Big data-based intelligent building energy-saving and carbon-reducing control method and system

Abstract

The application relates to the technical field of energy-saving and carbon-reducing control, and discloses an intelligent building energy-saving and carbon-reducing control method and system based on big data, which comprises the following steps: obtaining an initial energy consumption data set; preliminarily dividing edge node tasks according to the position distribution of the edge nodes and the data volume of each cold storage, and obtaining a processing task range; analyzing the task load of each node, performing task migration on the nodes with task load exceeding a preset load balancing threshold, and obtaining a task allocation scheme; screening out an edge node with abundant resources as a target node according to the task allocation scheme, obtaining a real-time energy consumption data subset from the target node, and screening out an energy consumption data subset that needs to be accelerated; performing parallel analysis on the energy consumption data subset that needs to be accelerated, and obtaining an energy consumption change trend of the cold storage equipment; and generating a power adjustment instruction for the cold storage equipment with abnormal energy consumption and delivering the power adjustment instruction to each cold storage equipment. The method can realize fast and accurate analysis of the energy consumption data of the cold storage equipment and improve the data processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy conservation and carbon reduction control technology, and in particular to a smart building energy conservation and carbon reduction control method and system based on big data. Background Technology

[0002] Against the backdrop of continuous advancements in modern urbanization, the building sector, as a key area of ​​energy consumption and carbon emissions, has made energy conservation and carbon reduction one of the core measures for addressing global climate change and alleviating resource shortages. With the increasing prevalence and application of big data and other intelligent technologies, intelligent management of building energy consumption has gradually become a hot research topic in the industry, especially demonstrating enormous potential and value in achieving precise energy control and high-efficiency energy saving through technological innovation.

[0003] In existing technologies, building energy management relies on SCADA (Supervisory Control and Data Acquisition System) for centralized data processing and decision-making. A central server receives energy consumption data collected by terminal devices and generates corresponding decision commands based on preset rules, then issues these commands to control equipment operation. Because this traditional centralized architecture requires transmitting massive amounts of energy consumption data to a central server for processing, it struggles to achieve rapid and accurate energy consumption analysis when dealing with large-scale, multi-regional energy consumption data. This is due to long data transmission links and high pressure on centralized processing nodes, resulting in insufficient data support for energy conservation and carbon reduction control, thus impacting energy-saving control efficiency.

[0004] In summary, existing technologies suffer from low efficiency in energy consumption data processing in the field of building energy conservation and carbon reduction control. Summary of the Invention

[0005] This invention provides a smart building energy-saving and carbon-reduction control method and system based on big data, in order to improve the processing efficiency of energy consumption data of cold storage equipment, realize intelligent management of cold storage equipment, and reduce energy consumption.

[0006] In a first aspect, to address the aforementioned technical problems, this invention provides a smart building energy-saving and carbon-reduction control method based on big data, comprising: By distributing multiple edge nodes within the building area, the equipment operation status data, energy consumption data, and environmental temperature and humidity parameters of the cold storage area are obtained from each edge node to form an initial energy consumption dataset. Based on the initial energy consumption dataset, the data volume of each cold storage area is determined. According to the location distribution of the edge nodes and the data volume of each cold storage area, the processing tasks of the edge nodes are initially divided to obtain the processing task range of each edge node. The processing task range of each edge node is analyzed to obtain the task load of each edge node. For edge nodes whose task load exceeds the preset load balancing threshold, a task migration operation is performed to obtain an adjusted task allocation scheme. According to the adjusted task allocation scheme, a set of urgent tasks to be processed with high urgency level is determined, and edge nodes with sufficient resources are selected as target nodes. Real-time energy consumption data subsets are obtained from the target nodes, and energy consumption data subsets that need to be accelerated are selected. By using the parallel processing mechanism within the edge nodes, the energy consumption data subset that needs to be processed quickly is analyzed to obtain the energy consumption change trend of the cold storage equipment. Based on the energy consumption change trend, the corresponding cold storage equipment operating parameters are obtained. For cold storage equipment with abnormal energy consumption, a power adjustment command is generated according to the preset power adjustment rules and sent to the equipment control unit of each cold storage area.

[0007] In one optional implementation, the preliminary division of the processing tasks for each edge node based on the location distribution of the edge nodes and the data volume of each cold storage area, to obtain the processing task range for each edge node, includes: Based on the location distribution of the edge nodes and the amount of data in each cold storage area, the processing tasks of the edge nodes are initially divided and resources are allocated using pre-established allocation rules, resulting in a preliminary task scope division. Based on the preliminary task scope division, the current capability data of the edge nodes is obtained, and some tasks in the nodes whose current capability data is lower than the preset capability threshold are transferred to the nodes with higher capability data to obtain a preliminary task allocation scheme. The tasks are sorted according to the preset task priority of the cold storage area to obtain the task execution order; Monitor the task execution process according to the task execution order, obtain data volume fluctuation characteristics, dynamically adjust the preliminary task allocation scheme according to the data volume fluctuation characteristics, and determine the processing task range of each edge node.

[0008] In one optional implementation, the step of performing task migration operations on edge nodes whose task load exceeds a preset load balancing threshold to obtain an adjusted task allocation scheme includes: Determine whether the task load of each edge node exceeds the preset load balancing threshold. If so, add the corresponding node to the high load node list; otherwise, add the corresponding node to the low load node list. Obtain the communication latency data of each edge node, and select nodes whose communication latency data is lower than a preset latency threshold from the list of low-load nodes as the transfer nodes; In the list of high-load nodes, the transfer priority is determined based on the level of the communication latency data, and a list of tasks to be transferred is generated based on the transfer priority. The task list to be transferred is split according to the preset granularity control rules to obtain the split task units. The split task units are then distributed to the receiving node through a dynamic migration mechanism to obtain the adjusted task allocation scheme.

[0009] In one optional implementation, the step of determining a set of urgent tasks with high urgency indicators based on the adjusted task allocation scheme, and selecting resource-rich edge nodes as target nodes, includes: Based on the adjusted task plan, and combined with the preset data processing priorities and high urgency indicators of each node, a list of nodes involving cold storage energy consumption tasks is selected, the list of nodes is monitored, and the set of urgent tasks to be processed is formed. Obtain the availability of additional computing resources for each edge node; Edge nodes with an additional computing resource availability rate greater than a preset resource availability threshold are selected as target nodes to undertake and process tasks in the set of urgent tasks to be processed.

