Distributed park infrastructure multi-node cooperative processing method based on edge computing

Abstract

The invention discloses a distributed park infrastructure multi-node cooperative processing method based on edge computing, and belongs to the technical field of edge computing. The method comprises the following steps: deploying distributed edge nodes beside a park infrastructure and adding line interface identifiers, and establishing exclusive association between the nodes and the infrastructure; time scales are configured by taking days as a cycle, peak periods are determined in combination with historical data, and cooperative processing periods are divided; collecting power consumption of each time period, calculating a consumption ratio and a statistical index, and quantifying a consumption ratio interval; and based on the initial power distribution quantity and the proportion interval, estimating a temporary distribution quantity when the facility switches the path and feeding back the temporary distribution quantity. The system realizes the functions through three modules of node deployment and time management, energy consumption data evaluation and power collaborative optimization. The method solves the problems of high delay, unreasonable power distribution and poor node collaboration of the existing centralized processing, improves the operation stability of the park infrastructure, reduces the energy consumption waste, and is suitable for infrastructure management of various industrial parks and commercial parks.

Description

Technical Field

[0001] This invention relates to the field of edge computing technology, specifically to a multi-node collaborative processing method for distributed campus infrastructure based on edge computing. Background Technology

[0002] In the operation and management of park infrastructure (such as production equipment, office equipment, and refrigeration systems), the rational allocation of power resources and multi-node coordination are core requirements for ensuring stable facility operation and reducing energy consumption. Currently, most mainstream park infrastructure management adopts a "centralized data processing + fixed power distribution" model, which has the following significant drawbacks: (1) High processing latency and poor real-time performance are reflected in the centralized mode, where all infrastructure operation data need to be transmitted to a remote cloud or central server, processed, and then control commands are issued. For large parks (such as those covering more than 50 acres and with more than 100 facilities), the data transmission path is several kilometers long, and the command response time is usually more than 10 seconds. This cannot meet the real-time requirements of power regulation for key equipment in the production workshop (such as large machine tools and assembly lines) (requiring ≤5 seconds), which can easily lead to equipment shutdown due to power supply lag.

[0003] (2) The rigid power distribution leads to serious waste of resources. This is reflected in the fact that existing technologies mostly adopt fixed power distribution strategies, such as setting a uniform allocation amount according to facility type, without combining historical operating data to identify the peak / off-peak operating periods of facilities. For example, production workshop equipment is in peak operating state from 8:00 to 12:00, requiring a high power supply, but the fixed allocation amount cannot be dynamically adjusted, resulting in insufficient power for some equipment during peak hours; while after 18:00, the equipment operates at low load, and the fixed allocation amount remains high, causing a waste of power resources. According to statistics, under this model, the energy waste in the park during off-peak hours can reach 25%-30%.

[0004] (3) Lack of multi-node coordination and large fluctuations in grid load are reflected in the independent operation of each infrastructure node in the park and the lack of a coordination mechanism based on the overall energy consumption of the grid path. When some equipment in a certain grid path is running at full load, other equipment still maintains the original energy consumption, which can easily lead to overload of the total load of the path (overload rate can reach 15%-20%), causing grid tripping; while some paths have too low load (below 50% of the rated capacity), resulting in idle grid resources and poor overall operational stability.

[0005] (4) The existing solutions are not adaptable to different scenarios and have high maintenance costs. This is because the existing solutions are mostly designed for single-type parks (such as pure office building parks) and cannot adjust parameters according to the facility characteristics of different scenarios such as industrial parks, commercial parks, and science and technology parks (such as industrial parks having many high-energy-consuming equipment and commercial parks having dense office equipment). At the same time, the maintenance of centralized servers requires professional technicians, and the maintenance cost is higher than that of distributed solutions.

[0006] In summary, existing park infrastructure management solutions cannot meet the requirements of "low latency, precision, and collaboration," and there is an urgent need for a multi-node collaborative processing method based on a distributed architecture and data-driven approach to solve the aforementioned technical pain points. Summary of the Invention

