A cloud computing-based intelligent building electrical management method and system

Abstract

This invention relates to the field of electrical management technology, specifically to a cloud-based intelligent building electrical management method and system, comprising the following steps: acquiring electricity consumption data to establish access records, statistically analyzing access frequency and adjusting nodes, analyzing task queues to allocate execution channels, resetting task paths to complete scheduling mapping, and constructing an electrical management command network. In this invention, by real-time recording of access behavior paths and statistical analysis of trigger frequencies, combined with access concentration judgment to adjust node positions, the problem of high-frequency node congestion is effectively alleviated. Task allocation is optimized through control sequence detection of task execution channels. The flexibility of task response is improved through a mechanism for resetting the attribution path and updating the task path, achieving precise correspondence between task status and node command flow, enhancing the ability to quickly adapt to changes in building electricity consumption status, and solving problems such as delayed equipment management response, uneven task distribution, and inefficient command execution in traditional models.

Description

Technical Field

[0001] This invention relates to the field of electrical management technology, and in particular to a cloud computing-based intelligent building electrical management method and system. Background Technology

[0002] The field of electrical management technology involves core aspects such as monitoring the operating status of power systems, scheduling and controlling power distribution, and managing the operation of electrical equipment. It primarily achieves the analysis and control of the energy efficiency of electrical equipment through the construction of a systematic architecture. This includes establishing data acquisition terminals to obtain power consumption data, using a central processing system to perform logical operations and strategic allocation on the data, and completing command feedback and equipment scheduling through a control execution mechanism. The aim is to improve power utilization, ensure the safe operation of electrical systems, and achieve dynamic power balance. Traditional building electrical management methods refer to the management of the operating status of electrical equipment such as power supply and distribution systems, lighting systems, and air conditioning systems in building facilities. This typically relies on manual inspections combined with local area monitoring systems to complete the operation records and energy consumption statistics of various equipment. Start-up and shutdown control of equipment is achieved by setting timed controls or load limiting logic. Specifically, this includes installing relays to achieve timed start-up and shutdown, adjusting load distribution using time-of-use electricity price data, managing lighting brightness and air conditioning temperature control range by manually setting parameters, and using wired communication networks to transmit sensor data back to the management terminal for preliminary analysis. Operators then issue control commands based on their experience, thereby completing the overall electrical operation management of the building.

[0003] Existing technologies in building electrical management rely on manual inspections and local monitoring systems, making it difficult to achieve real-time synchronous control of the operating status of equipment in multiple areas. Equipment data updates are limited to periodic uploads and manual readings, leading to the risk of system lag in regulation. Furthermore, there is a lack of dynamic correlation between access behavior and equipment status, failing to effectively reflect the actual load changes. Power consumption strategy adjustments are mostly based on fixed rules, lacking a flexible judgment mechanism based on access behavior frequency and equipment response status. Communication methods are mainly wired networks with fixed transmission paths, which cannot adaptively switch when faced with node congestion or delays. Equipment scheduling efficiency is limited, and task execution order often causes congestion or backlog due to uneven distribution of response nodes, ultimately affecting the energy efficiency and load balance stability of the overall electrical system. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a cloud computing-based intelligent building electrical management method, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a cloud computing-based intelligent building electrical management method, comprising the following steps: S1: Obtain the power consumption status data uploaded by the building power distribution terminal, extract the device identifier and corresponding time information, establish corresponding record items in the cloud dispatch center, detect whether each access behavior triggers the status reading operation, if it is triggered, record the corresponding trigger path of the behavior, and generate a list of regional access records. S2: Calculate the trigger frequency corresponding to each record in the area access record list, obtain the total number of times the same data node is accessed within a unit time period, determine whether there is a concentrated access situation, adjust the node according to the judgment result, and generate a path scheduling table adjustment list. S3: Statistically analyze the node distribution in the path scheduling table adjustment list, detect the current control task queue arrangement order of the nodes, mark the task occupancy status in the queue, filter execution channels with available slots, and generate a control queue order allocation list. S4: Based on the execution channel with an available slot in the control queuing order allocation list, locate the original task's belonging path, check whether the original path points to a lagging node, and if it matches, perform a task belonging path reset operation, transfer the task to the update node, and generate a task distribution path form. S5: Based on all updated records in the task distribution path form, summarize all currently effective paths and task ownership status, map the ownership list to the logical path of the control node in the cloud scheduling center, complete the confirmation of task execution and scheduling relationship, and establish a smart building electrical management command network.

[0005] As a further aspect of the present invention, the centralized access situation specifically refers to the situation where a data node is accessed more than three times consecutively within the same set time period, and the access interval is less than a set time interval threshold.

[0006] As a further aspect of the present invention, during the node adjustment process, the data node pointed to by the corresponding record is switched to a node with a shorter response time. If the number of accesses is less than two consecutive times within a unit time period and the access interval is greater than a set time interval threshold, the node position pointed to by the corresponding record is switched to a backup node.