[0010] In one optional implementation, the step of obtaining a real-time subset of energy consumption data from the target node and filtering out the subset of energy consumption data that needs to be accelerated includes: Based on the set of urgent tasks to be processed, determine the corresponding types and quantities of computing resources required, and obtain a resource allocation list for accelerating processing; Based on the resource allocation list, additional resources are distributed to the target nodes as needed; Based on the pre-acquired characteristics of cold storage energy consumption tasks, the execution order of the tasks is adjusted to obtain an adjusted task schedule. According to the task scheduling table, a subset of real-time energy consumption data is obtained from the target node, and the subset that needs to be processed first is selected to obtain the subset of energy consumption data that needs to be accelerated.

[0011] In one optional implementation, the step of rapidly analyzing the subset of energy consumption data requiring accelerated processing through a parallel processing mechanism within edge nodes to obtain the energy consumption change trend of the cold storage equipment includes: The energy consumption data subset that needs to be accelerated is divided into multiple data sub-units, and an independent processing thread is assigned to each data sub-unit to obtain an independent data unit. Based on a preset monitoring frequency, the thread processing progress of the independent data unit is monitored. After comparing the thread processing progress with a preset processing progress threshold, the computing resource allocation priority of the independent processing thread is dynamically adjusted to obtain a thread resource configuration table. Based on the thread resource configuration table, the processing results of the independent data units are integrated to obtain energy consumption data analysis results including energy consumption change trends.

[0012] In one optional implementation, the step of obtaining the corresponding cold storage equipment operating parameters based on the energy consumption change trend, generating a power adjustment command according to a preset power adjustment rule for cold storage equipment with abnormal energy consumption, and issuing it to the equipment control unit of each cold storage area includes: Based on the energy consumption change trend, obtain the corresponding operating parameters of the cold storage equipment; The energy consumption trend of the cold storage equipment is visualized and abnormal data points are marked to obtain the abnormal energy consumption range. According to the preset tracking frequency, the abnormal energy consumption range is continuously monitored to obtain a list of abnormal energy consumption devices; Based on the preset power adjustment rules and the operating parameters of the cold storage equipment, a preliminary power adjustment range is generated, and a draft adjustment instruction is determined for each cold storage device in the list of energy-abnormal equipment. When the adjustment range in the draft adjustment instruction exceeds the preset instruction adjustment threshold, the draft adjustment instruction is verified through the preset instruction issuance protocol to obtain a control instruction set that meets the requirements. Based on the control instruction set and preset device priorities, instructions are distributed sequentially to each cold storage device in the list of devices with abnormal energy consumption.

[0013] Secondly, the present invention provides an intelligent building energy-saving and carbon-reduction control system based on big data, comprising: The initial data acquisition module is used to obtain equipment operation status data, energy consumption data and environmental temperature and humidity parameters of the cold storage area from multiple edge nodes distributed within the building area, forming an initial energy consumption dataset. The task preliminary division module is used to determine the amount of data in each of the cold storage areas based on the initial energy consumption dataset, and to perform preliminary division of the processing tasks of the edge nodes according to the location distribution of the edge nodes and the amount of data in each of the cold storage areas, so as to obtain the processing task range of each edge node. The load balancing adjustment module is used to analyze the processing task range of each edge node to obtain the task load of each edge node, and to perform task migration operation for edge nodes whose task load exceeds the preset load balancing threshold to obtain an adjusted task allocation scheme. The emergency task processing module is used to determine the set of emergency tasks with high urgency level according to the adjusted task allocation scheme, filter out the edge nodes with sufficient resources as target nodes, obtain a subset of real-time energy consumption data from the target nodes, and filter out the subset of energy consumption data that needs to be accelerated. The energy consumption trend analysis module is used to quickly analyze the subset of energy consumption data that needs to be accelerated through the parallel processing mechanism within the edge node, and obtain the energy consumption change trend of the cold storage equipment. The equipment control and execution module is used to obtain the corresponding cold storage equipment operating parameters according to the energy consumption change trend. For cold storage equipment with abnormal energy consumption, it generates power adjustment instructions according to preset power adjustment rules and sends them to the equipment control units of each cold storage area.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention collects equipment operating status, energy consumption data, and environmental temperature and humidity parameters of the cold storage area by distributing multiple edge nodes within the building area, forming an initial energy consumption dataset. This method of distributing data processing tasks to various edge nodes avoids the transmission delay and data processing pressure caused by centralized collection, and improves the real-time performance and accuracy of data collection. At the same time, this invention preliminarily divides the processing tasks of the edge nodes according to the location distribution of the edge nodes and the amount of data in each cold storage area, and further optimizes the task allocation through task migration operations to ensure a balanced task load for each edge node. This distributed processing method improves the processing efficiency of energy consumption data, can quickly respond to changes in energy consumption, and provides efficient data support for energy conservation and carbon reduction control.

[0015] (2) Based on the adjusted task allocation scheme, this invention determines the set of tasks with high urgency, selects resource-rich edge nodes as target nodes, obtains a subset of real-time energy consumption data, and selects a subset of energy consumption data that needs to be accelerated. This series of operations enables priority processing of urgent tasks, ensures that high-priority tasks can be responded to in a timely manner, avoids the failure to detect and handle energy consumption anomalies in a timely manner, and improves the system's response speed and the timeliness of energy-saving control.

[0016] (3) This invention uses a parallel processing mechanism within edge nodes to quickly analyze a subset of energy consumption data that needs to be processed faster, enabling timely acquisition of energy consumption trends in cold storage equipment. This rapid analysis capability allows the system to quickly and accurately identify equipment with abnormal energy consumption, ensuring the pertinence and effectiveness of energy-saving measures, effectively reducing energy waste, improving energy utilization efficiency, and enabling the system to maintain high-efficiency operation even when faced with large-scale energy consumption data.

[0017] (4) Based on the energy consumption change trend, the present invention obtains the operating parameters of the cold storage equipment. For equipment with abnormal energy consumption, it generates power adjustment instructions according to preset power adjustment rules and sends them to the equipment control units of each cold storage area. Through this automated control method, the system can react quickly when abnormal energy consumption occurs, avoiding the lag and uncertainty of manual intervention, reducing the duration and scope of abnormal energy consumption, and ensuring the timeliness and accuracy of energy-saving and carbon-reduction measures. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an embodiment of the smart building energy-saving and carbon-reduction control method based on big data provided by the present invention. Figure 2 This is a schematic diagram of an embodiment of the intelligent building energy-saving and carbon-reduction control system based on big data provided by the present invention. Detailed Implementation

[0019] 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.