[0007] The purpose of this invention is to provide a multi-node collaborative processing method for distributed campus infrastructure based on edge computing, so as to solve the problems mentioned in the background art.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This system is a distributed campus infrastructure multi-node collaborative processing system based on edge computing. The system includes a storage unit for storing computer programs and a processor for executing those programs. The processor also integrates three core functional modules: a node deployment and time management module, an energy consumption data assessment module, and a power collaborative optimization module. These three modules achieve power collaborative processing of multiple nodes in the campus infrastructure through data interaction. This includes establishing connections with the power grid, edge nodes, and campus infrastructure within the campus via Ethernet, 4G / 5G, or LoRa wireless communication, and supporting interface integration with the campus power management platform via API to achieve data sharing and collaborative control. The node deployment and time management module is used to deploy distributed edge nodes in the power grid path corresponding to the park infrastructure, match each edge node with a line interface identifier, establish a unique association between the edge node and the park infrastructure, and realize the accurate collection of infrastructure power data and the directional transmission of control commands. It is also used to uniformly configure the command trigger time scale for adjusting the power distribution of infrastructure by each edge node with a daily time cycle. By statistically analyzing the number of infrastructure starts within each time range of the historical cycle, the peak operation start scale is determined, and two adjacent time scales are divided into a collaborative processing period to form a time period management system adapted to the operation pattern of the infrastructure. The energy consumption data assessment module is used to collect the power consumption of park infrastructure fed back by each line interface during each collaborative processing period, calculate the power consumption ratio of a single line interface, and assess the average and dispersion of the consumption ratio of each line interface based on the consumption ratio of each collaborative period throughout the day; it is also used to obtain the maximum and minimum consumption ratio of all line interfaces during each collaborative processing period for each power grid path, and determine the reasonable consumption ratio range of the line interface in the corresponding power grid path by combining the average and dispersion of the consumption ratio of a single line interface, covering the normal energy consumption fluctuation range of the infrastructure. The power coordination optimization module is used to preset the initial power allocation of each edge node to the corresponding infrastructure. When the power supply of the park infrastructure is temporarily changed due to power grid path switching or load adjustment, the consumption ratio range of the corresponding line interface of the park infrastructure is called to estimate the temporary power allocation range and feed it back to the staff monitoring port in real time to realize the dynamic allocation of auxiliary power resources.

[0009] As a preferred embodiment of the present invention, the node deployment and time management module includes: The node deployment submodule generates an edge node deployment plan based on the type, energy consumption level, and distribution location of the park's infrastructure. It guides the physical installation and network configuration of edge nodes and establishes a database linking line interface identifiers with the infrastructure, supporting the query, modification, and deletion of identifier information. The time management submodule provides a time scale configuration interface, allowing staff to customize scale intervals, automatically count the number of infrastructure startups at each scale within a historical period, generate a peak operation start scale report, and automatically divide collaborative processing time periods to form a time period management list.

[0010] As a preferred embodiment of the present invention, the energy consumption data evaluation module includes: The data acquisition submodule collects power consumption data in real time through the communication channel between the edge nodes and the infrastructure. It supports data retransmission. When the network is interrupted, the edge nodes cache the data locally and automatically upload it after the network is restored. The percentage calculation submodule automatically calculates the consumption percentage for each collaborative processing period, generates a consumption percentage trend chart, and displays it by day, week, and month. It also calculates the mean and coefficient of variation and marks line interfaces with abnormal energy consumption fluctuations. The interval quantization submodule automatically calculates the consumption ratio interval based on the maximum energy consumption of the power grid path and the statistical indicators of a single interface, and supports manual adjustment of the interval.

[0011] As a preferred embodiment of the present invention, the power collaborative optimization module includes: The initial allocation management submodule allows staff to preset the initial power allocation for different types of infrastructure and establish the relationship between the allocation and infrastructure type, season, and time period. The temporary estimation submodule automatically retrieves the energy consumption data of the target path and the consumption ratio range of the corresponding interface when the infrastructure switches the grid path, calculates the temporary power allocation range, and generates allocation suggestions. The feedback submodule pushes the temporary allocation range to the staff's monitoring terminal in real time. The monitoring terminal supports PC and mobile terminals and records the staff's final allocation results to form an allocation log.