[0007] As a further embodiment of the present invention, the area access record list includes device identification information, corresponding timestamp, and access behavior trigger path; the node adjustment list includes centralized access node number, backup node number, before and after adjustment number, task occupancy identifier, and optimization suggestion channel; the task distribution path form includes original path node number, updated path node number, task number, and transfer marker; and the intelligent building electrical management instruction network includes effective path set, task ownership node set, logical path mapping table, and scheduling confirmation identifier.

[0008] As a further aspect of the present invention, the step of obtaining the area access record list is as follows: S111: Obtain the power consumption status data uploaded by the building power distribution terminal, retrieve the timestamp and device identifier carried by each data record, use the device identifier as the primary key, establish a mapping between the timestamp and the bound key value, and generate a device access time mapping set; S112: Based on the device access time mapping set, detect whether each access time record coincides with the trigger time of the current state reading behavior. If they coincide, it is determined to be a trigger behavior. The determination result is added to the mapping item as a logical identifier to generate a trigger path labeling record table. S113: Based on the trigger path annotation record table, aggregate by device identifier, extract all trigger paths corresponding to each device, construct a data list structure with device identifier as primary key and corresponding trigger path set as key value, and generate a regional access record list.

[0009] As a further aspect of the present invention, the step of obtaining the path scheduling table adjustment list is as follows: S211: Obtain the list of access records for the area, extract the trigger time, data node identifier and access path information from each record, divide the time according to the length of the unit time period, count and count the access records of the same data node in each time period, aggregate them by node identifier and calculate the access frequency, and generate a data node access frequency table. S212: Based on the data node access frequency table, determine the change in access frequency of each data node in adjacent time periods. If the number of accesses is greater than three in three consecutive time periods and the time interval between adjacent accesses is less than the set time interval threshold, then mark it as a centralized access node and replace the data node pointed to by the record with a node position with a shorter response time. If the access frequency is less than two in a unit time period and the access interval is greater than the set time interval threshold, then replace the position pointed to by the record with a backup node and generate a data node replacement mapping table. S213: Based on the data node replacement mapping table, update the path corresponding to the node according to the path information in the original access record, establish a one-to-one mapping relationship between the changed path and the original path, and generate a path scheduling table adjustment list.

[0010] As a further aspect of the present invention, the step of obtaining the control queuing order allocation list is as follows: S311: Obtain the path scheduling table adjustment list, extract the data node number in each record, perform aggregation statistics according to the node number, calculate the number of times each node appears in the record, retrieve the current task queue status of each node, extract the task arrangement order and occupation identifier, and generate a node task queue occupation table. S312: Based on the node task queue occupancy table, identify the node numbers whose task queuing position is lagging or whose average waiting time exceeds a preset threshold, extract the response sequence parameter in the task queue, compare it with other node numbers under the same control area, filter the node numbers with priority in response order and current task occupancy rate of less than 50%, classify and aggregate them according to control channel number, and generate a set of schedulable channel numbers. S313: Based on the set of schedulable channel numbers, according to the number index of the lagging node in the node task queue occupancy table, reconstruct the original task path, replace the execution channel with the number corresponding to the idle position in the schedulable channel, establish a one-to-one mapping relationship between the updated node grouping information and the adjusted path, and generate a control queuing order allocation list.

[0011] As a further aspect of the present invention, the task distribution path form acquisition step is as follows: S411: Obtain the execution channel number marked as idle position in the control queuing order allocation list, extract the path identifier corresponding to each channel number, retrieve the path structure to which all tasks belong in the original path set, match the current channel number with the target node number in the original path, identify whether the node pointed to by the original path is a delayed response node, add an identifier label to the matching path, and generate a delayed task path location result set. S412: Based on the delayed task path location result set, extract the task number and original node number associated with each path, retrieve the idle channel number corresponding to the task in the control queuing order allocation list, perform task path adjustment operation according to the task number as index, replace the original target node in the path structure with the new node number pointed to by the corresponding idle channel number, and generate a task path update mapping table. S413: Based on the task path update mapping table, integrate all path structures with completed node replacements, aggregate and rearrange according to task number, extract the order relationship and node number of each level of nodes in the updated path, establish a serialized binding structure between task number and updated path, and generate a task distribution path form.

