power IoT communication channel between power devices related to the Internet of Things (IoT). Based on the power consumption data, it determines the actual power quality early warning index and voltage frequency regulation control index of the power devices, enabling real-time determination of the actual operating status of the power devices. Next, it determines the actual voltage frequency change correlation detection coefficient based on the actual power quality early warning index and the voltage frequency regulation control index. Based on the actual voltage frequency change correlation detection coefficient and a preset voltage frequency change correlation detection coefficient, it determines the power performance detection result, allowing for timely detection of changes in power system performance. This solves the problems of incomplete information and delayed response in power performance detection in related technologies, not only improving the stability and reliability of the power system but also providing data support for preventative maintenance of power devices, helping to reduce the failure rate and improve energy utilization efficiency.

[0047] Example 2

[0048] Figure 2 This is a flowchart of a power performance detection method based on the Internet of Things (IoT) provided in Embodiment 2 of the present invention. This embodiment further refines how to determine the actual power quality early warning index of power equipment based on the power consumption data, building upon the previous embodiments. Optionally, determining the actual power quality early warning index of power equipment based on the power consumption data includes: determining the generator speed response deviation of the power IoT communication channel based on the power consumption data, and determining the actual power quality early warning index based on the generator speed response deviation. Detailed implementation methods can be found in the description of this embodiment. Technical features that are the same as or similar to those in the foregoing embodiments will not be repeated here.

[0049] like Figure 2 As shown, the method may specifically include:

[0050] S210. By collecting power consumption data of the power IoT communication channel between power devices related to the Internet of Things, the generator speed response deviation of the power IoT communication channel is determined based on the power consumption data, the actual power quality early warning index is determined based on the generator speed response deviation, and the voltage frequency regulation control index is determined based on the power consumption data.

[0051] The generator speed response deviation can be understood as the difference between the actual generator speed and the expected or set generator speed. In this embodiment, generator speed is one of the key factors affecting the power output frequency. When the load changes, if the generator cannot quickly adjust its speed to maintain a constant output frequency, a generator speed response deviation will occur. This deviation can be used to evaluate the generator's performance and stability.

[0052] Based on the above scheme, optionally, the power consumption data includes at least generator speed data, voltage data, and current data; the generator speed response deviation of the power IoT communication channel is determined according to the power consumption data using the following formula:

[0053]

[0054] in, Let be the generator speed response deviation of the i-th power device in the i-th power IoT communication channel. Let HV represent the generator speed of the i-th power device in the j-th power IoT communication channel. i 阈 Let represent the threshold value for the generator speed of the i-th power device. Let the voltage of the j-th power IoT communication channel of the i-th power device be represented. Let HY represent the current of the j-th power IoT communication channel of the i-th power device. i 阈 Let be the threshold voltage of the i-th electrical device. This represents the threshold value of the current for the i-th power device.

[0055] The threshold value for generator speed can be understood as a preset target speed value. The generator speed should be kept as close as possible to the threshold value to ensure the stability and efficiency of the power equipment.

[0056] Based on the above scheme, optionally, the power quality early warning index can be determined according to the generator speed response deviation using the following formula:

[0057]

[0058] Where, α i Let be the power quality early warning index of the i-th power device. This represents the maximum value of the generator speed response deviation of the i-th power device. This represents the minimum value of the generator speed response deviation of the i-th power device. The generator speed of the j-th generator in the i-th power device, ΔPX i Let λ represent the mean value of the generator speed response deviation, λ1 represent the influencing factor of the generator speed response deviation, and m represent the number of communication channels in the power Internet of Things. n represents the number of electrical devices.

[0059] The generator speed response deviation influence factor can be understood as a factor used to adjust the degree of influence of the generator speed response deviation on the power quality early warning index. By setting different λ1 values, the influence of the generator speed response deviation on the power quality early warning index can be emphasized or weakened according to demand.

[0060] S220. Determine the correlation detection coefficient of actual voltage and frequency change based on the actual power quality early warning index and the voltage and frequency regulation control index, and determine the power performance detection result based on the correlation detection coefficient of actual voltage and frequency change and the preset correlation detection coefficient of voltage and frequency change.

[0061] The technical solution of this invention collects detailed power consumption data (including generator speed, voltage, current, power loss and power factor), calculates generator speed response deviation, and determines power quality early warning index accordingly. This enables real-time detection of the operating status of power equipment, provides early warning of potential power quality problems, and ensures the stability and reliability of the power system.