[0012] A multi-node collaborative processing method for distributed campus infrastructure based on edge computing, comprising the following steps: Step S1: Deploy distributed edge nodes in the power grid pathways corresponding to the park's infrastructure, match each edge node with a line interface identifier, establish a unique association between the edge node and the park's infrastructure, and realize the accurate collection of infrastructure power data and the targeted transmission of control commands; Step S2: Using a daily time cycle, uniformly configure the command trigger time scale for adjusting the power distribution of infrastructure at each edge node. By statistically analyzing the number of infrastructure starts within each time range of the historical cycle, determine the peak operation start scale, divide two adjacent time scales into a collaborative processing period, and form a time period management system that adapts to the operation pattern of infrastructure. Step S3: During each collaborative processing period, collect the power consumption of the park infrastructure fed back by each line interface, calculate the power consumption ratio of a single line interface, and evaluate the mean and dispersion of the consumption ratio of each line interface based on the consumption ratio of each collaborative period throughout the day. Step S4: For each power grid path, obtain the maximum and minimum consumption ratio of all line interfaces in each collaborative processing period. Combine the average and dispersion of the consumption ratio of a single line interface to determine the reasonable consumption ratio range of the line interface in the corresponding power grid path, covering the normal energy consumption fluctuation range of the infrastructure. Step S5: Preset the initial power allocation of each edge node to the corresponding infrastructure. When the power supply of the park infrastructure is temporarily changed due to grid path switching or load adjustment, call the consumption ratio range of the corresponding line interface of the park infrastructure, estimate the temporary power allocation range, and feed it back to the staff monitoring port in real time to realize the dynamic allocation of auxiliary power resources.

[0013] As a preferred embodiment of the present invention, the specific implementation process of step S1 includes: Distributed edge nodes are deployed in the park's infrastructure to regulate the power distribution of the facilities; based on the power grid pathways of the park's infrastructure, line interface identifiers are attached to the edge nodes, where each line interface identifier corresponds to a connection to one piece of park infrastructure. Let the j-th line interface be denoted as Let the k-th power grid path be denoted as Furthermore, the power grid path includes line interfaces adapted to the park's infrastructure services to form the power grid path. Line interface set ,in, Indicates the power grid path The total number of line interfaces included; It should be noted that deploying edge nodes close to the park's infrastructure (within 100 meters) replaces traditional centralized servers, enabling local collection of power data and local issuance of commands. By establishing a unique association between edge nodes and infrastructure through line interface identifiers, the accuracy of data attribution is ensured, and data from different facilities is avoided from being mixed up.

[0014] As a preferred embodiment of the present invention, the specific implementation process of step S2 includes: The command trigger time scale is uniformly configured when adjusting the power distribution of each edge node adjustment facility. The time cycle is based on a day. The number of times the park's infrastructure is started within each time range of a previous day is counted. The time range with the highest percentage of start-ups is taken as the starting time range of the park's infrastructure peak operation, forming a one-dimensional command time scale line with a time cycle of a day. Based on a one-dimensional instruction time scale, a collaborative processing period is formed by two adjacent scales; It should be noted that, using a daily cycle, the system identifies the peak operation start-up time scale (the time scale with the highest percentage of start-ups) by statistically analyzing the number of facilities started up at each time point within a 30-day period. Adjacent time points are then divided into collaborative processing periods. This design enables a deep match between power regulation and facility operation patterns. For example, during the collaborative processing period in the production workshop during peak hours (8:00-12:00), priority is given to ensuring the power supply for high-energy-consuming equipment; during the collaborative processing period in off-peak hours (22:00-6:00 the next day), the power allocation for non-essential facilities is automatically reduced to avoid resource waste.

[0015] As a preferred embodiment of the present invention, the specific implementation process of step S3 includes: Get the collaborative processing time period of the day The k-th power grid path The j-th line interface The feedback power consumption is obtained from the line interface. The consumption percentage is denoted as The consumption ratio is the ratio of the total power consumption during the same collaborative processing period to the cumulative sum of power consumption reported by each line interface in the power grid pathway, where e is the scale number. This represents the e-th scale mark. This represents the (e+1)th tick mark; Evaluate line interfaces based on consumption percentage. Average percentage of daily consumption with standard deviation ,and , .

[0016] As a preferred embodiment of the present invention, the specific implementation process of step S4 includes: Based on the collaborative processing time of the day In the kth power grid path Line interface set In the middle, obtain the maximum value of the consumption ratio of the line interface. and minimum value ; Combined with the average consumption ratio with standard deviation Quantize the j-th line interface The k-th power grid path Consumption percentage range ,in, , ; It should be noted that by collecting the power consumption of each coordinated period, the consumption ratio (reflecting the energy consumption ratio of a single facility in the power grid path) is calculated, and the mean (reflecting the normal level of energy consumption) and dispersion (reflecting the stability of energy consumption) are evaluated. Combined with the maximum and minimum energy consumption of the power grid path, the consumption ratio range is quantified. This range can cover normal energy consumption fluctuations of facilities (such as energy consumption changes caused by equipment start-up and shutdown) and filter abnormal energy consumption (such as a sudden increase in energy consumption caused by equipment failure).

