A cloud-edge coordinated control method and system for power distribution gateway based on active detection

Abstract

The present invention discloses a power distribution gateway cloud-edge coordinated control method and system based on active detection, which relates to the field of power distribution technology, including: an architecture classification module; a regional coefficient module; a benchmark data acquisition module; a heat dissipation identification module, wherein the heat dissipation identification module obtains the total heat dissipation value of the power equipment in the monitoring area, and obtains the total heat dissipation value of the running equipment in the monitoring area; a data calculation module, wherein the data calculation module calculates the operating power of the monitoring area and calculates the ambient temperature of the monitoring area. By setting the regional coefficient module, the benchmark data acquisition module, the heat dissipation identification module and the data calculation module, when in use, data can be directly called to save computing power, while ensuring that when adjusting the power equipment, the adjustment accuracy is high, which can avoid energy waste and avoid excessive temperature or high load.

Description

Technical Field

[0001] The present invention relates to the field of power distribution technology, and in particular to a power distribution gateway cloud-edge coordinated control method and system based on active detection. Background Art

[0002] The intelligent gateway for power distribution room is an intelligent control system used to monitor and control the power equipment in the power distribution room. It can realize remote monitoring, fault diagnosis, data collection, energy consumption management, remote control and other functions of power equipment, improving the reliability, safety and energy efficiency of the power distribution system. The intelligent gateway adopts advanced communication technology and intelligent algorithms to achieve interconnection with other intelligent devices to form an intelligent power distribution system. At the same time, the intelligent gateway can also realize remote monitoring and management of the power distribution system through the cloud platform, improving management efficiency and response speed.

[0003] However, due to the different operating conditions and types of power equipment, the existing monitoring methods are unable to comprehensively and accurately monitor buildings, resulting in low reliability of the monitoring results of the power equipment environmental monitoring method. This leads to low adjustment accuracy when the distribution gateway coordinates and controls the power equipment, which can easily cause over-temperature or high load, thereby reducing the service life of the power equipment. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, a distribution gateway cloud-edge coordinated control method and system based on active detection are provided. The technical solution solves the problem raised in the above-mentioned background technology that due to the different operating conditions and types of power equipment, the existing monitoring methods are unable to comprehensively and accurately monitor the buildings, resulting in low reliability of the monitoring results of the power equipment environmental monitoring method, resulting in low adjustment accuracy when the distribution gateway coordinates and controls the power equipment, which can easily cause over-high temperature or high load, thereby reducing the service life of the power equipment.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A cloud-edge coordinated control method for a power distribution gateway based on active detection, comprising:

[0007] Divide the power equipment monitored by the power distribution gateway into at least one monitoring area, and classify the monitoring areas according to the types of power equipment to obtain at least one monitoring area classification;

[0008] Select one of the monitoring area classifications as a benchmark monitoring area classification, obtain a regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and select one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area;

[0009] Generate at least one sample group, the sample group consists of temperature and power load, and obtain the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area;

[0010] Identify the power equipment in the monitoring area, obtain the number of power equipment in the monitoring area, and obtain the total heat dissipation value of the power equipment in the monitoring area;

[0011] Obtain the target power load and ambient target temperature in the monitoring area, calculate the operating power of the monitoring area, and calculate the ambient temperature of the monitoring area;

[0012] Load balance the operating power in the monitoring area and control the temperature of the monitoring area to the ambient temperature of the monitoring area;

[0013] Use distributed cloud storage technology to store the data generated during the monitoring process.

[0014] Preferably, obtaining the regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification comprises the following steps:

[0015] Select one of the monitoring areas in the monitoring area classification as a characteristic monitoring area;

[0016] The preset operating power and the preset ambient temperature are used to control the power distribution in both the reference monitoring area and the characteristic monitoring area;

[0017] Obtaining a reference temperature and a reference power load of a reference monitoring area after power distribution is stabilized, and obtaining a characteristic temperature and a characteristic power load of a characteristic monitoring area after power distribution is stabilized;

[0018] The characteristic temperature is divided by the reference temperature to obtain the first regional coefficient;

[0019] The characteristic power load is divided by the reference power load to obtain the second area coefficient;

[0020] The first regional coefficient and the second regional coefficient are averaged to obtain the regional coefficient.