[0020] Reference Figure 1 The first embodiment of the present invention provides a smart building energy-saving and carbon-reduction control method based on big data, including steps S11 to S17, as follows: S11: By distributing multiple edge nodes within the building area, the system obtains equipment operation status data, energy consumption data, and environmental temperature and humidity parameters of the cold storage area from each edge node to form an initial energy consumption dataset. S12, Based on the initial energy consumption dataset, determine the data volume of each cold storage area, and according to the location distribution of the edge nodes and the data volume of each cold storage area, preliminarily divide the processing tasks of the edge nodes to obtain the processing task range of each edge node. S13, Analyze the processing task range of each edge node to obtain the task load of each edge node, and perform task migration operation for edge nodes whose task load exceeds the preset load balancing threshold to obtain the adjusted task allocation scheme. S14. Based on the adjusted task allocation scheme, determine the set of urgent tasks to be processed with high urgency level, and select resource-rich edge nodes as target nodes. S15, obtain a subset of real-time energy consumption data from the target node, and filter out the subset of energy consumption data that needs to be accelerated; S16, through the parallel processing mechanism within the edge node, the energy consumption data subset that needs to be accelerated is quickly analyzed to obtain the energy consumption change trend of the cold storage equipment; S17. Obtain the corresponding cold storage equipment operating parameters according to the energy consumption change trend. For cold storage equipment with abnormal energy consumption, generate a power adjustment command according to the preset power adjustment rules and send it to the equipment control unit of each cold storage area.

[0021] In step S11, multiple edge nodes are distributed within the building area, and equipment operation status data, energy consumption data, and environmental temperature and humidity parameters of the cold storage area are obtained from each edge node to form an initial energy consumption dataset.

[0022] It should be noted that edge nodes refer to computing devices or data acquisition terminals deployed close to the data source (such as cold storage equipment). Through reasonable deployment within the building area, these edge nodes can perform real-time data acquisition for small cold storage equipment in remote areas. The acquired raw data includes the equipment's operating status (such as on / off status, refrigeration power, etc.) and environmental temperature and humidity parameters. Using a pre-established data transmission protocol, the time point of the raw data acquisition is recorded, a unique device identifier is added to each data point, and the data is stored and classified in time periods to form a structured initial energy consumption dataset, facilitating efficient subsequent data analysis.

[0023] Taking a cold storage network located on a remote farm as an example, this network consists of 10 devices distributed across 5 edge nodes, collecting real-time data on the cooling power and ambient temperature and humidity parameters of each device, such as a temperature of 5 degrees Celsius and humidity of 60%. The day is divided into 24-hour segments, with energy consumption data stored hourly. The hourly power consumption of a single device ranges from 4 to 5 kilowatt-hours, and this data is further categorized into peak and off-peak periods. This data processing method facilitates subsequent analysis, ensures data traceability, reduces data redundancy, and improves processing efficiency.

[0024] In step S12, the data volume of each cold storage area is determined based on the initial energy consumption dataset. According to the location distribution of the edge nodes and the data volume of each cold storage area, the processing tasks of the edge nodes are initially divided to obtain the processing task range of each edge node, including: Based on the location distribution of the edge nodes and the amount of data in each cold storage area, the processing tasks of the edge nodes are initially divided and resources are allocated using pre-established allocation rules, resulting in a preliminary task scope division. Based on the preliminary task scope division, the current capability data of the edge nodes is obtained, and some tasks in the nodes whose current capability data is lower than the preset capability threshold are transferred to the nodes with higher capability data to obtain a preliminary task allocation scheme. The tasks are sorted according to the preset task priority of the cold storage area to obtain the task execution order; Monitor the task execution process according to the task execution order, obtain data volume fluctuation characteristics, dynamically adjust the preliminary task allocation scheme according to the data volume fluctuation characteristics, and determine the processing task range of each edge node.

[0025] It should be noted that the allocation rule is a logic formulated by combining the geographical proximity of edge nodes and cold storage areas, the data volume of each cold storage area, and the basic processing capabilities of each edge node. Its core purpose is to ensure that task allocation is both physically location-based, reducing data transmission distance, and data-scale-matched, preventing small data volume areas from consuming too many node resources. Specifically, taking the management of cold storage in a remote farm as an example, if the five cold storages on the east side generate 5,000 data entries per hour, and the three cold storages on the west side generate 1,000 data entries per hour, and there are three edge nodes on the east side and two on the west side, the allocation rule can be set as follows: the east-side nodes undertake 60% of the task, the west-side nodes undertake 20% of the task, and the remaining task is shared by the other nodes. The initial task scope division obtained through this rule provides a basic framework for subsequent processing, avoiding blind task allocation.

[0026] In this embodiment, the current capability data refers to the actual processing capacity of the edge node under the current load, such as the number of records it can process per hour and memory usage. The preset capability threshold is the minimum capability standard to ensure efficient task processing by the node, such as 1500 records per hour. Its purpose is to determine whether a node is overloaded. When a node's current capability data is lower than this threshold, it indicates that it cannot efficiently complete the assigned tasks. In this case, some tasks are transferred to nodes with higher capability data, i.e., nodes whose processing capacity exceeds the preset capability threshold and has surplus capacity, thus avoiding task backlog. For example, if the capability threshold is set to 1800 records / hour, and the initial task allocation for a node on the east side requires processing 1800 records per hour, but its current capability data is only 1200 records / hour, lower than the preset capability threshold; while a node on the west side currently only handles 500 records per hour, its current capability data is 2000 records / hour, higher than the preset capability threshold, then 600 records per hour from the east node can be transferred to the west node. This adjustment-based initial task allocation scheme optimizes the load based on the initial allocation, improving overall processing efficiency.