[0017] As a preferred embodiment of the present invention, the specific implementation process of step S5 includes: Obtain the distributed edge node initialization adjustment line interface The corresponding power allocation G for the park's infrastructure; if the park's infrastructure temporarily switches to the power grid... At that time, retrieve the consumption percentage range. And estimate the range of temporary power allocation for the park's infrastructure. And feedback is sent to the staff's interface; It should be noted that when infrastructure needs to switch paths due to grid path failure or load adjustment, the system automatically retrieves the energy consumption data of the target path and the consumption ratio range of the corresponding facilities, and estimates the temporary power allocation range in combination with the initial allocation amount. During this process, all facilities in the grid path are coordinated and adjusted based on the ratio range to avoid high energy consumption of a single facility leading to path overload, and to ensure that the total grid load is controlled within the rated capacity, thereby improving the overall operational stability.

[0018] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By deploying distributed edge nodes alongside park infrastructure and attaching line interface identifiers, a unique association is established between nodes and facilities; a daily time scale is configured, and peak periods are determined and collaborative processing periods are divided based on historical data; power consumption in each period is collected, consumption ratios and statistical indicators are calculated, and the consumption ratio range is quantified; based on the initial power allocation and ratio range, the temporary allocation during facility switching is estimated and fed back. The system achieves the above functions through three modules: node deployment and time management, energy consumption data evaluation, and power collaborative optimization. This invention solves the problems of high latency, unreasonable power allocation, and poor node collaboration in existing centralized processing, improves the operational stability of park infrastructure, reduces energy waste, and is suitable for infrastructure management in various industrial parks and commercial parks. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0020] Figure 1 This is a schematic diagram illustrating the steps of the multi-node collaborative processing method for distributed campus infrastructure based on edge computing according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0022] In this first embodiment: a distributed campus infrastructure multi-node collaborative processing system based on edge computing is provided. The system includes a storage device and a processor. The storage device is used to store computer programs, and the processor is used to execute computer programs. The processor also integrates three core functional modules: a node deployment and time management module, an energy consumption data assessment module, and a power collaborative optimization module. The three core functional modules realize the power collaborative processing of multiple nodes of the campus infrastructure through data interaction, including establishing connections with the power grid, edge nodes, and campus infrastructure within the campus through Ethernet, 4G / 5G, or LoRa wireless communication, and supporting the docking with the campus power management platform through API interfaces to achieve data sharing and collaborative control. The node deployment and time management module is used to deploy distributed edge nodes in the power grid pathways corresponding to the park's infrastructure, match each edge node with a line interface identifier, establish a unique association between the edge node and the park's infrastructure, and realize the accurate collection of infrastructure power data and the targeted transmission of control commands. It is also used to uniformly configure the command trigger time scale for adjusting the power distribution of infrastructure by each edge node with a daily time cycle. By statistically analyzing the number of infrastructure starts within each time range of the historical cycle, the peak operation start scale is determined, and two adjacent time scales are divided into a collaborative processing period to form a time period management system adapted to the operation pattern of the infrastructure. The node deployment and time management module includes: The node deployment submodule generates an edge node deployment plan based on the type, energy consumption level, and distribution location of the park's infrastructure. It guides the physical installation and network configuration of edge nodes and establishes a database linking line interface identifiers with the infrastructure, supporting the query, modification, and deletion of identifier information. The time management submodule provides a time scale configuration interface, supports staff to customize scale intervals, automatically counts the number of infrastructure startups at each scale within a historical period, generates a peak operation start scale report, and automatically divides collaborative processing time periods to form a time period management list; The energy consumption data assessment module is used to collect the power consumption of park infrastructure reported by each line interface during each collaborative processing period, calculate the power consumption ratio of a single line interface, and assess the average and dispersion of the consumption ratio of each line interface based on the consumption ratio of each collaborative period throughout the day. It is also used to obtain the maximum and minimum consumption ratio of all line interfaces during each collaborative processing period for each power grid path, and determine the reasonable consumption ratio range of the line interface in the corresponding power grid path by combining the average and dispersion of the consumption ratio of a single line interface, covering the normal energy consumption fluctuation range of the infrastructure. The energy consumption data assessment module includes: The data acquisition submodule collects power consumption data in real time through the communication channel between the edge nodes and the infrastructure. It supports data retransmission. When the network is interrupted, the edge nodes cache the data locally and automatically upload it after the network is restored. The percentage calculation submodule automatically calculates the consumption percentage for each collaborative processing period, generates a consumption percentage trend chart, and displays it by day, week, and month. It also calculates the mean and coefficient of variation and marks line interfaces with abnormal energy consumption fluctuations. The interval quantization submodule automatically calculates the consumption ratio interval based on the maximum energy consumption of the power grid path and the statistical indicators of a single interface, and supports manual adjustment of the interval. The power coordination optimization module is used to preset the initial power allocation of each edge node to the corresponding infrastructure. When the power supply of the park infrastructure is temporarily changed due to grid path switching or load adjustment, the consumption ratio range of the corresponding line interface of the park infrastructure is called to estimate the temporary power allocation range and feed it back to the staff monitoring port in real time to realize the dynamic allocation of auxiliary power resources. The power collaborative optimization module includes: The initial allocation management submodule allows staff to preset the initial power allocation for different types of infrastructure and establish the relationship between the allocation and infrastructure type, season, and time period. The temporary estimation submodule automatically retrieves the energy consumption data of the target path and the consumption ratio range of the corresponding interface when the infrastructure switches the grid path, calculates the temporary power allocation range, and generates allocation suggestions. The feedback submodule pushes the temporary allocation range to the staff's monitoring terminal in real time. The monitoring terminal supports PC and mobile terminals and records the staff's final allocation results to form an allocation log.