[0062] Example 3

[0063] Figure 3 This is a flowchart of a power performance detection method based on the Internet of Things (IoT) provided in Embodiment 3 of the present invention. This embodiment further refines how to determine the voltage and frequency regulation control index of power equipment based on the power consumption data, building upon the previous embodiments. Optionally, determining the voltage and frequency regulation control index of power equipment based on the power consumption data includes: determining the generator energy utilization level of the power IoT communication channel based on the power consumption data, and determining the voltage and frequency regulation control index based on the generator energy utilization level. Specific implementation details can be found in the description of this embodiment. Technical features that are the same as or similar to those in the previous embodiments will not be repeated here.

[0064] S310. By collecting power consumption data of the power IoT communication channel between power devices related to the Internet of Things, the actual power quality early warning index of the power devices is determined based on the power consumption data, and the generator power utilization level of the power IoT communication channel is determined based on the power consumption data, and the voltage frequency regulation control index is determined based on the generator power utilization level.

[0065] The generator's energy utilization rate can be understood as the effective utilization rate of the generator's output energy, reflecting the generator's efficiency in converting mechanical energy into electrical energy, as well as the loss of the generated energy during transmission and use.

[0066] Based on the above scheme, optionally, the power consumption data includes at least generator speed data, power loss data, and power factor; the generator power utilization level of the power IoT communication channel is determined according to the power consumption data using the following formula:

[0067]

[0068] Among them, PD i j This represents the generator power utilization rate of the i-th power device via the j-th power Internet of Things communication channel. Let the power loss of the i-th power device in the j-th power IoT communication channel be represented. Let the power factor be the power factor of the j-th power IoT communication channel for the i-th power device. This represents the generator speed of the i-th power device in the j-th power Internet of Things communication channel.

[0069] The power factor can be understood as an indicator that measures the ratio of the effective power actually consumed in the power Internet of Things (IoT) communication channel to the total power. In this embodiment, the power factor ranges from 0 to 1. When the power factor is 1, all input electrical energy is effectively utilized; when the power factor is less than 1, it indicates the existence of reactive power, i.e., ineffective electrical energy, which increases current demand and leads to additional line losses.

[0070] Based on the above scheme, optionally, the voltage frequency regulation control index can be determined according to the generator's energy utilization level using the following formula:

[0071]

[0072] Where, β i Let represent the power system voltage and frequency regulation control index for the i-th power device. This represents the minimum energy utilization rate of the generator of the i-th power equipment. Let PD represent the maximum energy utilization rate of the generator of the i-th power equipment. i 预 Let λ represent the preset generator energy utilization level of the i-th power device, and λ2 represent the influence factor of the generator energy utilization level.

[0073] The influence factor of generator energy utilization can be understood as adjusting the degree of influence of generator energy utilization on the voltage and frequency regulation control index. By setting different λ2 values, the influence of generator energy utilization on the voltage and frequency regulation control index can be emphasized or weakened according to demand.

[0074] S320. Determine the correlation detection coefficient of actual voltage and frequency change based on the actual power quality early warning index and the voltage and frequency regulation control index, and determine the power performance detection result based on the correlation detection coefficient of actual voltage and frequency change and the preset correlation detection coefficient of voltage and frequency change.

[0075] The technical solution of this invention collects power consumption data, calculates the generator's energy utilization level, and determines the voltage and frequency regulation control index accordingly, thereby realizing real-time detection and evaluation of the operating status of power equipment and ensuring the efficient and stable operation of the power system.

[0076] Example 4

[0077] Figure 4 This is a schematic diagram of a power performance testing device based on the Internet of Things (IoT) provided in Embodiment 4 of the present invention. Figure 4 As shown, the device includes: a voltage frequency regulation control index determination module 410 and a power performance detection result determination module 420. Among them,

[0078] The voltage and frequency regulation control index determination module 410 is used to collect power consumption data of the power IoT communication channel between power devices related to the Internet of Things, and determine the actual power quality early warning index and voltage and frequency regulation control index of the power devices based on the power consumption data; the power performance detection result determination module 420 is used to determine the actual voltage and frequency change correlation detection coefficient based on the actual power quality early warning index and the voltage and frequency regulation control index, and determine the power performance detection result based on the actual voltage and frequency change correlation detection coefficient and the preset voltage and frequency change correlation detection coefficient.

[0079] The technical solution of this invention firstly involves collecting power consumption data from the power IoT communication channel between power devices related to the Internet of Things (IoT) through a voltage and frequency regulation control index determination module 410. Based on the power consumption data, the actual power quality early warning index and voltage and frequency regulation control index of the power devices are determined, enabling real-time determination of the actual operating status of the power devices. Next, a power performance detection result determination module 420 determines the actual voltage and frequency change correlation detection coefficient based on the actual power quality early warning index and the voltage and frequency regulation control index. The power performance detection result is then determined based on the actual voltage and frequency change correlation detection coefficient and a preset voltage and frequency change correlation detection coefficient. This allows for timely detection of changes in power system performance, solving the problems of incomplete information and delayed response in power performance detection in related technologies. This not only improves the stability and reliability of the power system but also provides data support for preventative maintenance of power devices, helping to reduce the failure rate and improve energy utilization efficiency.