[0027] It should be noted that the preset task priority is set according to the importance of the cold storage area (such as the perishability of the stored items and the requirements for temperature stability). For example, a cold storage for storing low-temperature vaccines has a higher priority than a cold storage for storing ordinary frozen foods because it requires real-time temperature monitoring. This operation ensures that critical tasks are processed first, avoiding delays in critical data processing. By recording the processing time and data input changes of each node, the task execution process is monitored to obtain data volume fluctuation characteristics, i.e., sudden increases or decreases in data volume during task execution, so that task allocation adapts to actual data changes. For example, if the eastern node experiences a 30% surge in data volume due to equipment debugging, causing the processing time to increase from 1 hour to 2 hours, some tasks can be temporarily transferred to nodes with lower load based on this fluctuation characteristic, or the memory usage limit for the eastern node can be increased to provide temporary resources. Through this dynamic adjustment, the final task processing range of each edge node not only conforms to the initial allocation logic but also adapts to variables in actual execution, ensuring that task processing remains efficient and stable.

[0028] In step S13, the processing task range of each edge node is analyzed to obtain the task load of each edge node. For edge nodes whose task load exceeds a preset load balancing threshold, a task migration operation is performed to obtain an adjusted task allocation scheme, including: Determine whether the task load of each edge node exceeds the preset load balancing threshold. If so, add the corresponding node to the high load node list; otherwise, add the corresponding node to the low load node list. Obtain the communication latency data of each edge node, and select nodes whose communication latency data is lower than a preset latency threshold from the list of low-load nodes as the transfer nodes; In the list of high-load nodes, the transfer priority is determined based on the level of the communication latency data, and a list of tasks to be transferred is generated based on the transfer priority. The task list to be transferred is split according to the preset granularity control rules to obtain the split task units. The split task units are then distributed to the receiving node through a dynamic migration mechanism to obtain the adjusted task allocation scheme.

[0029] It's important to note that by evaluating the total number of tasks currently handled by each edge node, task complexity, and other processing capacity-related metrics, the actual task pressure, or task load, is determined for each edge node. Task load directly reflects the node's operational load status and is the fundamental data for determining whether a node needs adjustment. After comprehensively considering factors such as the edge node's hardware performance and historical processing efficiency, a load balancing threshold is set. This threshold represents the maximum task load that an edge node can withstand while maintaining efficient and stable operation. When a node's task load exceeds the preset load balancing threshold, it indicates excessive operational pressure, potentially leading to processing delays, slow responses, and other issues. Therefore, such nodes are included in the high-load node list. Nodes whose task load does not exceed the preset load balancing threshold have relatively stable operation and the ability to process tasks normally or even handle additional tasks; these are included in the low-load node list.

[0030] In this embodiment, communication latency refers to the time delay in data transmission between nodes, which directly affects the data interaction efficiency and timeliness of task processing after task migration. In the low-load node list, nodes with communication latency below a preset latency threshold are selected as receiving nodes. This preset latency threshold is a critical value ensuring efficient data transmission between nodes; nodes below this threshold have shorter data transmission times, ensuring that the processing progress of migrated tasks is not affected by excessive latency during inter-node interaction. In the high-load node list, high-load nodes with higher communication latency have lower data transmission and interaction efficiency, making them more prone to task backlog and processing delays. Therefore, high-load nodes with higher latency have higher task transfer priority. A task list to be transferred is generated based on this transfer priority; that is, a summary list of tasks from high-load nodes that exceed their capacity and need to be migrated to receiving nodes to reduce load. Sorting by transfer priority ensures that nodes most urgently needing load reduction are adjusted first.

[0031] Next, the task list to be transferred is split according to preset granularity control rules, resulting in split task units. These preset granularity control rules are formulated based on the carrying capacity of the receiving node and the smallest divisible unit of the task. The aim is to break down tasks into appropriately sized units, allowing the receiving node to more flexibly accept and process tasks, reducing the risk of overloading the receiving node due to concentrated task transfers. Then, a dynamic migration mechanism distributes the split task units to the receiving nodes, achieving orderly transmission and allocation of task units between nodes, ensuring that task units accurately reach their target receiving nodes, thus obtaining an adjusted task allocation scheme. The dynamic migration mechanism dynamically adjusts the task allocation strategy by monitoring the load, communication latency, and other statuses of the receiving nodes in real time. It also checks and feeds back the processing status after task transmission is completed to adapt to the actual carrying capacity of the nodes.

[0032] For example, a cold storage system has four edge nodes, with a load balancing threshold set at 4000 records per hour. Monitoring shows that node 1 processes 5000 records per hour and is listed as high-load; nodes 2, 3, and 4 each process 3000 records per hour and are listed as low-load. The wireless network has a preset latency threshold of 200 milliseconds. The wireless communication latency data for nodes 1, 2, 3, and 4 are 250 milliseconds, 160 milliseconds, 180 milliseconds, and 200 milliseconds, respectively. Nodes 2 and 3 are selected as the transfer nodes. Node 1 has a higher latency, so it is prioritized to generate a transfer task consisting of 1200 records. This task is then split into units of 600 records each and distributed to nodes 2 and 3 through a dynamic migration mechanism. Ultimately, the load on each node is controlled to remain within the load balancing threshold. In step S14, based on the adjusted task allocation scheme, a set of urgent tasks with high urgency is determined, and resource-rich edge nodes are selected as target nodes, including: Based on the adjusted task plan, and combined with the preset data processing priorities and high urgency indicators of each node, a list of nodes involving cold storage energy consumption tasks is selected, the list of nodes is monitored, and the set of urgent tasks to be processed is formed. Obtain the availability of additional computing resources for each edge node; Edge nodes with an additional computing resource availability rate greater than a preset resource availability threshold are selected as target nodes to undertake and process tasks in the set of urgent tasks to be processed.

[0033] It should be noted that the preset data processing priorities for each node are based on rules governing the order of processing, such as node importance and historical processing efficiency. High urgency indicators are pre-added markers for cold storage energy consumption tasks that require priority processing, used to quickly identify urgent tasks. Based on the adjusted task allocation scheme, tasks with high urgency indicators are identified, such as cold storage temperature change alarms and equipment overload risks, which require millisecond-level response scenarios. Using preset node data processing priority rules, for example, the data processing priority for vaccine storage cold storage energy consumption tasks is higher than that for fresh food cold storage, and further higher than that for conventional warehousing cold storage, a list of edge nodes involved in cold storage energy consumption tasks is selected. Real-time indicators such as task queue depth, load, and processing latency of each node in the list are continuously monitored. Tasks with high urgency indicators in each node are tracked and aggregated to form a set of urgent tasks to be processed.