[0023] Please see Figure 1 In this second embodiment: a multi-node collaborative processing method for distributed park infrastructure based on edge computing is provided, applicable to the first embodiment above. In this embodiment, a "medium-sized industrial park" is used as the application scenario. The park covers an area of ​​80 acres and includes 3 production workshops (A, B, C), 2 office buildings (D1, D2), and 1 storage area (E). The specific facility configuration is as follows: Production Workshop A: 10 large machine tools (each with a rated power of 10kW, requiring high energy consumption and high real-time performance). Production Workshop B: 8 assembly line machines (each with a rated power of 8kW and moderate energy consumption). Production Workshop C: 12 small processing equipment (each unit has a rated power of 5kW and low energy consumption); Office Building D1 / D2: A total of 50 offices, each equipped with 2 computers (0.5kW / unit) and 1 air conditioner (2kW / unit). Storage Area E: 8 refrigeration units (each with a rated power of 7kW, requiring low fluctuations and high stability); The park's power grid is divided into four independent channels: Channel 1 (Workshop A), Channel 2 (Workshops B and C), Channel 3 (Office Buildings D1 and D2), and Channel 4 (Warehouse Area E and Backup Facilities). Each channel has a rated capacity of 100kW.

[0024] The method includes the following steps: Step S1: Deploy distributed edge nodes in the power grid pathways corresponding to the park's infrastructure, match each edge node with a line interface identifier, establish a unique association between the edge node and the park's infrastructure, and realize the accurate collection of infrastructure power data and the targeted transmission of control commands; For example, distributed edge nodes are deployed in the park infrastructure to regulate the power distribution of the facilities; based on the power grid path of the park infrastructure, line interface identifiers are attached to the edge nodes, wherein one line interface identifier corresponds to a connection to one park infrastructure. Let the j-th line interface be denoted as Let the k-th power grid path be denoted as Furthermore, the power grid path includes line interfaces adapted to the park's infrastructure services to form the power grid path. Line interface set ,in, Indicates the power grid path The total number of line interfaces included; For example, the node deployment submodule of the node deployment and time management module generates a plan based on the facility energy consumption level: one edge node is deployed for each machine tool in production workshop A (10 in total), one edge node is deployed for every two machines in workshops B / C (10 in total), one edge node is deployed for every five offices in the office building (10 in total), and one edge node is deployed for each refrigeration unit in the storage area (8 in total), for a total of 38 edge nodes deployed; Each edge node is assigned a line interface identifier, such as “Path 1 - Workshop A - Machine Tool 1 - Interface 001”, “Path 3 - Office Building D1 - 3rd Floor - Interface 015”, and “Path 4 - Warehouse Area E - Refrigeration Unit 5 - Interface 030”. The identifier information is stored in the associated database. Two-way communication between the edge node and the facility is established through the LoRaWAN protocol, with the data collection frequency set to 1 minute / time and the command issuance delay set to ≤3 seconds.