[0080] Based on the above scheme, optionally, the voltage frequency regulation control index determination module includes: an actual power quality early warning index determination submodule. The actual power quality early warning index determination submodule is used to determine the generator speed response deviation of the power IoT communication channel based on the power consumption data, and to determine the actual power quality early warning index based on the generator speed response deviation.

[0081] Based on the above scheme, optionally, the power consumption data includes at least generator speed data, voltage data, and current data; the generator speed response deviation of the power IoT communication channel is determined according to the power consumption data using the following formula:

[0082]

[0083] in, Let be the generator speed response deviation of the i-th power device in the i-th power IoT communication channel. Let HV represent the generator speed of the i-th power device in the j-th power IoT communication channel. i 阈 Let represent the threshold value for the generator speed of the i-th power device. Let the voltage of the j-th power IoT communication channel of the i-th power device be represented. Let HY represent the current of the j-th power IoT communication channel of the i-th power device. i 阈 Let be the threshold voltage of the i-th electrical device. This represents the threshold value of the current for the i-th power device.

[0084] Based on the above scheme, optionally, the actual power quality early warning index can be determined according to the generator speed response deviation using the following formula:

[0085]

[0086] Where, α i Let PX be the actual power quality early warning index for the i-th power device. i max This represents the maximum value of the generator speed response deviation of the i-th power device. This represents the minimum value of the generator speed response deviation of the i-th power device. The generator speed of the j-th generator in the i-th power device, ΔPX i Let λ represent the mean value of the generator speed response deviation, λ1 represent the influencing factor of the generator speed response deviation, and m represent the number of communication channels in the power Internet of Things. n represents the number of electrical devices.

[0087] Based on the above scheme, optionally, the voltage frequency regulation control index determination module includes a voltage frequency regulation control index determination submodule. The voltage frequency regulation control index submodule is used to determine the generator power utilization level of the power IoT communication channel based on the power consumption data, and to determine the voltage frequency regulation control index based on the generator power utilization level.

[0088] Based on the above scheme, optionally, the power consumption data includes at least generator speed data, power loss data, and power factor; the generator power utilization level of the power IoT communication channel is determined according to the power consumption data using the following formula:

[0089]

[0090] in, This represents the generator power utilization rate of the i-th power device via the j-th power Internet of Things communication channel. Let the power loss of the i-th power device in the j-th power IoT communication channel be represented. Let the power factor be the power factor of the j-th power IoT communication channel for the i-th power device. This represents the generator speed of the i-th power device in the j-th power Internet of Things communication channel.

[0091] Based on the above scheme, optionally, the voltage frequency regulation control index can be determined according to the generator's energy utilization level using the following formula:

[0092]

[0093] Where, β i Let represent the power system voltage and frequency regulation control index for the i-th power device. This represents the minimum energy utilization rate of the generator of the i-th power equipment. Let PD represent the maximum energy utilization rate of the generator of the i-th power equipment. i 预 Let λ represent the preset generator energy utilization level of the i-th power device, and λ2 represent the influence factor of the generator energy utilization level.

[0094] Based on the above scheme, optionally, the correlation detection coefficient of actual voltage frequency change can be determined according to the actual power quality early warning index and the voltage frequency regulation control index using the following formula:

[0095]

[0096] Where θ represents the correlation detection coefficient of actual voltage frequency change, and αi Let β be the actual power quality early warning index for the i-th power device. i Let Δα represent the power system voltage and frequency regulation control index of the i-th power device, Δβ represent the mean of the power quality early warning index, and n represent the number of power devices.

[0097] The IoT-based power performance testing device provided in this embodiment of the invention can execute the IoT-based power performance testing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0098] Example 5

[0099] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0100] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0101] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0102] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as an Internet of Things-based power performance detection method.

[0103] In some embodiments, an Internet of Things (IoT)-based power performance testing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the IoT-based power performance testing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform an IoT-based power performance testing method by any other suitable means (e.g., by means of firmware).

