Energy storage equipment remote monitoring method for energy internet

Abstract

The invention relates to the technical field of energy storage management, in particular to an energy internet-oriented energy storage equipment remote monitoring method, which comprises the following steps of: connecting energy storage system servers with load balancing servers in a one-to-one correspondence manner, and simultaneously monitoring real-time load data of the plurality of energy storage system servers through the plurality of load balancing servers; acquiring IP address data of the energy storage system servers, converting the IP address data into Hash values, and forming a Hash value label set by the Hash values of the plurality of energy storage system servers; and the load balancing server performs load balancing processing on the energy storage system server according to the real-time load data and the hash value label set of the energy storage system server. Therefore, operability is good, accuracy is high, flexibility is high, and a guarantee is provided for healthy development of the energy internet.

Description

Background Art

[0002] With the continuous growth of global energy demand and the large-scale application of renewable energy, Energy Internet (Energy Internet), as a new energy management model, is gradually becoming an important part of the future energy system. The core concept of Energy Internet is to organically combine distributed energy, smart grid, energy storage equipment and power users through information and communication technology (ICT) to achieve efficient production, transmission, distribution and consumption of energy. Energy storage equipment plays a vital role in Energy Internet. It can balance supply and demand, stabilize the power grid, optimize energy utilization, and thus improve the flexibility and reliability of the energy system. In the remote monitoring method of energy storage equipment for Energy Internet, the problem of load imbalance is a key background technical problem. Load imbalance refers to the uneven load distribution between various energy storage devices during energy distribution and use, resulting in some devices being overloaded, while other devices may be in an inefficient or idle state. This situation not only affects the efficiency of the overall system, but may also lead to a shortened equipment life and increased operating costs. Summary of the invention

[0003] The main purpose of the present invention is to provide a remote monitoring method for energy storage equipment for energy Internet, which effectively solves the above-mentioned problems mentioned in the background technology.

[0004] The technical solution of the present invention is as follows:

[0005] A remote monitoring method for energy storage equipment for energy Internet is proposed, which includes the following steps:

[0006] S1. Connect the energy storage system servers to the load balancing servers one by one, and monitor the real-time load data of multiple energy storage system servers simultaneously through multiple load balancing servers;

[0007] S2. Obtain IP address data of the energy storage system server, convert the IP address data into a hash value, and form a hash value tag set with the hash values ​​of multiple energy storage system servers;

[0008] S3. According to the real-time load data and hash value tag set of the energy storage system server, the load balancing server performs load balancing processing on the energy storage system server.

[0009] A further improvement of the present invention is that the load balancing server in S1 includes a load monitoring unit and at least one load balancing unit.

[0010] A further improvement of the present invention is that S1 comprises the following specific steps:

[0011] S11, configuring a single energy storage system server with a separate load balancing server;

[0012] S12, within the load monitoring time period, using the load monitoring unit of the load balancing server to monitor the real-time load data of the energy storage system server connected thereto, wherein the real-time load data includes time data and load data;

[0013] S13. Construct a two-dimensional rectangular coordinate system of time and load to obtain the changing trend of load data with time data.

[0014] A further improvement of the present invention is that S2 comprises the following specific steps:

[0015] S21, converting the IP address data of the energy storage system server into IPv4 format data, segmenting the IPv4 format data according to the segment positions, converting the segmented IPv4 format data segments into binary numbers respectively, connecting all the binary numbers in sequence to form a 32-bit binary number, and converting the 32-bit binary number into a decimal integer, where the decimal integer is a numerical representation of the IP address;

[0016] S22, taking the numerical representation of the IP address as input, using the CRC32 hash function to perform hash calculation, using the output hash value as the hash value tag of the IP address, and forming a hash value tag set with the hash values ​​of multiple energy storage system servers.

[0017] A further improvement of the present invention is that the step S1 further includes: calculating and summing the real-time load data of multiple energy storage system servers, and the calculation formula is: in n is the number of energy storage system servers, P is the total load of all energy storage system servers during the load monitoring period, m is the number of load balancing units in a single load balancing server, C1 is the rated load of a single load balancing server, C2 is the rated load of the load balancing unit in a single load balancing server, P is the load data at any time during the load monitoring period, t1 is the start time of load monitoring, and t2 is the end time of load monitoring.

[0018] A further improvement of the present invention is that, in S3, load balancing processing of the energy storage system server is performed according to the real-time load data and hash value tag set of the energy storage system server, including: taking the hash values ​​in the hash value tag set modulo the number n of the energy storage system servers to obtain the index of the energy storage system server; when the load data of the energy storage system server reaches a preset threshold within the load monitoring time period, it is determined that the energy storage system server needs to be dynamically adjusted, and the load balancing unit is called to perform load balancing processing.