[0021] Preferably, generating at least one sample group comprises the following steps:

[0022] Obtaining a value range of the temperature of the power equipment and a value range of the power load of the power equipment;

[0023] Divide the value range of the temperature of the power equipment at equal intervals to obtain at least one temperature sampling point;

[0024] Dividing the value range of the power load of the power equipment at equal intervals to obtain at least one power load sampling point;

[0025] At least one temperature sampling point and at least one power load sampling point are randomly paired to obtain at least one sample group.

[0026] Preferably, the step of obtaining the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area comprises the following steps:

[0027] Adjust the operating power and ambient temperature in the reference monitoring area;

[0028] When the power load of the reference monitoring area is smaller than the power load in the sample group, the operating power of the reference monitoring area is increased until the difference between the power load of the reference monitoring area and the power load in the sample group is smaller than a first preset interval, and the operating power at this time is used as the reference operating power;

[0029] When the power load of the reference monitoring area is greater than the power load in the sample group, the operating power of the reference monitoring area is reduced until the difference between the power load of the reference monitoring area and the power load in the sample group is less than a first preset interval, and the operating power at this time is used as the reference operating power;

[0030] When the temperature of the reference monitoring area is lower than the temperature in the sample group, the ambient temperature of the reference monitoring area is increased until the difference between the temperature of the reference monitoring area and the temperature in the sample group is lower than a second preset interval, and the ambient temperature at this time is used as the reference ambient temperature;

[0031] When the temperature of the reference monitoring area is greater than the temperature in the sample group, the ambient temperature of the reference monitoring area is reduced until the difference between the temperature of the reference monitoring area and the temperature in the sample group is less than a second preset interval, and the ambient temperature at this time is used as the reference ambient temperature.

[0032] Preferably, the obtaining of the number of electric power equipment in the monitoring area and the obtaining of the total heat dissipation value of the electric power equipment in the monitoring area comprises the following steps:

[0033] Perform contour recognition on the power equipment, count the contours of the power equipment in the monitoring area, and obtain the number of power equipment in the monitoring area;

[0034] Acquire at least one heat dissipation value of the electric power equipment based on the big data, average the at least one heat dissipation value of the electric power equipment, and obtain an average heat dissipation value of the electric power equipment;

[0035] The average heat dissipation value of the power equipment is multiplied by the number of power equipment in the monitoring area to obtain the total heat dissipation value of the power equipment in the monitoring area.

[0036] Preferably, the calculating and obtaining the operating power of the monitoring area comprises the following steps:

[0037] Obtaining a sample group closest to the target power load and the target ambient temperature as a characteristic sample group;

[0038] The operating power of the monitoring area is obtained by multiplying the benchmark operating power corresponding to the characteristic sample group by the regional coefficient of the monitoring area classification where the monitoring area is located.

[0039] Preferably, the step of calculating the ambient temperature of the monitoring area comprises the following steps:

[0040] The base ambient temperature corresponding to the characteristic sample group is multiplied by the regional coefficient of the monitoring area classification where the monitoring area is located to obtain the transit ambient temperature;

[0041] The ambient temperature of the monitoring area is obtained by subtracting the total heat dissipation value of the power equipment in the monitoring area from the transit ambient temperature.

[0042] Preferably, load balancing the operating power in the monitoring area includes the following steps:

[0043] Obtain the actual operating power in the monitoring area;

[0044] The calculated portion of the actual operating power in the monitoring area exceeding the operating power in the monitoring area is used as the balancing portion;

[0045] Acquire at least one idle monitoring area as an idle monitoring area;

[0046] Evenly distribute the balanced portion to the power equipment in the idle monitoring area.