[0104] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0106] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0108] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0109] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0110] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A power performance detection method based on Internet of Things, characterized in that, The method comprises the following steps: By collecting power supply power consumption data of a power internet of things communication channel between power equipment related to the internet of things, determining an actual power quality early warning index and a voltage frequency regulation control index of the power equipment according to the power supply power consumption data; According to the actual power quality early warning index and the voltage frequency regulation control index, determining an actual voltage frequency change correlation detection coefficient, and determining a power performance detection result according to the actual voltage frequency change correlation detection coefficient and a preset voltage frequency change correlation detection coefficient; The actual power quality early warning index of the power equipment is determined according to the power supply power consumption data, which comprises the following steps: According to the power supply power consumption data, determining a generator speed response deviation of the power internet of things communication channel, and determining the actual power quality early warning index according to the generator speed response deviation; The voltage frequency regulation control index of the power equipment is determined according to the power supply power consumption data, which comprises the following steps: According to the power supply power consumption data, determining a generator electric energy utilization degree of the power internet of things communication channel, and determining the voltage frequency regulation control index according to the generator electric energy utilization degree. 2.The IoT-based electric power performance detection method of claim 1, wherein, The power supply power consumption data at least comprises generator speed data, voltage data and current data; the generator speed response deviation of the power internet of things communication channel is determined according to the power supply power consumption data by the following formula: ， wherein, a generator speed response deviation for the nth power device nth power internet of things communication channel, a generator speed for the nth power device nth power internet of things communication channel, a threshold value for a generator speed for the nth power device, a voltage for the nth power device nth power internet of things communication channel, a current for the nth power device nth power internet of things communication channel, a threshold value for a voltage for the nth power device, a threshold value for a current for the nth power device. 3.The IoT-based power performance detection method of claim 1, wherein, The power quality early warning index is determined according to the generator speed response deviation by the following formula: ， wherein, a power quality early warning index of the a maximum value of a generator speed response deviation of the a minimum value of a generator speed response deviation of the a generator speed of the a mean value of the generator speed response deviation, an influence factor of the generator speed response deviation, a number of power internet of things communication channels, , a number of power devices.​​​​​ 4.The IoT-based electric power performance detection method of claim 1, wherein, The power supply power consumption data at least comprises generator speed data, power loss data and power factor; the generator electric energy utilization degree of the power internet of things communication channel is determined according to the power supply power consumption data by the following formula: ； in, Represented as the first The first power equipment The power utilization rate of generators in each power Internet of Things communication channel Represented as the first The first power equipment Power loss of individual power IoT communication channels Represented as the first The first power equipment Power factor of each power IoT communication channel Represented as the first The first power equipment The generator speed is measured via a power Internet of Things (IoT) communication channel. 5.The IoT-based power performance detection method of claim 1, wherein, The voltage frequency regulation control index is determined according to the generator electric energy utilization degree by the following formula: ； wherein, a power system voltage frequency regulation control index of the first power device, a generator electric energy utilization minimum of the first power device, a generator electric energy utilization maximum of the first power device, a preset generator electric energy utilization of the first power device, an influence factor of the generator electric energy utilization. 6.The IoT-based electric power performance detection method of claim 1, wherein, The actual voltage frequency change correlation detection coefficient is determined according to the actual power quality early warning index and the voltage frequency regulation control index by the following formula: ； ； ； wherein, represents an actual voltage frequency variation correlation detection coefficient, represents an actual power quality early warning index of the power device, represents a power system voltage frequency regulation control index of the power device, represents a mean value of the power quality early warning index, represents a mean value of the power system voltage frequency regulation control index, represents a number of the power devices.

7. An Internet of Things based power performance detection device, characterized in that, The method comprises the following steps: The voltage frequency regulation control index determination module is used for collecting power supply power consumption data of a power internet of things communication channel between power equipment related to the internet of things, determining an actual power quality early warning index and a voltage frequency regulation control index of the power equipment according to the power supply power consumption data; The power performance detection result determination module is used for determining an actual voltage frequency change correlation detection coefficient according to the actual power quality early warning index and the voltage frequency regulation control index, and determining a power performance detection result according to the actual voltage frequency change correlation detection coefficient and a preset voltage frequency change correlation detection coefficient; The voltage frequency regulation control index determination module comprises an actual power quality early warning index determination sub-module; wherein The actual power quality early warning index determination sub-module is used for determining a generator speed response deviation of the power internet of things communication channel according to the power supply power consumption data, and determining the actual power quality early warning index according to the generator speed response deviation; The voltage frequency regulation control index determination module comprises a voltage frequency regulation control index determination sub-module; wherein The voltage frequency regulation control index submodule is configured for determining a generator electric energy utilization degree of the power internet of things communication channel according to the power supply power consumption data, and determining a voltage frequency regulation control index according to the generator electric energy utilization degree.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are configured to enable the processor to implement the power performance detection method based on the internet of things in any one of claims 1-6 when executed.

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