[0019] The technical effects of the present invention are as follows:

[0020] A remote monitoring method for energy storage equipment oriented to the energy Internet is constructed. The present invention connects energy storage system servers to load balancing servers in a one-to-one correspondence, and monitors the real-time load data of multiple energy storage system servers simultaneously through multiple load balancing servers; obtains the IP address data of the energy storage system server, converts the IP address data into a hash value, and forms a hash value tag set with the hash values ​​of multiple energy storage system servers; according to the real-time load data of the energy storage system server and the hash value tag set, the load balancing server performs load balancing processing on the energy storage system server. Therefore, it has good operability, high accuracy, and high flexibility, providing guarantee for the healthy development of the energy Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0022] Figure 1 The present invention is a flowchart of a remote monitoring method for energy storage equipment for energy internet. DETAILED DESCRIPTION

[0023] The present invention aims to propose a remote monitoring method for energy storage equipment for energy Internet, by connecting energy storage system servers with load balancing servers one by one, and simultaneously monitoring the real-time load data of multiple energy storage system servers through multiple load balancing servers; obtaining the IP address data of the energy storage system server, converting the IP address data into a hash value, and forming a hash value tag set with the hash values ​​of multiple energy storage system servers; according to the real-time load data of the energy storage system server and the hash value tag set, the load balancing server performs load balancing processing on the energy storage system server. Therefore, it has good operability, high accuracy and high flexibility, providing guarantee for the healthy development of energy Internet.

[0024] Embodiment 1:

[0025] This embodiment proposes a remote monitoring method for energy storage devices for energy Internet. Figure 1 As shown, the specific steps include:

[0026] S1. Connect the energy storage system servers to the load balancing servers one by one, and monitor the real-time load data of multiple energy storage system servers simultaneously through multiple load balancing servers;

[0027] S2. Obtain IP address data of the energy storage system server, convert the IP address data into a hash value, and form a hash value tag set with the hash values ​​of multiple energy storage system servers;

[0028] S3. According to the real-time load data and hash value tag set of the energy storage system server, the load balancing server performs load balancing processing on the energy storage system server.

[0029] In this embodiment, the load balancing server in S1 includes a load monitoring unit and at least one load balancing unit.

[0030] In this embodiment, S1 includes the following specific steps:

[0031] S11. Configure a separate load balancing server for each energy storage system server; each energy storage system server is configured with a separate load balancing server, which means that the load of each energy storage system server is managed by a specific load balancing server. This one-to-one configuration ensures that each energy storage system server can receive exclusive monitoring and load balancing processing, avoiding resource competition and management confusion. This configuration also enhances the scalability of the system, because when more energy storage system servers need to be added, only the corresponding load balancing servers need to be added.

[0032] S12. During the load monitoring period, the load monitoring unit of the load balancing server is used to monitor the real-time load data of the energy storage system server connected thereto, wherein the real-time load data includes time data and load data; the time data records the specific time of each monitoring time point, and these time points can be fixed intervals, such as every minute, every hour, or based on event triggering. The load data records the current load of the energy storage system server at each time point, including CPU usage, memory usage, network bandwidth usage, etc. These data can reflect the working status of the server at a specific time point.

[0033] S13. Construct a two-dimensional rectangular coordinate system of time-load to obtain the change trend of load data over time. A two-dimensional rectangular coordinate system of time-load is constructed based on the data obtained by the load monitoring unit. The load data is used as the vertical axis and the time data is used as the horizontal axis to draw a curve of the load change over time. This visualization method can intuitively display the fluctuation trend of the load and help system administrators understand the change pattern of the load.

[0034] In this embodiment, S2 includes the following specific steps:

[0035] S21, converting the IP address data of the energy storage system server into IPv4 format data, segmenting the IPv4 format data according to the segment positions, converting the segmented IPv4 format data segments into binary numbers respectively, connecting all the binary numbers in sequence to form a 32-bit binary number, and converting the 32-bit binary number into a decimal integer, where the decimal integer is a numerical representation of the IP address;

[0036] S22, taking the numerical representation of the IP address as input, using the CRC32 hash function to perform hash calculation, using the output hash value as the hash value label of the IP address, and forming a hash value label set with the hash values ​​of multiple energy storage system servers. This label set will be used for subsequent load balancing processing.

[0037] In this embodiment, S1 further includes: calculating and summing the real-time load data of multiple energy storage system servers, and the calculation formula is: in n is the number of energy storage system servers, P is the total load of all energy storage system servers during the load monitoring period, m is the number of load balancing units in a single load balancing server, C1 is the rated load of a single load balancing server, C2 is the rated load of the load balancing unit in a single load balancing server, P is the load data at any time during the load monitoring period, t1 is the start time of load monitoring, and t2 is the end time of load monitoring. Through this calculation, the total load of all energy storage system servers during the load monitoring period can be obtained, which provides an important reference for subsequent load balancing processing.