[0025] Step S2: Using a daily time cycle, uniformly configure the command trigger time scale for adjusting the power distribution of infrastructure at each edge node. By statistically analyzing the number of infrastructure starts within each time range of the historical cycle, determine the peak operation start scale, divide two adjacent time scales into a collaborative processing period, and form a time period management system that adapts to the operation pattern of infrastructure. For example, when uniformly configuring the power distribution of each edge node adjustment facility, the command trigger time scale is set with a daily time cycle. The number of park infrastructure starts within each time range of a previous day is counted, and the time range with the highest percentage of starts is taken as the starting scale of the park infrastructure peak operation, thus forming a one-dimensional command time scale line with a daily time cycle. Based on a one-dimensional instruction time scale, a collaborative processing period is formed by two adjacent scales; For example, the time management submodule of the node deployment and time management module is configured with a time scale interval of 1 hour, covering the entire 24 hours of the day, and counts the number of facilities started at each scale over the past 30 days: Workshops A / B / C: The number of units started between 8:00 and 9:00 is the highest (an average of 25 units started per day, accounting for 28% of the total number of units started per day), which is determined as the starting point for peak production facilities; Office buildings D1 / D2: The number of devices started between 9:00 and 10:00 is the highest (an average of 45 offices started per day, accounting for 25%), which is determined as the starting point of the peak period for office facilities; Warehouse Area E: The number of units started is stable (8 units running all day), with no obvious peaks; The system automatically divides the day into 23 collaborative processing time periods (such as 0:00-1:00, 1:00-2:00, ..., 23:00-24:00) and displays a list of time periods and peak periods on the system interface.

[0026] Step S3: During each collaborative processing period, collect the power consumption of the park infrastructure fed back by each line interface, calculate the power consumption ratio of a single line interface, and evaluate the mean and dispersion of the consumption ratio of each line interface based on the consumption ratio of each collaborative period throughout the day. For example, obtaining the collaborative processing time periods of the day. The k-th power grid path The j-th line interface The feedback power consumption is obtained from the line interface. The consumption percentage is denoted as The consumption ratio is the ratio of the total power consumption during the same collaborative processing period to the cumulative sum of power consumption reported by each line interface in the power grid pathway, where e is the scale number. This represents the e-th scale mark. This represents the (e+1)th tick mark; Evaluate line interfaces based on consumption percentage. Average percentage of daily consumption with standard deviation ,and , ; For example, the data acquisition submodule of the energy consumption data assessment module collects electricity consumption data during each collaborative processing period: During the collaborative period from 8:00 to 9:00 (peak production time): the power consumption of each interface of channel 1 is 8-10kW, channel 2 is 5-8kW, channel 3 is 2-4kW, and channel 4 is 6-7kW. During the collaborative period from 19:00 to 20:00 (off-peak): the power consumption of each interface of channel 1 is 2-3kW, channel 2 is 1-2kW, channel 3 is 1-2kW, and channel 4 is 6-7kW. The percentage calculation submodule calculates the consumption percentage: For example, if the consumption of "Interface 001" in Channel 1 is 9kW from 8:00 to 9:00, and the total consumption of Channel 1 is 85kW, the consumption percentage = 9 / 85 × 100% ≈ 10.59%; Based on 30 days of data, the following statistical indicators were calculated: the average consumption rate of "Interface 001" was 10.3%, with a coefficient of variation of 0.11; the average consumption rate of "Interface 015" in the office building was 8.2%, with a coefficient of variation of 0.18; and the average consumption rate of "Interface 030" in the warehouse area was 12.5%, with a coefficient of variation of 0.07.

[0027] Step S4: For each power grid path, obtain the maximum and minimum consumption ratio of all line interfaces in each collaborative processing period. Combine the average and dispersion of the consumption ratio of a single line interface to determine the reasonable consumption ratio range of the line interface in the corresponding power grid path, covering the normal energy consumption fluctuation range of the infrastructure. For example, based on the collaborative processing time periods of the day In the kth power grid path Line interface set In the middle, obtain the maximum value of the consumption ratio of the line interface. and minimum value ; Combined with the average consumption ratio with standard deviation Quantize the j-th line interface The k-th power grid path Consumption percentage range ,in, , .