[0012] As a further aspect of the present invention, the intelligent building electrical management instruction network acquisition step is as follows: S511: Obtain all updated records in the task distribution path form, extract the task number, path node sequence and current belonging node information from each record, aggregate path data by task number, identify the registration status of the current path-pointing node in the control channel, extract the number of the path node that has been registered as a control node, establish the correspondence between task number and effective path node, and generate a task belonging path structure mapping table. S512: Based on the task attribution path structure mapping table, extract the attribution node corresponding to each task, retrieve the pre-set control node logical path structure in the cloud scheduling center, and perform a matching operation between the attribution node and the node number in the logical path according to the task number as the index. Eliminate path data that do not have a control path mapping relationship and generate a task logical path mapping list. S513: Based on the task logical path mapping list, extract the node numbers in all mapping paths, construct the mapping topology according to the task number, control node number and path structure, generate the path instruction flow link between control nodes according to the task configuration standard of the cloud scheduling center, and mark the node response status and task forwarding relationship to generate the intelligent building electrical management instruction network.

[0013] A cloud-based intelligent building electrical management system includes: The access record construction module is used to execute S1: obtain the power consumption status data uploaded by the building power distribution terminal, extract the device identifier and corresponding time information, establish the corresponding record item in the cloud dispatch center, detect whether each access behavior triggers the status reading operation, if it is triggered, record the corresponding trigger path of the behavior, and generate a list of regional access records. The path scheduling update module is used to execute S2: count the trigger frequency corresponding to each record in the area access record list, obtain the total number of times the same data node is accessed within a unit time period, determine whether there is a concentrated access situation, adjust the node according to the judgment result, and generate a path scheduling table adjustment list. The control sequence extraction module is used to perform S3: statistically analyze the node distribution in the path scheduling table adjustment list, detect the current control task queue arrangement order of the nodes, mark the task occupancy status in the queue, filter execution channels with available slots, and generate a control queuing order allocation list; The task path distribution module is used to execute S4: according to the control queuing order allocation list, the execution channel with an available position is located, the original task belonging path is located, and it is checked whether the original path points to the lagging node. If they match, the task belonging path is reset, the task is transferred to the update node, and a task distribution path form is generated. The instruction network establishment module is used to execute S5: based on all updated records in the task distribution path form, it summarizes all currently effective paths and task ownership status, maps the ownership list to the logical path of the control node in the cloud scheduling center, completes the confirmation of task execution and scheduling relationship, and establishes an intelligent building electrical management instruction network.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by recording access behavior paths in real time and statistically analyzing trigger frequencies, and adjusting node positions based on access concentration, the problem of high-frequency node congestion is effectively alleviated. By detecting the control sequence of task execution channels, task allocation is optimized, avoiding control delays caused by lagging nodes. The flexibility of task response is improved through the mechanism of resetting the belonging path and updating the task path. The precise correspondence between task status and node instruction flow is achieved through path mapping and confirmation of scheduling relationships, improving the ability to quickly adapt to changes in building power consumption status. The dynamic allocation of scheduling resources and efficient coordination of execution order among nodes are realized, thereby solving the problems of delayed equipment management response, uneven task distribution, and inefficient instruction execution in the traditional mode. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a flowchart of the process for obtaining the regional access record list in this invention; Figure 3 This is a flowchart of the process for obtaining the path scheduling table adjustment list in this invention; Figure 4 This is a flowchart illustrating the process of obtaining the queue order allocation list for this invention. Figure 5 This is a flowchart of the task distribution path form acquisition process for this invention; Figure 6 This is a flowchart illustrating the process of obtaining intelligent building electrical management instructions via a network, as per the present invention. Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a cloud computing-based intelligent building electrical management method, comprising the following steps: S1: Obtain the power consumption status data uploaded by the building power distribution terminal, extract the device identifier and corresponding time information, establish corresponding record items in the cloud dispatch center, detect whether each access behavior triggers the status reading operation, if it is triggered, record the trigger path corresponding to the behavior, and generate a list of regional access records. S2: Statistically analyze the trigger frequency of each record in the access record list of the statistical area, obtain the total number of times the same data node is accessed within a unit time period, and determine whether there is a concentrated access situation. If the number of accesses to a data node within the same set time period exceeds three consecutive times and the access interval is less than the set time interval threshold, it is considered that a concentrated access situation has occurred. Based on the judgment result, the node is adjusted and the data node pointed to by the corresponding record is changed to a node with a shorter response time. If the number of accesses is less than two consecutive times within a unit time period and the access interval is greater than the set time interval threshold, the node position pointed to by the corresponding record is changed to a backup node, and a path scheduling table adjustment list is generated. S3: Statistically analyze the node distribution in the path scheduling table adjustment list, detect the current control task queue arrangement order of the nodes, mark the task occupancy status in the queue, identify the node number with lagging response order and group it with other nodes for comparison, filter execution channels with available slots, and generate a control queuing order allocation list. S4: Based on the execution channel with available slots in the control queuing order allocation list, locate the original task's belonging path, check if the original path points to a lagging node, and if it matches, perform a task belonging path reset operation, transfer the task to the update node, and generate a task distribution path form. S5: Based on all updated records in the task distribution path form, summarize all currently effective paths and task ownership status, map the ownership list to the logical path of the control node in the cloud scheduling center, complete the confirmation of task execution and scheduling relationship, and establish a smart building electrical management command network.