[0034] In this embodiment, the additional computing resource availability rate refers to the percentage of unused computing resources (such as CPU, memory, bandwidth, etc.) of an edge node relative to its total resources. It reflects the node's ability to handle new tasks and can be obtained by collecting resource data such as CPU utilization and remaining memory at a rate of seconds using resource probes. Specifically, if a node currently has a CPU idle rate of 50% and a memory idle rate of 40%, its additional computing resource availability rate can be taken as the average of these two values, which is 45%. Nodes with an additional computing resource availability rate exceeding a preset resource availability threshold are selected as target nodes. This preset resource availability threshold is the minimum resource availability standard to ensure that a node can successfully handle urgent tasks. Nodes with a resource availability rate higher than this threshold are considered to have sufficient resources, ensuring that urgent tasks receive adequate computing power.

[0035] In step S15, a subset of real-time energy consumption data is obtained from the target node, and the subset of energy consumption data that needs to be accelerated is selected, including: Based on the set of urgent tasks to be processed, determine the corresponding types and quantities of computing resources required, and obtain a resource allocation list for accelerating processing; Based on the resource allocation list, additional resources are distributed to the target nodes as needed; Based on the pre-acquired characteristics of cold storage energy consumption tasks, the execution order of the tasks is adjusted to obtain an adjusted task schedule. According to the task scheduling table, a subset of real-time energy consumption data is obtained from the target node, and the subset that needs to be processed first is selected to obtain the subset of energy consumption data that needs to be accelerated.

[0036] It should be noted that the computing resource type refers to the specific resource category required to process the task, including independent processing threads, memory space, etc., while the computing resource quantity refers to the specific quota of each type of resource, such as the number of CPU cores, memory bandwidth, etc. Based on the complexity and data volume of the urgent task to be processed, and combined with the historical task feature database, the resource requirements of the current urgent task are dynamically matched to generate a resource allocation list for accelerated processing, resulting in a detailed list of resources to be allocated for processing the urgent task. According to the resource allocation list, the idle resources of the central cloud or nearby nodes are virtualized and divided into standardized units, and distributed to the target nodes as needed. The characteristics of cold storage energy consumption tasks include temperature sensitivity, periodic fluctuations, and spatial temperature gradient distribution, which can be obtained in advance through equipment parameter databases or business rules. Based on the characteristics of cold storage energy consumption tasks, the task execution order is adjusted, placing high-time-sensitivity tasks at the front of the task queue, resulting in an adjusted task schedule.

[0037] Furthermore, based on the task scheduling table, it is necessary to obtain real-time data fragments related to cold storage energy consumption generated by the target node during task processing, thus obtaining a real-time energy consumption data subset. This real-time energy consumption data subset includes diverse data related to various tasks, such as temperature records for different cold storage areas, equipment power changes, and total energy consumption statistics. Based on the priority correspondence of tasks in the task scheduling table, data content directly associated with high-priority tasks is filtered out from the energy consumption data subset, resulting in the energy consumption data subset that needs to be processed more quickly.

[0038] For example, if the set of urgent tasks to be processed includes two tasks: "Core Cold Storage Temperature Fluctuation Data Processing" and "Ordinary Cold Storage Daily Energy Consumption Statistics," the former is more complex and involves a larger amount of data. Analysis determines that the core cold storage temperature fluctuation data processing task requires two independent processing threads and 4GB of memory, while the ordinary cold storage daily energy consumption statistics task requires one processing thread and 2GB of memory. Based on this, a resource allocation list is formed. After distributing the corresponding resources to the target nodes according to the resource allocation list, considering the characteristics of the core cold storage temperature fluctuation data processing task—that delays could lead to food spoilage—its priority is higher than that of the ordinary cold storage statistics task. Therefore, the execution order is adjusted, placing the core cold storage temperature fluctuation data processing task first to form a task scheduling table. Based on this table, a subset of real-time energy consumption data is obtained from the target nodes. Data related to the core cold storage temperature fluctuations, such as the real-time temperature change curve of the core cold storage, the energy consumption data corresponding to temperature anomaly alarms, and the power fluctuation data of related refrigeration equipment due to temperature regulation, are selected to constitute the subset of energy consumption data that needs to be processed more quickly.

[0039] In step S16, the energy consumption data subset requiring accelerated processing is rapidly analyzed using a parallel processing mechanism within the edge nodes to obtain the energy consumption change trend of the cold storage equipment, including: The energy consumption data subset that needs to be accelerated is divided into multiple data sub-units, and an independent processing thread is assigned to each data sub-unit to obtain an independent data unit. Based on a preset monitoring frequency, the thread processing progress of the independent data unit is monitored. After comparing the thread processing progress with a preset processing progress threshold, the computing resource allocation priority of the independent processing thread is dynamically adjusted to obtain a thread resource configuration table. Based on the thread resource configuration table, the processing results of the independent data units are integrated to obtain energy consumption data analysis results including energy consumption change trends.

[0040] It should be noted that the data subunit is an independent data unit block obtained by splitting the energy-consuming data subset that needs to be accelerated according to spatiotemporal dimensions or data types. To overcome the bottleneck of single-threaded processing, an independent processing thread is allocated to each data subunit, and a parallel computing channel is built within the edge node to form an independent data unit execution queue. Each thread operates independently, improving the overall data processing speed while avoiding mutual interference between data processing. Based on a preset monitoring frequency, the thread processing progress of each independent data unit is monitored in real time. When the processing progress of a certain thread is detected to be lower than the preset processing progress threshold, dynamic resource scheduling is immediately triggered, automatically increasing the CPU or memory quota of that thread, and generating a thread resource configuration table to record the optimization strategy. Here, thread processing progress refers to the proportion of processing completed by the thread for the corresponding independent data unit, the preset processing progress threshold is the minimum completion ratio standard set to ensure processing efficiency, and the thread resource configuration table is a table that records the priority of computing resource allocation for each independent processing thread. When the thread processing progress is lower than the preset processing progress threshold, it indicates that the thread's processing speed is slow, and the resource allocation priority needs to be increased to ensure that the processing progress of each thread is balanced. This real-time feedback mechanism can effectively eliminate the impact of local latency on the overall analysis.

[0041] In this embodiment, after allocating computing resources according to the thread resource configuration table, each independent data unit is efficiently processed and generates corresponding scattered results. These scattered results are then integrated and summarized to form complete energy consumption data analysis results, including peak energy consumption, average energy consumption, and abnormal energy consumption fluctuations. By analyzing the patterns of energy consumption changes from low to high and from high to low, the energy consumption trend is obtained, thus intuitively reflecting the energy consumption changes of the cold storage equipment.