[0047] Preferably, the use of distributed cloud storage technology to store data generated during the monitoring process includes the following steps:

[0048] Divide and shard the data generated during the monitoring process, and store the data evenly on multiple distributed database nodes;

[0049] Set up data replication and redundant backup strategies in distributed databases, using master-slave replication or multi-master replication to replicate data to multiple distributed database nodes;

[0050] Use distributed transaction processing technology to achieve data consistency and synchronization in distributed databases;

[0051] Use data sharding routing method to implement load balancing and performance optimization strategies in distributed databases;

[0052] In a distributed database, a disaster recovery and fault recovery mechanism including fault detection and automatic switching is set up.

[0053] A power distribution gateway cloud-edge coordination control system based on active detection, used to implement the above-mentioned power distribution gateway cloud-edge coordination control method based on active detection, comprising:

[0054] An architecture classification module, wherein the architecture classification module divides the power equipment monitored by the power distribution gateway into at least one monitoring area, and classifies the monitoring area according to the type of the power equipment to obtain at least one monitoring area classification;

[0055] A regional coefficient module, wherein the regional coefficient module selects one of the monitoring area classifications as a benchmark monitoring area classification, obtains a regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and selects one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area;

[0056] A benchmark data acquisition module, wherein the benchmark data acquisition module generates at least one sample group, the sample group consists of temperature and power load, and acquires a benchmark operating power and a benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area;

[0057] A heat dissipation identification module, which identifies the power equipment in the monitoring area, obtains the number of power equipment in the monitoring area, and obtains the total heat dissipation value of the power equipment in the monitoring area;

[0058] A data calculation module, wherein the data calculation module obtains a target power load and an environmental target temperature in a monitoring area, calculates an operating power of the monitoring area, and calculates an environmental temperature of the monitoring area;

[0059] A data balancing module, which performs load balancing on the operating power in the monitoring area and controls the temperature of the monitoring area to the ambient temperature of the monitoring area;

[0060] A distributed cloud storage module uses distributed cloud storage technology to store data generated during the monitoring process.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] By setting up the regional coefficient module, the benchmark data acquisition module, the heat dissipation identification module, the power equipment breathing identification module and the data calculation module, different identifications can be performed according to the operating conditions of the power equipment and the types of the power equipment, so as to accurately monitor the building and make adjustments based on the monitoring results. When adjusting, the regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification and the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group are constructed. When in use, data can be called directly to save computing power. At the same time, it is ensured that when the power equipment is adjusted, the adjustment accuracy is high, which can avoid energy waste and avoid excessive temperature or high load. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a structural diagram of the cloud-edge coordinated control method of the power distribution gateway based on active detection of the present invention;

[0064] Figure 2 A schematic diagram of a process flow for obtaining a regional coefficient of a monitoring area classification relative to a benchmark monitoring area classification according to the present invention;

[0065] Figure 3 A schematic diagram of a process for generating at least one sample group according to the present invention;

[0066] Figure 4 A schematic diagram of a process of obtaining a benchmark operating power and a benchmark ambient temperature corresponding to the parameters in a sample group in a benchmark monitoring area according to the present invention;

[0067] Figure 5 A schematic diagram of a flow chart of obtaining the number of power equipment in a monitoring area and obtaining the total heat dissipation value of power equipment in the monitoring area according to the present invention;

[0068] Figure 6 A schematic diagram of the operating power flow of the monitoring area obtained by calculation according to the present invention;

[0069] Figure 7 A schematic diagram of a flow chart of calculating the ambient temperature of a monitoring area according to the present invention;

[0070] Figure 8 It is a schematic diagram of the process of load balancing the operating power within the monitoring area of ​​the present invention;

[0071] Fig. 9 This is a schematic diagram of the data flow generated by the monitoring process using distributed cloud storage technology in the present invention. DETAILED DESCRIPTION