[0020] The area access record list includes device identification information, corresponding timestamp, and access behavior trigger path; the node adjustment list includes centralized access node number, backup node number, response time before and after adjustment, and access frequency information; the control queuing order allocation list includes lagging node number, idle execution channel number, task occupation identifier, and optimization suggestion channel; the task distribution path form includes original path node number, updated path node number, task number, and transfer marker; and the intelligent building electrical management instruction network includes effective path set, task ownership node set, logical path mapping table, and scheduling confirmation identifier.

[0021] Please see Figure 2 The specific steps of S1 are as follows: S111: Obtain the power consumption status data uploaded by the building power distribution terminal, retrieve the timestamp and device identifier carried by each data record, use the device identifier as the primary key, establish a mapping between the timestamp and the bound key value, and generate a device access time mapping set; First, a data communication interface is established with the building's power distribution terminals. The system subscribes to topic messages published by the power distribution IoT gateway via the MQTT protocol and continuously receives raw power consumption status data packets uploaded by the terminals. For each received data packet, its JSON-formatted payload is parsed, extracting the value of the "timestamp" field as a timestamp and the string of the "device_id" field as a device identifier. A hash mapping storage space is allocated in memory, using the extracted device identifier as the primary key of the hash table. Then, all parsed timestamp data is traversed, converted to Unix time format (e.g., 1737936000 representing a specific time point), and all timestamps belonging to the same device identifier are sorted in ascending order of value, forming a time series array as the key-value pair corresponding to that primary key. This process is repeated for all connected terminal data, thereby constructing a device access time mapping set containing all devices and their corresponding historical access times.

[0022] S112: Based on the device access time mapping set, detect whether each access time record coincides with the trigger time of the current state read behavior. If they coincide, it is determined to be a triggered behavior. The determination result is added to the mapping item as a logical identifier to generate a trigger path annotation record table. Obtain the precise time point when the current state read action is triggered, and record it as the trigger time base value (e.g., 1737936100). Then, iterate through each key-value pair in the device access time mapping set. For each device identifier's time series array, extract each access time record and perform a difference calculation with the trigger time base value. Set the time overlap judgment threshold to 500 milliseconds. If the absolute value of the difference between the access time record and the trigger time base value is less than or equal to 500 milliseconds, then the access record is determined to be a triggered action. At this time, append a boolean logical flag "Is_Trigger=True" next to the time record; if the difference exceeds the threshold, mark it as "False". After comparing all records, filter out all records marked as True, extract their respective device identifier, timestamp, and corresponding original data path, and generate a trigger path annotation record table.

[0023] S113: Based on the trigger path annotation record table, aggregate by device identifier, extract all trigger paths corresponding to each device, construct a data list structure with device identifier as primary key and corresponding trigger path set as key value, and generate a regional access record list; The system reads the trigger path annotation record table and aggregates trigger path information belonging to the same device at different times, using device identifier as the aggregation dimension. For each unique device identifier, a list container is created, and all trigger paths appearing for that device in the record table are stored in the container in chronological order, forming the trigger path set for that device. Next, a new data list structure is constructed, setting the device identifier as the primary key and the aggregated trigger path set as the corresponding key-value pair. This list details all historical access trajectories for each device under specific trigger conditions, and the final output is a regional access record list.

[0024] Please see Figure 3 The specific steps of S2 are as follows: S211: Obtain the list of regional access records, extract the trigger time, data node identifier and access path information from each record, divide the time according to the length of the unit time period, count and count the access records of the same data node in each time period, aggregate them by node identifier and calculate the access frequency, and generate a data node access frequency table. The system reads the region access record list, parses each record, and extracts the trigger time, data node identifier (e.g., Node_ID_8801), and the specific access path string. The unit time period is set to 15 minutes (900 seconds). The entire 24 hours are divided into 96 consecutive time windows. For each time window, the system iterates through the trigger times of all records and maps them to the corresponding time period index. Within each time period, access records with the same data node identifier are identified and filtered. A counter is set to accumulate the count, yielding the total number of accesses for that node within that time period. A division operation is then performed, dividing the total number of accesses by the unit time period length (in minutes) to calculate the node's access frequency within the current time period. For example, if a node is accessed 45 times in 15 minutes, its access frequency is 3 times per minute. The calculated frequency value is then associated with the node identifier and time period index to generate a data node access frequency table.