[0038] In this embodiment, the load balancing process of the energy storage system server in S3 according to the real-time load data and hash value tag set of the energy storage system server includes: taking the hash values ​​in the hash value tag set modulo the number n of energy storage system servers to obtain the index of the energy storage system server, and determining that the energy storage system server needs to be dynamically adjusted when the load data of the energy storage system server reaches the preset threshold during the load monitoring time period, and calling the load balancing unit to perform load balancing. Taking the hash values ​​in the hash value tag set modulo the number n of energy storage system servers to obtain the corresponding server index. In this way, a direct mapping relationship is established between the hash value and the energy storage system server. For each hash value tag, its corresponding server index can be determined, that is, the server to which the request represented by the hash value will be assigned. During the load monitoring time period, when the load data of a certain energy storage system server reaches the preset threshold, the system will determine that the server is in a high load state and needs to be dynamically adjusted. At this time, the load balancing server calls the load balancing unit to reallocate tasks or requests to reduce the load pressure of the server. The load balancing unit can achieve this goal by transferring some tasks to other servers with lower loads, ensuring that the load of the entire system is reasonably distributed among the servers and avoiding single-point overload.

[0039] This method ensures the efficient operation of energy storage system servers in the energy internet through real-time monitoring, hash value tag generation and load balancing. By accurately monitoring and dynamically adjusting the load of each server, it not only improves the reliability and stability of the system, but also optimizes the utilization of resources.

[0040] Embodiment 2:

[0041] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned remote monitoring method of energy storage equipment for energy Internet by calling the computer program stored in the memory.

[0042] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a remote monitoring method for energy storage devices for the energy Internet provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0043] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0044] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0045] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A remote monitoring method for energy storage equipment for energy Internet, characterized in that: The specific steps include: S1. Connect the energy storage system servers to the load balancing servers one by one, and monitor the real-time load data of multiple energy storage system servers simultaneously through multiple load balancing servers; S2. Obtain IP address data of the energy storage system server, convert the IP address data into a hash value, and form a hash value tag set with the hash values ​​of multiple energy storage system servers; S3. According to the real-time load data and hash value tag set of the energy storage system server, the load balancing server performs load balancing processing on the energy storage system server.

2. The method for remote monitoring of energy storage equipment for energy internet according to claim 1, characterized in that: The load balancing server in S1 includes a load monitoring unit and at least one load balancing unit.

3. The method for remote monitoring of energy storage equipment for energy internet according to claim 2 is characterized in that: The S1 comprises the following specific steps: S11, configuring a single energy storage system server with a separate load balancing server; S12, within the load monitoring time period, using the load monitoring unit of the load balancing server to monitor the real-time load data of the energy storage system server connected thereto, wherein the real-time load data includes time data and load data; S13. Construct a two-dimensional rectangular coordinate system of time and load to obtain the changing trend of load data with time data.

4. The method for remote monitoring of energy storage equipment for energy internet according to claim 3 is characterized in that: The S2 comprises the following specific steps: S21, converting the IP address data of the energy storage system server into IPv4 format data, segmenting the IPv4 format data according to the segment positions, converting the segmented IPv4 format data segments into binary numbers respectively, connecting all the binary numbers in sequence to form a 32-bit binary number, and converting the 32-bit binary number into a decimal integer, where the decimal integer is a numerical representation of the IP address; S22, taking the numerical representation of the IP address as input, using the CRC32 hash function to perform hash calculation, using the output hash value as the hash value tag of the IP address, and forming a hash value tag set with the hash values ​​of multiple energy storage system servers.

5. The method for remote monitoring of energy storage equipment for energy internet according to claim 4 is characterized in that: The S1 further includes: calculating and summing the real-time load data of multiple energy storage system servers, and the calculation formula is: in n is the number of energy storage system servers, P is the total load of all energy storage system servers during the load monitoring period, m is the number of load balancing units in a single load balancing server, C1 is the rated load of a single load balancing server, C2 is the rated load of the load balancing unit in a single load balancing server, P is the load data at any time during the load monitoring period, t1 is the start time of load monitoring, and t2 is the end time of load monitoring.

6. The method for remote monitoring of energy storage equipment for energy internet according to claim 5 is characterized in that: In S3, load balancing processing is performed on the energy storage system server according to the real-time load data and hash value tag set of the energy storage system server, including: taking the hash values ​​in the hash value tag set modulo the number n of the energy storage system servers to obtain the index of the energy storage system server; when the load data of the energy storage system server reaches a preset threshold within the load monitoring time period, it is determined that the energy storage system server needs to be dynamically adjusted, and the load balancing unit is called to perform load balancing processing.

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