[0072] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0073] Reference Figure 1 As shown, a distribution gateway cloud-edge coordinated control method based on active detection includes:

[0074] Divide the power equipment monitored by the power distribution gateway into at least one monitoring area, and classify the monitoring areas according to the types of power equipment to obtain at least one monitoring area classification;

[0075] Select one of the monitoring area classifications as a benchmark monitoring area classification, obtain a regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and select one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area;

[0076] Generate at least one sample group, the sample group consists of temperature and power load, and obtain the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area;

[0077] Identify the power equipment in the monitoring area, obtain the number of power equipment in the monitoring area, and obtain the total heat dissipation value of the power equipment in the monitoring area;

[0078] Obtain the target power load and ambient target temperature in the monitoring area, calculate the operating power of the monitoring area, and calculate the ambient temperature of the monitoring area;

[0079] Load balance the operating power in the monitoring area and control the temperature of the monitoring area to the ambient temperature of the monitoring area;

[0080] Use distributed cloud storage technology to store the data generated during the monitoring process.

[0081] Reference Figure 2 As shown, obtaining the regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification includes the following steps:

[0082] Select one of the monitoring areas in the monitoring area classification as a characteristic monitoring area;

[0083] The preset operating power and the preset ambient temperature are used to control the power distribution in both the reference monitoring area and the characteristic monitoring area;

[0084] Obtaining a reference temperature and a reference power load of a reference monitoring area after power distribution is stabilized, and obtaining a characteristic temperature and a characteristic power load of a characteristic monitoring area after power distribution is stabilized;

[0085] The characteristic temperature is divided by the reference temperature to obtain the first regional coefficient;

[0086] The characteristic power load is divided by the reference power load to obtain the second area coefficient;

[0087] The first regional coefficient and the second regional coefficient are averaged to obtain the regional coefficient.

[0088] Although the purpose can be achieved by using a dynamic adjustment method for each monitoring area, it consumes a lot of computing power. After selecting the benchmark monitoring area classification, since the relative conditions of the benchmark monitoring area and the characteristic monitoring area are consistent, the adjustment parameters between them are approximately different by a proportional coefficient. Therefore, the proportional coefficient, that is, the regional coefficient, can be obtained in advance. When adjusting, only the benchmark monitoring area is adjusted, and the remaining monitoring areas can be calculated based on the regional coefficient and the actual adjustment parameters of the benchmark monitoring area. However, this result does not include the impact of power equipment and equipment operation. Therefore, compensation needs to be made in subsequent operations.

[0089] Reference Figure 3 As shown, generating at least one sample group includes the following steps:

[0090] Obtaining a value range of the temperature of the power equipment and a value range of the power load of the power equipment;

[0091] Divide the value range of the temperature of the power equipment at equal intervals to obtain at least one temperature sampling point;

[0092] Dividing the value range of the power load of the power equipment at equal intervals to obtain at least one power load sampling point;

[0093] Randomly pairing at least one temperature sampling point with at least one power load sampling point to obtain at least one sample group;

[0094] As long as the interval between at least one temperature sampling point is small enough and the interval between at least one power load sampling point is small enough, the sample group can approximate the combination of actual possible values ​​of the power equipment temperature and the power load of the power equipment, and then call the benchmark operating power and benchmark ambient temperature corresponding to the data of the sample group for estimation, and the accuracy of the estimation can meet the usage requirements.