[0042] For example, the energy consumption data subset requiring accelerated processing is the 24-hour energy consumption data of a core cold storage facility. This subset is divided into 24 data sub-units per hour, with each sub-unit assigned an independent processing thread. Monitoring is set at once every 5 minutes, and a preset processing progress threshold is 80% completion of unit data processing per hour. If a thread processes the 10:00-11:00 data sub-unit and its progress is only 30% after 5 minutes, below the preset processing progress threshold, then the priority of that thread's computing resource allocation is increased. Finally, the processing results of the 24 independent data units are integrated, and the energy consumption data for each time period are concatenated along a timeline to obtain the energy consumption trend of the cold storage facility over 24 hours.

[0043] In step S17, the corresponding operating parameters of the cold storage equipment are obtained according to the energy consumption change trend. For cold storage equipment with abnormal energy consumption, a power adjustment command is generated according to a preset power adjustment rule and sent to the equipment control unit of each cold storage area, including: Based on the energy consumption change trend, obtain the corresponding operating parameters of the cold storage equipment; The energy consumption trend of the cold storage equipment is visualized and abnormal data points are marked to obtain the abnormal energy consumption range. According to the preset tracking frequency, the abnormal energy consumption range is continuously monitored to obtain a list of abnormal energy consumption devices; Based on the preset power adjustment rules and the operating parameters of the cold storage equipment, a preliminary power adjustment range is generated, and a draft adjustment instruction is determined for each cold storage device in the list of energy-abnormal equipment. When the adjustment range in the draft adjustment instruction exceeds the preset instruction adjustment threshold, the draft adjustment instruction is verified through the preset instruction issuance protocol to obtain a control instruction set that meets the requirements. Based on the control instruction set and preset device priorities, instructions are distributed sequentially to each cold storage device in the list of devices with abnormal energy consumption.

[0044] It should be noted that the system locates the corresponding cold storage equipment based on energy consumption change trends, and then obtains the corresponding equipment operating parameters, including the runtime of the refrigeration compressor, power load, and temperature setpoint. The energy consumption change data is presented as a line graph through a dynamic visualization interface. First, the data access module receives and aligns the energy consumption data, then the rendering component generates a line graph with time as the horizontal axis and energy consumption as the vertical axis, while adding interactive functionality and dynamically updating it. Abnormal data points during periods of sudden power increases or decreases are highlighted to form precise energy consumption anomaly ranges. Considering the core nature and energy consumption stability of the cold storage equipment, different tracking frequencies are designed for core critical equipment and general auxiliary equipment, and a rule base is formed through system backend configuration for future use. Based on the equipment type, a preset tracking frequency is selected to continuously scan and monitor the equipment operating status within the energy consumption anomaly range, filtering out equipment with energy consumption exceeding the normal range to form a list of equipment with energy consumption anomalies.

[0045] In this embodiment, for each device in the list of devices with abnormal energy consumption, a preset power adjustment rule is invoked. Combined with the device's real-time operating power, operating temperature, and other operating parameters, a preliminary power adjustment range suitable for that device is calculated, forming a draft adjustment instruction at the device level. The preset power adjustment rule is a power adjustment specification based on device performance (such as rated power, maximum load capacity, and service life) and energy consumption standards, including adjustment ratios corresponding to different levels of energy consumption abnormality. The preset instruction adjustment threshold is the maximum power adjustment range set to ensure the safe and stable operation of the device. When the adjustment range of the draft instruction exceeds the preset instruction adjustment threshold, it may damage the device. Therefore, the draft instruction needs to be verified through a preset instruction issuance protocol to obtain a compliant control instruction set.

[0046] It's worth noting that the instruction issuance protocol is a set of rules that standardizes the entire process of instruction generation and issuance. It covers instruction format standards (such as the requirement for instructions to include fixed fields like device identifier, adjustment range, and execution time), transmission specifications (such as data encryption methods and transmission link selection), and verification logic (such as determining whether the adjustment range meets the equipment's safe operation requirements). Specifically, when a draft adjustment instruction needs to be verified, the default instruction issuance protocol first checks the draft for completeness according to the format standards. If format issues are found, it is returned for correction. Then, based on the verification logic in the protocol, the adjustment range in the draft is compared with the default instruction adjustment threshold. For drafts that exceed the standards or have incorrect formats, the protocol triggers a correction mechanism to modify the adjustment range and supplement missing field information. After format verification and adjustment range correction, all instructions that meet the protocol requirements are integrated to form a control instruction set containing information such as device identifier, compliant adjustment range, and execution order, ensuring that each instruction can be accurately identified and executed by the equipment control unit.

[0047] In this embodiment, control commands are issued to the cold storage equipment in the energy consumption anomaly list in sequence according to preset equipment priorities. The preset equipment priorities are determined by comprehensively considering factors such as the functional positioning of the cold storage, the purpose of the equipment, and the potential impact of equipment anomalies. For example, critical equipment such as the main refrigeration compressor, which maintains the overall temperature of the cold storage, has a higher priority than auxiliary equipment such as ventilation and lighting.

[0048] For example, a cold storage facility obtains the operating parameters of five refrigeration units by analyzing energy consumption trends. After presenting these trends as a line graph, the period from 2 AM to 3 AM is marked as the abnormal energy consumption interval. Monitoring this interval at a preset tracking frequency of once every 30 minutes reveals that units A and B have abnormal energy consumption, both significantly higher than the historical average for the same period, and are added to the list of abnormal energy consumption units. Based on power adjustment rules and the operating parameters of these two units, draft adjustment instructions are generated, reducing power by 8% and 15% respectively. To ensure that the temperature fluctuation after power adjustment does not exceed 1°C, the preset power adjustment threshold is set to 10%. Therefore, the draft instruction to reduce power by 15% needs to be verified and corrected to a 10% reduction, forming the control instruction set. Because unit A is the core cold storage unit for storing temperature-sensitive items such as pharmaceuticals, it has a higher priority than unit B in a regular cold storage facility, so instructions are distributed to unit A first.