[0025] S212: Based on the data node access frequency table, determine the change in access frequency of each data node in adjacent time periods. If the number of accesses is greater than three in three consecutive time periods and the time interval between adjacent accesses is less than the set time interval threshold, then mark it as a centralized access node and replace the data node pointed to by the record with the node position with a shorter response time. If the access frequency is less than two in a unit time period and the access interval is greater than the set time interval threshold, then replace the position pointed to by the record with a backup node and generate a data node replacement mapping table. Based on the data node access frequency table, logical judgments are made on the frequency fluctuations of each data node on a continuous time axis. First, a concentrated access threshold of 3 times per minute is set, and the threshold for adjacent access intervals is set to 2 seconds. The process selects frequency data from three consecutive time periods (e.g., time periods 10, 11, and 12). If a data node's access frequency is strictly greater than 3 times per minute in all three time periods, and the timestamp difference between two adjacent accesses in the node's record is strictly less than 2 seconds, then the node is determined to be in a high-load concentrated access state and is marked as a "concentrated access node." For such nodes, the network topology database is searched to find high-speed nodes with closer physical distances and lower hardware response latency parameters (e.g., response time less than 5 milliseconds), and the target pointer in the original record is replaced with that high-speed node. Conversely, if a data node's access frequency is continuously less than 2 times per minute in any unit of time period, and the adjacent access interval is greater than 5 minutes (300 seconds), then it is marked as an idle node, and the record's target pointer is replaced with a backup archive node (Cold_Store_Node). During this process, all changed node pairs (original node ID and new node ID) are recorded, and a data node replacement mapping table is generated.

[0026] To verify the effectiveness of the above logic and demonstrate the specific replacement decision-making process, some implementation data are selected for illustration, as shown in Table 1.

[0027] Table 1. Data Node Access Frequency Analysis and Replacement Decision Table; As shown in Table 1, the frequencies of node Node_101 in the three consecutive time periods were 4.2, 4.5, and 5.1 times / min, respectively, all exceeding the set threshold of 3 times / min, and the average interval of 1.2 seconds was less than the threshold of 2 seconds. Therefore, it was determined to be a concentrated access point and was replaced by the faster-responding Flash_Node_X. Node_102, with its low frequency and large intervals, was migrated to the backup node Backup_Node_Y. These experimental results demonstrate that dynamic frequency analysis can effectively identify hot and cold data points, allowing for targeted optimization of node resource allocation. Compared to the static allocation method, node response latency was reduced by approximately 25%.

[0028] S213: Based on the data node replacement mapping table, update the path corresponding to the node according to the path information in the original access record, establish a one-to-one mapping relationship between the replaced path and the original path, and generate a path scheduling table adjustment list. The system reads the data node replacement mapping table, iterates through each mapping record, and extracts the original node identifier and the new node identifier. Then, it backtracks the original list of area access records, searching for all access path information containing the original node identifier. For each matching path, it performs a string replacement operation, modifying the corresponding original node identifier field in the path string to the new node identifier. For example, it updates the path "Gateway->Switch_A->Node_101" to "Gateway->Switch_A->Flash_Node_X". After the update, it establishes a bidirectional index relationship between the modified new path and the original path, meaning that the new path can be queried through the original path, and the original path can be traced through the new path, ensuring audit traceability. Finally, it summarizes all updated and indexed entries to generate a path scheduling table adjustment list.

[0029] Please see Figure 4 The specific steps of S3 are as follows: S311: Obtain the path scheduling table adjustment list, extract the data node number in each record, perform aggregation statistics by node number, calculate the number of times each node appears in the record, retrieve the current task queue status of each node, extract the task arrangement order and occupation identifier, and generate a node task queue occupation table. The process begins by retrieving the path scheduling table adjustment list and parsing the data node numbers involved in each record. A counting hash table is then created in memory, using the node number as the key. Each time a node is encountered while traversing the list, its corresponding count is incremented by 1, thus calculating the total number of times each node appears in the adjustment list (i.e., the potential load). Subsequently, a status query command is sent to each data node via the internal bus to obtain a snapshot of the task queue for each node. From the snapshot data, the task order array (e.g., [Task_A, Task_B, Task_C]) and the task queue occupancy flag (e.g., Queue_Fullness_Flag) are extracted. These three types of information—the number of occurrences, the task order, and the occupancy flag—are then correlated and integrated to generate a node task queue occupancy table.

[0030] S312: Based on the node task queue occupancy table, identify the node numbers whose task queuing position is lagging or whose average waiting time exceeds a preset threshold, extract the response sequence parameter in the task queue, compare it with other node numbers under the same control area, filter the node numbers with priority in response order and current task occupancy rate of less than 50%, classify and aggregate them according to control channel number, and generate a set of schedulable channel numbers. The task queue occupancy table is read, and a queuing time threshold of 200 milliseconds is initially set. The task order data in the table is traversed, and the average estimated queuing time for tasks waiting in the current task queue of each node is calculated (i.e., the number of tasks in the queue multiplied by the average processing time per task). If the average queuing time of a node exceeds 200 milliseconds, or its task queuing position is lagging (i.e., the number of tasks to be processed exceeds 80% of the queue capacity), then the node number is identified as a "lagging response node." Next, the response order parameter (i.e., its current position in the queue) of these lagging nodes is extracted. Other node numbers under the same control area (e.g., the same floor's electrical distribution box) are retrieved, and their current task occupancy rates are compared. Node numbers with a task occupancy rate strictly below 50% and a priority response order (i.e., idle or queue length less than 3) are selected. These preferred nodes are categorized and aggregated according to their control channel number (Channel_ID), faulty channels are removed, and available channels are retained to generate a set of schedulable channel numbers.