[0095] Reference Figure 4 As shown, obtaining the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area includes the following steps:

[0096] Adjust the operating power and ambient temperature in the reference monitoring area;

[0097] When the power load of the reference monitoring area is smaller than the power load in the sample group, the operating power of the reference monitoring area is increased until the difference between the power load of the reference monitoring area and the power load in the sample group is smaller than a first preset interval, and the operating power at this time is used as the reference operating power;

[0098] When the power load of the reference monitoring area is greater than the power load in the sample group, the operating power of the reference monitoring area is reduced until the difference between the power load of the reference monitoring area and the power load in the sample group is less than a first preset interval, and the operating power at this time is used as the reference operating power;

[0099] When the temperature of the reference monitoring area is lower than the temperature in the sample group, the ambient temperature of the reference monitoring area is increased until the difference between the temperature of the reference monitoring area and the temperature in the sample group is lower than a second preset interval, and the ambient temperature at this time is used as the reference ambient temperature;

[0100] When the temperature of the reference monitoring area is greater than the temperature in the sample group, the ambient temperature of the reference monitoring area is reduced until the difference between the temperature of the reference monitoring area and the temperature in the sample group is less than a second preset interval, and the ambient temperature at this time is used as the reference ambient temperature.

[0101] Reference Figure 5 As shown, obtaining the number of power equipment in the monitoring area and obtaining the total heat dissipation value of the power equipment in the monitoring area includes the following steps:

[0102] Perform contour recognition on the power equipment, count the contours of the power equipment in the monitoring area, and obtain the number of power equipment in the monitoring area;

[0103] Acquire at least one heat dissipation value of the electric power equipment based on the big data, average the at least one heat dissipation value of the electric power equipment, and obtain an average heat dissipation value of the electric power equipment;

[0104] The average heat dissipation value of the power equipment is multiplied by the number of power equipment in the monitoring area to obtain the total heat dissipation value of the power equipment in the monitoring area.

[0105] The total heat dissipation value of the power equipment in the monitoring area will affect the temperature in the monitoring area. Therefore, when making adjustments, it is necessary to lower the temperature to offset the heat dissipation value of the power equipment in the monitoring area.

[0106] Reference Figure 6 As shown, calculating the operating power of the monitoring area includes the following steps:

[0107] Obtaining a sample group closest to the target power load and the target ambient temperature as a characteristic sample group;

[0108] The operating power of the monitoring area is obtained by multiplying the benchmark operating power corresponding to the characteristic sample group by the regional coefficient of the monitoring area classification where the monitoring area is located.

[0109] The operating power of the monitored area can be obtained based on the regional coefficient and the benchmark operating power corresponding to the characteristic sample group.

[0110] Reference Figure 7 As shown, calculating the ambient temperature of the monitoring area includes the following steps:

[0111] The base ambient temperature corresponding to the characteristic sample group is multiplied by the regional coefficient of the monitoring area classification where the monitoring area is located to obtain the transit ambient temperature;

[0112] The ambient temperature of the monitoring area is obtained by subtracting the total heat dissipation value of the power equipment in the monitoring area from the transit ambient temperature.

[0113] The transit ambient temperature can be obtained based on the regional coefficient and the benchmark ambient temperature corresponding to the characteristic sample group. However, the transit ambient temperature does not take into account the heat effect of the heat dissipation of the power equipment. Therefore, it is necessary to subtract the total heat dissipation value of the power equipment in the monitoring area to obtain the ambient temperature of the monitoring area.

[0114] Reference Figure 8As shown, load balancing the operating power in the monitoring area includes the following steps:

[0115] Obtain the actual operating power in the monitoring area;

[0116] The calculated portion of the actual operating power in the monitoring area exceeding the operating power in the monitoring area is used as the balancing portion;

[0117] Acquire at least one idle monitoring area as an idle monitoring area;

[0118] Evenly distribute the balanced portion to the power equipment in the idle monitoring area.

[0119] The actual operating power in the monitoring area may exceed the operating power in the monitoring area. As a result, its composite power is too large. If it operates in this way for a long time, it is easy to age and reduce its service life. At this time, other power equipment in the monitoring area is idle. Therefore, the load can be distributed to other power equipment, thereby reducing the load and extending its life.