[0049] It should be noted that after the specific control command set is sent to the equipment control units in each cold storage area, the equipment control units will receive and execute the commands according to the control unit interface and command sending protocol, and simultaneously generate and send back the executed feedback data. The control unit interface is the software interface for data interaction between the cold storage equipment control unit and the command sending system. Before receiving the feedback data, it checks whether the data conforms to the preset format requirements. If it does, it extracts status parameters such as the actual power and operational stability of the equipment; if it does not conform, it is marked as a transmission anomaly and a retransmission is requested. Then, the extracted status parameters are compared with the expected values. If the difference is within the preset deviation threshold, the equipment is considered to be operating normally; if the difference exceeds the preset deviation threshold, a secondary calibration is triggered according to the deviation calibration rules, generating a new adjustment command and sending it until the equipment operating status meets expectations, completing the calibration.

[0050] In summary, this invention discloses a smart building energy-saving and carbon-reduction control method based on big data, comprising: distributing multiple edge nodes within a building area, acquiring equipment operation status data, energy consumption data, and environmental temperature and humidity parameters of a cold storage area from each edge node to form an initial energy consumption dataset; determining the data volume of each cold storage area based on the initial energy consumption dataset; preliminarily dividing the processing tasks of the edge nodes according to the location distribution of the edge nodes and the data volume of each cold storage area to obtain the processing task range of each edge node; analyzing the processing task range of each edge node to obtain the task load of each edge node; and targeting edge nodes whose task load exceeds a preset load balancing threshold. Edge nodes perform task migration to obtain an adjusted task allocation scheme. Based on the adjusted scheme, a set of urgent tasks with high urgency is identified, and resource-rich edge nodes are selected as target nodes. Real-time energy consumption data subsets are obtained from these target nodes, and subsets requiring accelerated processing are identified. Through parallel processing mechanisms within the edge nodes, the energy consumption data subsets requiring accelerated processing are quickly analyzed to obtain the energy consumption trend of the cold storage equipment. Based on this trend, corresponding operating parameters for the cold storage equipment are obtained. For cold storage equipment with abnormal energy consumption, power adjustment commands are generated according to preset power adjustment rules and sent to the equipment control units in each cold storage area. This invention achieves rapid and accurate analysis of cold storage equipment energy consumption data through distributed data processing at edge nodes, generating targeted power adjustment commands and issuing them for control, thereby improving data processing efficiency and effectively reducing energy consumption.

[0051] Reference Figure 2 The second embodiment of the present invention provides a smart building energy-saving and carbon-reduction control system based on big data, comprising: The initial data acquisition module is used to obtain equipment operation status data, energy consumption data and environmental temperature and humidity parameters of the cold storage area from multiple edge nodes distributed within the building area, forming an initial energy consumption dataset. The task preliminary division module is used to determine the amount of data in each of the cold storage areas based on the initial energy consumption dataset, and to perform preliminary division of the processing tasks of the edge nodes according to the location distribution of the edge nodes and the amount of data in each of the cold storage areas, so as to obtain the processing task range of each edge node. The load balancing adjustment module is used to analyze the processing task range of each edge node to obtain the task load of each edge node, and to perform task migration operation for edge nodes whose task load exceeds the preset load balancing threshold to obtain an adjusted task allocation scheme. The emergency task processing module is used to determine the set of emergency tasks with high urgency level according to the adjusted task allocation scheme, filter out the edge nodes with sufficient resources as target nodes, obtain a subset of real-time energy consumption data from the target nodes, and filter out the subset of energy consumption data that needs to be accelerated. The energy consumption trend analysis module is used to quickly analyze the subset of energy consumption data that needs to be accelerated through the parallel processing mechanism within the edge node, and obtain the energy consumption change trend of the cold storage equipment. The equipment control and execution module is used to obtain the corresponding cold storage equipment operating parameters according to the energy consumption change trend. For cold storage equipment with abnormal energy consumption, it generates power adjustment instructions according to preset power adjustment rules and sends them to the equipment control units of each cold storage area.

[0052] It should be noted that the big data-based intelligent building energy-saving and carbon-reduction control system provided in this embodiment of the invention is used to execute all the process steps of the big data-based intelligent building energy-saving and carbon-reduction control method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0053] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program, such as a parallel processing program, stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described in the various embodiments of the big data-based intelligent building energy-saving and carbon-reduction control method. Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the initial data acquisition module.

[0054] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0055] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0056] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0057] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0058] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0059] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0060] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A smart building energy-saving and carbon-reduction control method based on big data, characterized in that, include: By distributing multiple edge nodes within the building area, equipment operation status data, energy consumption data, and environmental temperature and humidity parameters of the cold storage area are obtained from each edge node to form an initial energy consumption dataset. Based on the initial energy consumption dataset, the data volume of each cold storage area is determined. According to the location distribution of the edge nodes and the data volume of each cold storage area, the processing tasks of the edge nodes are initially divided to obtain the processing task range of each edge node. The processing task range of each edge node is analyzed to obtain the task load of each edge node. For edge nodes whose task load exceeds the preset load balancing threshold, a task migration operation is performed to obtain an adjusted task allocation scheme. According to the adjusted task allocation scheme, a set of urgent tasks to be processed with high urgency level is determined, and edge nodes with sufficient resources are selected as target nodes. Real-time energy consumption data subsets are obtained from the target nodes, and energy consumption data subsets that need to be accelerated are selected. By using the parallel processing mechanism within the edge nodes, the energy consumption data subset that needs to be processed quickly is analyzed to obtain the energy consumption change trend of the cold storage equipment. Based on the energy consumption change trend, the corresponding cold storage equipment operating parameters are obtained. For cold storage equipment with abnormal energy consumption, a power adjustment command is generated according to the preset power adjustment rules and sent to the equipment control unit of each cold storage area. The step of determining a set of urgent tasks with high urgency based on the adjusted task allocation scheme, and selecting resource-rich edge nodes as target nodes, includes: Based on the adjusted task plan, and combined with the preset data processing priorities and high urgency indicators of each node, a list of nodes involving cold storage energy consumption tasks is selected, the list of nodes is monitored, and the set of urgent tasks to be processed is formed. Obtain the availability of additional computing resources for each edge node; The edge nodes whose additional computing resource availability is greater than the preset resource availability threshold are selected as target nodes to undertake and process the tasks in the set of urgent tasks to be processed. The step of obtaining a real-time subset of energy consumption data from the target node and filtering out the subset of energy consumption data that needs to be accelerated includes: Based on the set of urgent tasks to be processed, determine the corresponding types and quantities of computing resources required, and obtain a resource allocation list for accelerating processing; Based on the resource allocation list, additional resources are distributed to the target nodes as needed; Based on the pre-acquired characteristics of cold storage energy consumption tasks, the task execution order is adjusted to obtain the adjusted task schedule. According to the task scheduling table, a subset of real-time energy consumption data is obtained from the target node, and the subset that needs to be processed first is selected to obtain the subset of energy consumption data that needs to be accelerated. The real-time energy consumption data subset includes temperature records, equipment power changes, and total energy consumption statistics for different cold storage areas. Based on the priority correspondence of tasks in the task scheduling table, the data content directly associated with high-priority tasks in the energy consumption data subset is filtered out to obtain the energy consumption data subset that needs to be accelerated.