[0028] Step S5: Preset the initial power allocation of each edge node to the corresponding infrastructure. When the power supply of the park infrastructure is temporarily changed due to grid path switching or load adjustment, call the consumption ratio range of the corresponding line interface of the park infrastructure, estimate the temporary power allocation range, and feed it back to the staff monitoring port in real time to realize the dynamic allocation of auxiliary power resources. For example, obtain the distributed edge node initialization adjustment line interface. The corresponding power allocation G for the park's infrastructure; if the park's infrastructure temporarily switches to the power grid... At that time, retrieve the consumption percentage range. And estimate the range of temporary power allocation for the park's infrastructure. And the feedback is sent to the staff's portal.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed park infrastructure multi-node collaborative processing method based on edge computing, characterized in that, The method comprises the following steps: Step S1: deploying distributed edge nodes in the power grid channel corresponding to the park infrastructure, matching a line interface identifier for each edge node, establishing a dedicated association between the edge node and the park infrastructure, and realizing accurate collection of infrastructure power data and directional transmission of control instructions; Step S2: configuring the instruction trigger time scale for each edge node to adjust the infrastructure power distribution amount with a day as the time cycle period, determining the peak operation starting scale by counting the number of infrastructure starts in each scale time range in the historical period, dividing the adjacent two time scales into a cooperative processing period, and forming a time period management system that adapts to the operation law of the infrastructure; Step S3: In each cooperative processing period, collect the park infrastructure power consumption feedback of each line interface, calculate the power consumption proportion value of a single line interface, and evaluate the consumption proportion mean value and dispersion degree of each line interface based on the consumption proportion value of each cooperative period throughout the day; Step S4: For each power grid channel, obtain the maximum and minimum consumption proportion values of all line interfaces in each cooperative processing period, combine the consumption proportion mean value and dispersion degree of a single line interface, determine the reasonable consumption proportion interval of the line interface in the corresponding power grid channel, and cover the normal energy consumption fluctuation range of the infrastructure; Step S5: preset the initial power distribution amount of each edge node to the corresponding infrastructure, call the consumption proportion interval of the line interface of the park infrastructure when the park infrastructure temporarily changes the power supply due to power grid channel switching or load adjustment, estimate the temporary power distribution amount interval, and feedback to the staff monitoring port in real time to realize dynamic allocation of auxiliary power resources. 2.The edge computing based distributed park infrastructure multi-node cooperative processing method according to claim 1, characterized in that, The specific implementation process of step S1 comprises: Distributed edge nodes are arranged in the park infrastructure to adjust the power distribution amount of the facility; based on the power grid channel of the park infrastructure, a line interface identifier is added to the edge node, wherein one line interface identifier corresponds to one park infrastructure; Let the jth line interface be denoted as Let the kth grid path be denoted as and the line interfaces in the grid path contain the adapted park infrastructure services to constitute the line interface set of the grid path wherein, denotes the total number of line interfaces contained in the grid path . 3.The edge computing based distributed park infrastructure multi-node cooperative processing method according to claim 1, characterized in that, The specific implementation process of step S2 comprises: The instruction trigger time scale for each edge node to adjust the power distribution amount of the facility is uniformly configured, with a day as the time cycle period, the number of park infrastructure starts in each scale time range in the past day is counted, the scale time range with the most start number is taken as the starting scale of the peak operation of the park infrastructure, and a one-dimensional instruction time scale line with a day as the time cycle period is constructed; Based on the one-dimensional instruction time scale line, an adjacent two scales constitute a cooperative processing period. 4.The edge computing based distributed park infrastructure multi-node cooperative processing method according to claim 1, characterized in that, The specific implementation process of step S3 comprises: Acquiring a cooperative processing period in a day The kth power grid channel The jth line interface The feedback power consumption of the line interface The consumption proportion value is recorded as The consumption proportion value is the ratio of the power consumption and the cumulative sum of the feedback power consumption of each line interface in the power grid channel in the same cooperative processing period, wherein e is the serial number of the scale, The e th scale is represented as The e+1 th scale is represented as Based on the consumption share value, evaluating the line interface Mean consumption share within a day With standard deviation , and , .

5. The edge computing based distributed park infrastructure multi-node cooperative processing method according to claim 4, characterized in that, The specific implementation process of step S4 comprises: Based on a day of coordinated processing period , in the kth power grid channel Line interface set , the consumption proportion value maximum and minimum of the line interface are obtained and ; combined consumption share mean value with standard deviation quantifying the jth line interface consumption share interval in the kth grid path wherein , .​ 6.The edge computing based distributed park infrastructure multi-node cooperative processing method according to claim 5, characterized in that, The specific implementation process of step S5 comprises: Acquiring distributed edge node initialization adjustment line interface The power distribution amount G of the corresponding park infrastructure, if the park infrastructure temporarily switches to the grid channel , the consumption proportion interval is adjusted , and the temporary power distribution amount interval of the park infrastructure is estimated , and feedback to the staff port.