[0031] S313: Based on the set of schedulable channel numbers, reconstruct the original task path according to the number index of the lagging node in the node task queue occupancy table, replace the execution channel with the number corresponding to the idle position in the schedulable channel, establish a one-to-one mapping relationship between the updated node grouping information and the adjusted path, and generate a control queuing order allocation list. Based on the set of schedulable channel numbers, the response lag node numbers identified in the node task queue occupancy table are used as index keys. For each lag node, a channel number in an idle position is searched in the set of schedulable channel numbers. A logical replacement operation is performed to reconstruct the original task path: the execution channel portion of the original path pointing to the lag node is replaced with the selected schedulable channel number. For example, if the original path used channel Ch_05 to access the lag node, it is now replaced with the use of the idle channel Ch_09. After the update, a one-to-one mapping is established between the new node grouping information (i.e., the binding relationship between the new channel and the node) and the adjusted path, and this mapping relationship is stored in a structured manner to generate a control queuing order allocation list.

[0032] Please see Figure 5 The specific steps of S4 are as follows: S411: Obtain the execution channel number marked as idle in the control queuing order allocation list, extract the path identifier corresponding to each channel number, retrieve the path structure to which all tasks belong in the original path set, match the current channel number with the target node number in the original path, identify whether the node pointed to by the original path is a delayed response node, add an identifier label to the matching path, and generate a delayed task path location result set. Read the control queuing order allocation list and filter out all execution channel numbers marked as "idle". For each idle channel number, extract its associated path identifier. Use this identifier to search the original path set to find all task items belonging to this path structure. Logically match the current idle channel number with the target node number defined in the original path, and check whether the node pointed to by the original path has been marked as a "lagging node" by previous steps. If the match is successful, it confirms that the original path is attempting to access a lagging node and has been allocated an idle channel, so add a specific identifier label "Status: Lag_Resolved" to the matching path. Summarize all tagged path information to generate a lagging task path location result set.

[0033] S412: Based on the delayed task path location result set, extract the task number and original node number associated with each path, retrieve the idle channel number corresponding to the task in the control queuing order allocation list, perform task path adjustment operation according to the task number as index, replace the original target node in the path structure with the new node number pointed to by the corresponding idle channel number, and generate a task path update mapping table. Based on the delayed task path location result set, the task ID (Task_ID) and original node ID associated with each path record are extracted. Simultaneously, the queuing order allocation list is retrieved again to obtain the available channel ID assigned to the task. Using the task ID as a unique index, a specific task path adjustment operation is performed: the original node ID is located in the path data structure, and its value is modified to the new node ID pointed to by the corresponding available channel ID. For example, if task Task_99 originally pointed to node Node_Old, it is now replaced with Node_New pointed to by channel Ch_New according to the list. This operation ensures that the task data flow actually flows to the less loaded physical channel. The correspondence before and after each replacement is recorded, generating a task path update mapping table.

[0034] S413: Based on the task path update mapping table, integrate all path structures with completed node replacements, aggregate and rearrange according to task number, extract the order relationship and node number of each level of nodes in the updated path, establish a serialized binding structure between task number and updated path, and generate a task distribution path form. Read the task path update mapping table and aggregate the scattered update records. Perform aggregation based on task number, concatenating or rearranging all path segments belonging to the same task number. Parse the updated complete path, extracting the order relationship of nodes at each level (from source to end) and the latest node number sequence. Establish a serialization binding structure in memory, using the task number as the index header and serializing the updated path node sequence into binary or JSON strings as the content body, ensuring that the execution order of the path is strictly locked. After binding all tasks, output a standardized task distribution path form.

[0035] Please see Figure 6 The specific steps of S5 are as follows: S511: Obtain all updated records in the task distribution path form, extract the task number, path node sequence and current belonging node information from each record, aggregate path data by task number, identify the registration status of the current path-pointed node in the control channel, extract the number of the path node that has been registered as a control node, establish the correspondence between task number and effective path node, and generate a task belonging path structure mapping table. Load all updated records in the task distribution path form, and parse each record to extract the task number, path node sequence, and current owner node information. Using the task number as the aggregation key, centralize all path data related to the same task. Then, access the control channel registry to identify the registration status (Registered / Unregistered) of each node pointed to in the current path within the control channel. Filter out node numbers with a status of "Registered" and control permissions from the path node sequence and mark them as "Effective Nodes." Establish a strong correspondence between task numbers and these effective path nodes, explicitly specifying which tasks must be handled by which registered nodes, and generate a task ownership path structure mapping table.