[0120] Reference Fig. 9 As shown, using distributed cloud storage technology to store the data generated by the monitoring process includes the following steps:

[0121] Divide and shard the data generated during the monitoring process, and store the data evenly on multiple distributed database nodes;

[0122] Set up data replication and redundant backup strategies in distributed databases, using master-slave replication or multi-master replication to replicate data to multiple distributed database nodes;

[0123] Use distributed transaction processing technology to achieve data consistency and synchronization in distributed databases;

[0124] Use data sharding routing method to implement load balancing and performance optimization strategies in distributed databases;

[0125] In a distributed database, a disaster recovery and fault recovery mechanism including fault detection and automatic switching is set up.

[0126] A power distribution gateway cloud-edge coordination control system based on active detection, used in the above-mentioned power distribution gateway cloud-edge coordination control method based on active detection, comprising:

[0127] An architecture classification module, wherein the architecture classification module divides the power equipment monitored by the power distribution gateway into at least one monitoring area, and classifies the monitoring area according to the type of the power equipment to obtain at least one monitoring area classification;

[0128] A regional coefficient module, wherein the regional coefficient module selects one of the monitoring area classifications as a benchmark monitoring area classification, obtains a regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and selects one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area;

[0129] A benchmark data acquisition module, wherein the benchmark data acquisition module generates at least one sample group, the sample group consists of temperature and power load, and acquires a benchmark operating power and a benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area;

[0130] A heat dissipation identification module, which identifies the power equipment in the monitoring area, obtains the number of power equipment in the monitoring area, and obtains the total heat dissipation value of the power equipment in the monitoring area;

[0131] A data calculation module, wherein the data calculation module obtains a target power load and an environmental target temperature in a monitoring area, calculates an operating power of the monitoring area, and calculates an environmental temperature of the monitoring area;

[0132] A data balancing module, which performs load balancing on the operating power in the monitoring area and controls the temperature of the monitoring area to the ambient temperature of the monitoring area;

[0133] A distributed cloud storage module uses distributed cloud storage technology to store data generated during the monitoring process.

[0134] The working process of the above-mentioned distribution gateway cloud-edge coordinated control system based on active detection is as follows:

[0135] Step 1: The architecture classification module divides the power equipment monitored by the power distribution gateway into at least one monitoring area, and classifies the monitoring area according to the type of power equipment to obtain at least one monitoring area classification;

[0136] Step 2: The regional coefficient module selects one of the monitoring area classifications as the benchmark monitoring area classification, obtains the regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and selects one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area;

[0137] Step 3: The benchmark data acquisition module generates at least one sample group, the sample group consists of temperature and power load, and obtains the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area;

[0138] Step 4: The heat dissipation identification module identifies the power equipment in the monitoring area, obtains the number of power equipment in the monitoring area, and obtains the total heat dissipation value of the power equipment in the monitoring area;

[0139] Step 5: The data calculation module obtains the target power load and the target ambient temperature in the monitoring area, calculates the operating power of the monitoring area, and calculates the ambient temperature of the monitoring area;

[0140] Step 6: The data balancing module load balances the operating power in the monitoring area and controls the temperature of the monitoring area to the ambient temperature of the monitoring area;

[0141] Step 7: The distributed cloud storage module uses distributed cloud storage technology to store the data generated during the monitoring process.

[0142] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned distribution gateway cloud-edge coordination control method based on active detection is executed.

[0143] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0144] By setting up the regional coefficient module, the benchmark data acquisition module, the heat dissipation identification module, the power equipment breathing identification module and the data calculation module, different identifications can be performed according to the operating conditions of the power equipment and the types of the power equipment, so as to accurately monitor the building and make adjustments based on the monitoring results. When adjusting, the regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification and the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group are constructed. When in use, data can be called directly to save computing power. At the same time, it is ensured that when the power equipment is adjusted, the adjustment accuracy is high, which can avoid energy waste and avoid excessive temperature or high load.