2. The intelligent building energy-saving and carbon-reduction control method based on big data according to claim 1, characterized in that, The processing tasks of the edge nodes are initially divided based on their location distribution and the amount of data in each cold storage area, resulting in the processing task scope for each edge node, including: Based on the location distribution of the edge nodes and the amount of data in each cold storage area, the processing tasks of the edge nodes are initially divided and resources are allocated using pre-established allocation rules, resulting in a preliminary task scope division. Based on the preliminary task scope division, the current capability data of the edge nodes is obtained, and some tasks in the nodes whose current capability data is lower than the preset capability threshold are transferred to the nodes with higher capability data to obtain a preliminary task allocation scheme. The tasks are sorted according to the preset task priority of the cold storage area to obtain the task execution order; Monitor the task execution process according to the task execution order, obtain data volume fluctuation characteristics, dynamically adjust the preliminary task allocation scheme according to the data volume fluctuation characteristics, and determine the processing task range of each edge node.

3. The intelligent building energy-saving and carbon-reduction control method based on big data according to claim 1, characterized in that, The step of performing task migration operations on edge nodes whose task load exceeds a preset load balancing threshold to obtain an adjusted task allocation scheme includes: Determine whether the task load of each edge node exceeds the preset load balancing threshold. If so, add the corresponding node to the high load node list; otherwise, add the corresponding node to the low load node list. Obtain the communication latency data of each edge node, and select nodes whose communication latency data is lower than a preset latency threshold from the list of low-load nodes as the transfer nodes; In the list of high-load nodes, the transfer priority is determined based on the level of the communication latency data, and a list of tasks to be transferred is generated based on the transfer priority. The task list to be transferred is split according to the preset granularity control rules to obtain the split task units. The split task units are then distributed to the receiving node through a dynamic migration mechanism to obtain the adjusted task allocation scheme.

4. The intelligent building energy-saving and carbon-reduction control method based on big data according to claim 1, characterized in that, The method involves using a parallel processing mechanism within edge nodes to rapidly analyze the subset of energy consumption data requiring accelerated processing, thereby obtaining the energy consumption trend of the cold storage equipment. This includes: The energy consumption data subset that needs to be accelerated is divided into multiple data sub-units, and an independent processing thread is assigned to each data sub-unit to obtain an independent data unit. Based on a preset monitoring frequency, the thread processing progress of the independent data unit is monitored. After comparing the thread processing progress with a preset processing progress threshold, the computing resource allocation priority of the independent processing thread is dynamically adjusted to obtain a thread resource configuration table. Based on the thread resource configuration table, the processing results of the independent data units are integrated to obtain energy consumption data analysis results including energy consumption change trends.

5. The intelligent building energy-saving and carbon-reduction control method based on big data according to claim 1, characterized in that, The process of obtaining corresponding cold storage equipment operating parameters based on the energy consumption change trend, generating power adjustment commands according to preset power adjustment rules for cold storage equipment with abnormal energy consumption, and issuing them to the equipment control units of each cold storage area includes: Based on the energy consumption change trend, obtain the corresponding operating parameters of the cold storage equipment; The energy consumption trend of the cold storage equipment is visualized and abnormal data points are marked to obtain the abnormal energy consumption range. According to the preset tracking frequency, the abnormal energy consumption range is continuously monitored to obtain a list of abnormal energy consumption devices; Based on the preset power adjustment rules and the operating parameters of the cold storage equipment, a preliminary power adjustment range is generated, and a draft adjustment instruction is determined for each cold storage device in the list of energy-abnormal equipment. When the adjustment range in the draft adjustment instruction exceeds the preset instruction adjustment threshold, the draft adjustment instruction is verified through the preset instruction issuance protocol to obtain a control instruction set that meets the requirements. Based on the control instruction set and preset device priorities, instructions are distributed sequentially to each cold storage device in the list of devices with abnormal energy consumption.

6. A smart building energy-saving and carbon-reduction control system based on big data, characterized in that, The method for implementing the big data-based intelligent building energy-saving and carbon-reduction control method as described in any one of claims 1 to 5 includes: The initial data acquisition module is used to obtain equipment operation status data, energy consumption data and environmental temperature and humidity parameters of the cold storage area from multiple edge nodes distributed within the building area, forming an initial energy consumption dataset. The task preliminary division module is used to determine the amount of data in each of the cold storage areas based on the initial energy consumption dataset, and to perform preliminary division of the processing tasks of the edge nodes according to the location distribution of the edge nodes and the amount of data in each of the cold storage areas, so as to obtain the processing task range of each edge node. The load balancing adjustment module is used to analyze the processing task range of each edge node to obtain the task load of each edge node, and to perform task migration operation for edge nodes whose task load exceeds the preset load balancing threshold to obtain an adjusted task allocation scheme. The emergency task processing module is used to determine the set of emergency tasks with high urgency level according to the adjusted task allocation scheme, filter out the edge nodes with sufficient resources as target nodes, obtain a subset of real-time energy consumption data from the target nodes, and filter out the subset of energy consumption data that needs to be accelerated. The energy consumption trend analysis module is used to quickly analyze the subset of energy consumption data that needs to be accelerated through the parallel processing mechanism within the edge node, and obtain the energy consumption change trend of the cold storage equipment. The equipment control and execution module is used to obtain the corresponding cold storage equipment operating parameters according to the energy consumption change trend. For cold storage equipment with abnormal energy consumption, it generates power adjustment instructions according to preset power adjustment rules and sends them to the equipment control units of each cold storage area.

7. An electronic device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the big data-based smart building energy-saving and carbon-reduction control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the big data-based smart building energy-saving and carbon-reduction control method as described in any one of claims 1 to 5.

Images