[0036] S512: Based on the task attribution path structure mapping table, extract the attribution node corresponding to each task, retrieve the pre-set control node logical path structure in the cloud scheduling center, use the task number as the index, perform the matching operation between the attribution node and the node number in the logical path, remove path data that does not have a control path mapping relationship, and generate a task logical path mapping list. Based on the task attribution path structure mapping table, extract the attribution node number corresponding to each task. Establish an encrypted connection with the cloud scheduling center and retrieve the pre-set standard control node logical path structure (i.e., theoretically valid control logic diagram) in the cloud. Using the task number as an index, match the locally generated attribution node path with the logical path structure in the cloud point by point. Check whether the node jumps in the path conform to logical constraints (e.g., check whether there is illegal logic of jumping directly from the lighting control node to the elevator power node). If path data without control path mapping relationship or violating logical constraints is found, remove it from the list. Retain all successfully matched and valid path data to generate a task logical path mapping list.

[0037] Suppose that the local path of Task_A contains the node jump sequence [N1->N2->N3].

[0038] Parameter acquisition: Local path node sequence: N1 (main control panel, type ID=10), N2 (intermediate relay, type ID=20), N3 (end lamp, type ID=30).

[0039] Cloud-based logical constraint table: Defines the allowed jump relationships. Rule R1: Type 10 can jump to Type 20; Rule R2: Type 20 can jump to Type 30; Rule R3: Type 20 cannot jump to Type 50 (security camera).

[0040] The path to be verified, Task_B, is a sequence [N1->N2->N5], where N5 is of type 50.

[0041] Logical verification operation: For Task_A: Check N1->N2, search rule, matches R1; check N2->N3, search rule, matches R2. Result: Path is valid, keep it.

[0042] For Task_B: Check N1->N2, which matches R1; check N2->N5, find the rule, and it matches R3 (prohibits jumps). Result: Invalid path, triggering the removal mechanism.

[0043] Result generation: Task_A is written into the task logical path mapping list.

[0044] Task_B is marked as "Logic_Error" and discarded, while also being logged.

[0045] The advantage of this verification logic is that by forcibly comparing the standard logic in the cloud, it prevents illegal device control links caused by errors in local scheduling algorithms or malicious tampering, thus ensuring the safe operation of building electrical systems.

[0046] S513: Based on the task logic path mapping list, extract the node numbers in all mapping paths, construct the mapping topology according to the task number, control node number and path structure, generate the path instruction flow link between control nodes according to the task configuration standard of the cloud scheduling center, and mark the node response status and task forwarding relationship to generate the intelligent building electrical management instruction network. Based on the verified task logic path mapping list, node numbers are extracted from all legal mapping paths. A multi-dimensional mapping topology is constructed according to the three elements of task number, control node number, and path structure. In the topology, nodes represent devices, and edges represent the flow direction of control commands. Based on the task configuration standards (such as priority policies and broadcast policies) issued by the cloud scheduling center, path command flow links between control nodes are generated in the topology. Simultaneously, each node in the topology is marked with its response status (e.g., online, dormant, busy) and task forwarding relationship (e.g., unicast, multicast). Finally, this topology structure, containing complete status information and flow logic, is instantiated to generate an intelligent building electrical management command network.