7. The multi-node cooperative processing system of the edge computing based distributed park infrastructure multi-node cooperative processing method according to any one of claims 1-6, characterized in that, The system comprises a storage and a processor, the storage is used for storing a computer program for implementing the edge computing based distributed park infrastructure multi-node cooperative processing method according to claims 1-6, and the processor is used for executing the computer program; the processor further integrates three core function modules, namely a node deployment and time management module, an energy consumption data evaluation module and a power cooperative optimization module, the three core function modules realize the power cooperative processing of the multi-node of the park infrastructure through data interaction, including establishing a connection with the power grid path, the edge node and the park infrastructure in the park through Ethernet, 4G / 5G or LoRa wireless communication mode, and supporting the docking with the park power management platform through an API interface to realize data sharing and cooperative control; The node deployment and time management module is used for deploying the distributed edge node in the power grid path corresponding to the park infrastructure, matching the line interface identifier for each edge node, establishing the exclusive association between the edge node and the park infrastructure, realizing the accurate collection of the infrastructure power data and the directional transmission of the control instruction, and further used for uniformly configuring the instruction trigger time scale of each edge node for adjusting the infrastructure power distribution amount in a time cycle period of day, determining the peak operation starting scale by counting the number of infrastructures started in each scale time range in the historical period, dividing the adjacent two time scales into a cooperative processing period, and forming a period management system adapting to the operation law of the infrastructure; The energy consumption data evaluation module is used for collecting the park infrastructure power consumption amount fed back by each line interface in each cooperative processing period, calculating the power consumption proportion value of a single line interface, and evaluating the consumption proportion mean value and the dispersion degree of each line interface based on the consumption proportion values of each cooperative period throughout the day; and further used for obtaining the maximum and minimum consumption proportion values of all line interfaces in each cooperative processing period for each power grid path, combining the consumption proportion mean value and the dispersion degree of a single line interface, determining the reasonable consumption proportion interval of the line interface in the corresponding power grid path, and covering the normal energy consumption fluctuation range of the infrastructure; The power cooperative optimization module is used for presetting the initial power distribution amount of each edge node to the corresponding infrastructure, calling the consumption proportion interval of the line interface corresponding to the park infrastructure when the park infrastructure temporarily changes the power supply due to the power grid path switching or load adjustment, estimating the temporary power distribution amount interval, and feeding back to the staff monitoring port in real time to realize the dynamic allocation of auxiliary power resources.

8. The many -node co-processing system of claim 7, wherein, The node deployment and time management module comprises: A node deployment sub-module generates an edge node deployment scheme according to the type, energy consumption level and distribution position of the park infrastructure, guides the physical installation and network configuration of the edge node, and establishes an association database of the line interface identifier and the infrastructure to support the query, modification and deletion of the identifier information; The time management submodule provides a time scale configuration interface, supports staff to customize scale intervals, automatically counts the infrastructure start quantity of each scale in the history period, generates a peak operation starting scale report, and automatically divides the cooperative processing period to form a time period management list.

9. The many -node co-processing system of claim 7, wherein, The energy consumption data evaluation module includes: The data acquisition submodule collects power consumption data in real time through the communication channel of the edge node and the infrastructure, supports data retransmission, locally caches data when the network is interrupted, and automatically uploads the data after the network is restored; The proportion calculation submodule automatically calculates the consumption proportion value of each cooperative processing period, generates a consumption proportion trend chart, and displays it by day, week, and month, calculates the mean and dispersion coefficient, and marks the line interface with energy consumption fluctuation anomalies; The interval quantization submodule automatically calculates the consumption proportion interval according to the energy consumption maximum and minimum of the power grid path and the statistical indicators of the individual interface, and supports manual adjustment of the interval.

10. The many -node co-processing system of claim 7, wherein, The power cooperative optimization module includes: The initial allocation amount management submodule supports staff to preset the initial power allocation amount of different types of infrastructure, and establishes the correlation between the allocation amount and the infrastructure type, season, and period; The temporary estimation submodule automatically retrieves the energy consumption data of the target path and the consumption proportion interval of the corresponding interface when the infrastructure switches the power grid path, calculates the temporary power allocation amount interval, and generates an allocation suggestion; The feedback submodule pushes the temporary allocation amount interval to the monitoring terminal of the staff in real time, the monitoring terminal supports PC and mobile terminals, and records the final allocation result of the staff to form an allocation log.

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