[0145] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A cloud-edge coordinated control method for power distribution gateway based on active detection, characterized in that: include: Divide the power equipment monitored by the power distribution gateway into at least one monitoring area, and classify the monitoring areas according to the types of power equipment to obtain at least one monitoring area classification; Select one of the monitoring area classifications as a benchmark monitoring area classification, obtain a regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and select one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area; Generate at least one sample group, the sample group consists of temperature and power load, and obtain the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area; Identify the power equipment in the monitoring area, obtain the number of power equipment in the monitoring area, and obtain the total heat dissipation value of the power equipment in the monitoring area; Obtain the target power load and ambient target temperature in the monitoring area, calculate the operating power of the monitoring area, and calculate the ambient temperature of the monitoring area; Load balance the operating power in the monitoring area and control the temperature of the monitoring area to the ambient temperature of the monitoring area; Use distributed cloud storage technology to store data generated during the monitoring process; Obtaining the benchmark operating power and benchmark ambient temperature corresponding to the parameters in the sample group for the benchmark monitoring area includes the following steps: Adjust the operating power and ambient temperature in the reference monitoring area; When the power load of the reference monitoring area is smaller than the power load in the sample group, the operating power of the reference monitoring area is increased until the difference between the power load of the reference monitoring area and the power load in the sample group is smaller than the first preset interval, and the operating power at this time is used as the reference operating power; When the power load of the reference monitoring area is greater than the power load in the sample group, the operating power of the reference monitoring area is reduced until the difference between the power load of the reference monitoring area and the power load in the sample group is less than a first preset interval, and the operating power at this time is used as the reference operating power; When the temperature of the reference monitoring area is lower than the temperature in the sample group, the ambient temperature of the reference monitoring area is increased until the difference between the temperature of the reference monitoring area and the temperature in the sample group is lower than a second preset interval, and the ambient temperature at this time is used as the reference ambient temperature; When the temperature of the reference monitoring area is greater than the temperature in the sample group, the ambient temperature of the reference monitoring area is reduced until the difference between the temperature of the reference monitoring area and the temperature in the sample group is less than a second preset interval, and the ambient temperature at this time is used as the reference ambient temperature.

2. According to the active detection-based cloud-edge coordinated control method for power distribution gateways according to claim 1, it is characterized in that: The obtaining of the regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification comprises the following steps: Select one of the monitoring areas in the monitoring area classification as a characteristic monitoring area; The preset operating power and the preset ambient temperature are used to control the power distribution in both the reference monitoring area and the characteristic monitoring area; Obtaining a reference temperature and a reference power load of a reference monitoring area after power distribution is stabilized, and obtaining a characteristic temperature and a characteristic power load of a characteristic monitoring area after power distribution is stabilized; The characteristic temperature is divided by the reference temperature to obtain the first regional coefficient; The characteristic power load is divided by the reference power load to obtain the second area coefficient; The first regional coefficient and the second regional coefficient are averaged to obtain the regional coefficient.

3. According to the active detection-based cloud-edge coordinated control method for power distribution gateways according to claim 2, it is characterized in that: Generating at least one sample group comprises the following steps: Obtaining a value range of the temperature of the power equipment and a value range of the power load of the power equipment; Divide the value range of the temperature of the power equipment at equal intervals to obtain at least one temperature sampling point; Dividing the value range of the power load of the power equipment at equal intervals to obtain at least one power load sampling point; At least one temperature sampling point and at least one power load sampling point are randomly paired to obtain at least one sample group.

4. The method for cloud-edge coordinated control of power distribution gateway based on active detection according to claim 3 is characterized in that: The obtaining of the number of electric power equipment in the monitoring area and the total heat dissipation value of the electric power equipment in the monitoring area comprises the following steps: Perform contour recognition on the power equipment, count the contours of the power equipment in the monitoring area, and obtain the number of power equipment in the monitoring area; Acquire at least one heat dissipation value of the electric power equipment based on the big data, average the at least one heat dissipation value of the electric power equipment, and obtain an average heat dissipation value of the electric power equipment; The average heat dissipation value of the power equipment is multiplied by the number of power equipment in the monitoring area to obtain the total heat dissipation value of the power equipment in the monitoring area.

5. According to the method of cloud-edge coordinated control of power distribution gateway based on active detection in claim 4, it is characterized in that: The calculation of the operating power of the monitoring area comprises the following steps: Obtaining a sample group closest to the target power load and the target ambient temperature as a characteristic sample group; The operating power of the monitoring area is obtained by multiplying the benchmark operating power corresponding to the characteristic sample group by the regional coefficient of the monitoring area classification where the monitoring area is located.

6. The method for cloud-edge coordinated control of power distribution gateway based on active detection according to claim 5 is characterized in that: The calculation of the ambient temperature of the monitoring area comprises the following steps: The base ambient temperature corresponding to the characteristic sample group is multiplied by the regional coefficient of the monitoring area classification where the monitoring area is located to obtain the transit ambient temperature; The ambient temperature of the monitoring area is obtained by subtracting the total heat dissipation value of the power equipment in the monitoring area from the transit ambient temperature.

7. The method for cloud-edge coordinated control of power distribution gateway based on active detection according to claim 6 is characterized in that: The load balancing of the operating power in the monitoring area comprises the following steps: Obtain the actual operating power in the monitoring area; The calculated portion of the actual operating power in the monitoring area exceeding the operating power in the monitoring area is used as the balancing portion; Acquire at least one idle monitoring area as an idle monitoring area; Evenly distribute the balanced portion to the power equipment in the idle monitoring area.

8. The method for cloud-edge coordinated control of power distribution gateway based on active detection according to claim 7 is characterized in that: The use of distributed cloud storage technology to store data generated during the monitoring process includes the following steps: Divide and shard the data generated during the monitoring process, and store the data evenly on multiple distributed database nodes; Set up data replication and redundant backup strategies in distributed databases, using master-slave replication or multi-master replication to replicate data to multiple distributed database nodes; Use distributed transaction processing technology to achieve data consistency and synchronization in distributed databases; Use data sharding routing method to implement load balancing and performance optimization strategies in distributed databases; In a distributed database, a disaster recovery and fault recovery mechanism including fault detection and automatic switching is set up.

9. A power distribution gateway cloud-edge coordination control system based on active detection, used to implement the power distribution gateway cloud-edge coordination control method based on active detection as described in any one of claims 1-8, characterized in that: include: An architecture classification module, wherein the architecture classification module divides the power equipment monitored by the power distribution gateway into at least one monitoring area, and classifies the monitoring area according to the type of the power equipment to obtain at least one monitoring area classification; A regional coefficient module, wherein the regional coefficient module selects one of the monitoring area classifications as a benchmark monitoring area classification, obtains a regional coefficient of the monitoring area classification relative to the benchmark monitoring area classification, and selects one of the monitoring areas in the benchmark monitoring area classification as the benchmark monitoring area; A benchmark data acquisition module, wherein the benchmark data acquisition module generates at least one sample group, the sample group consists of temperature and power load, and acquires a benchmark operating power and a benchmark ambient temperature corresponding to the parameters in the sample group in the benchmark monitoring area; A heat dissipation identification module, which identifies the power equipment in the monitoring area, obtains the number of power equipment in the monitoring area, and obtains the total heat dissipation value of the power equipment in the monitoring area; A data calculation module, wherein the data calculation module obtains a target power load and an environmental target temperature in a monitoring area, calculates an operating power of the monitoring area, and calculates an environmental temperature of the monitoring area; A data balancing module, which performs load balancing on the operating power in the monitoring area and controls the temperature of the monitoring area to the ambient temperature of the monitoring area; A distributed cloud storage module uses distributed cloud storage technology to store data generated during the monitoring process.

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