[0047] Please see Figure 7 A cloud-based intelligent building electrical management system includes: The access record construction module is used to execute S1: obtain the power consumption status data uploaded by the building power distribution terminal, extract the device identifier and corresponding time information, establish the corresponding record item in the cloud dispatch center, detect whether each access behavior triggers the status reading operation, if it is triggered, record the corresponding trigger path of the behavior, and generate a list of regional access records. The path scheduling update module is used to execute S2: the trigger frequency corresponding to each record in the statistical area access record list, to obtain the total number of times the same data node is accessed within a unit time period, to determine whether there is a concentrated access situation, to adjust the node according to the judgment result, and to generate a path scheduling table adjustment list. The control sequence extraction module is used to execute S3: statistical path scheduling table adjustment list node distribution, detect the current control task queue order of nodes, mark the task occupancy status in the queue, filter execution channels with available slots, and generate control queue order allocation list; The task path distribution module is used to execute S4: according to the control queuing order allocation list, the execution channel with an available position is allocated, the original task's belonging path is located, and the original path is checked to see if it points to a lagging node. If they match, the task's belonging path is reset, the task is transferred to the update node, and a task distribution path form is generated. The instruction network establishment module is used to execute S5: based on all updated records in the task distribution path form, it summarizes all currently effective paths and task ownership status, maps the ownership list to the logical path of the control node in the cloud scheduling center, completes the confirmation of task execution and scheduling relationship, and establishes an intelligent building electrical management instruction network.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A cloud computing-based intelligent building electrical management method, characterized in that, Includes the following steps: S1: Obtain the power consumption status data uploaded by the building power distribution terminal, extract the device identifier and corresponding time information, establish corresponding record items in the cloud dispatch center, detect whether each access behavior triggers the status reading operation, if it is triggered, record the corresponding trigger path of the behavior, and generate a list of regional access records. S2: Calculate the trigger frequency corresponding to each record in the area access record list, obtain the total number of times the same data node is accessed within a unit time period, determine whether there is a concentrated access situation, adjust the node according to the judgment result, and generate a path scheduling table adjustment list. S3: Statistically analyze the node distribution in the path scheduling table adjustment list, detect the current control task queue arrangement order of the nodes, mark the task occupancy status in the queue, filter execution channels with available slots, and generate a control queue order allocation list. S4: Based on the execution channel with an available slot in the control queuing order allocation list, locate the original task's belonging path, check whether the original path points to a lagging node, and if it matches, perform a task belonging path reset operation, transfer the task to the update node, and generate a task distribution path form. S5: Based on all updated records in the task distribution path form, summarize all currently effective paths and task ownership status, map the ownership list to the logical path of the control node in the cloud scheduling center, complete the confirmation of task execution and scheduling relationship, and establish a smart building electrical management command network.

2. The intelligent building electrical management method based on cloud computing according to claim 1, characterized in that: The term "centralized access situation" specifically refers to a situation where a data node is accessed more than three times consecutively within the same set time period, and the access interval is less than a set time interval threshold.

3. The intelligent building electrical management method based on cloud computing according to claim 1, characterized in that: During the node adjustment process, the data node pointed to by the corresponding record is switched to a node with a shorter response time. If the number of accesses is less than two consecutive times within a unit time period and the access interval is greater than a set time interval threshold, the node position pointed to by the corresponding record is switched to a backup node.

4. The intelligent building electrical management method based on cloud computing according to claim 1, characterized in that: The area access record list includes device identification information, corresponding timestamp, and access behavior trigger path. The node adjustment list includes centralized access node number, backup node number, response time before and after adjustment, and access frequency information. The control queuing order allocation list includes lagging node number, idle execution channel number, task occupancy identifier, and optimization suggestion channel. The task distribution path form includes original path node number, updated path node number, task number, and transfer marker. The intelligent building electrical management instruction network includes effective path set, task ownership node set, logical path mapping table, and scheduling confirmation identifier.

5. The cloud computing-based intelligent building electrical management method according to claim 1, characterized in that, The steps for obtaining the regional access record list are as follows: S111: Obtain the power consumption status data uploaded by the building power distribution terminal, retrieve the timestamp and device identifier carried by each data record, use the device identifier as the primary key, establish a mapping between the timestamp and the bound key value, and generate a device access time mapping set; S112: Based on the device access time mapping set, detect whether each access time record coincides with the trigger time of the current state reading behavior. If they coincide, it is determined to be a trigger behavior. The determination result is added to the mapping item as a logical identifier to generate a trigger path labeling record table. S113: Based on the trigger path annotation record table, aggregate by device identifier, extract all trigger paths corresponding to each device, construct a data list structure with device identifier as primary key and corresponding trigger path set as key value, and generate a regional access record list.

6. The cloud computing-based intelligent building electrical management method according to claim 1, characterized in that, The steps for obtaining the path scheduling table adjustment list are as follows: S211: Obtain the list of access records for the area, extract the trigger time, data node identifier and access path information from each record, divide the time according to the length of the unit time period, count and count the access records of the same data node in each time period, aggregate them by node identifier and calculate the access frequency, and generate a data node access frequency table. S212: Based on the data node access frequency table, determine the change in access frequency of each data node in adjacent time periods. If the number of accesses is greater than three in three consecutive time periods and the time interval between adjacent accesses is less than the set time interval threshold, then mark it as a centralized access node and replace the data node pointed to by the record with a node position with a shorter response time. If the access frequency is less than two in a unit time period and the access interval is greater than the set time interval threshold, then replace the position pointed to by the record with a backup node and generate a data node replacement mapping table. S213: Based on the data node replacement mapping table, update the path corresponding to the node according to the path information in the original access record, establish a one-to-one mapping relationship between the changed path and the original path, and generate a path scheduling table adjustment list.

7. The cloud computing-based intelligent building electrical management method according to claim 1, characterized in that, The steps for obtaining the control queuing order allocation list are as follows: S311: Obtain the path scheduling table adjustment list, extract the data node number in each record, perform aggregation statistics according to the node number, calculate the number of times each node appears in the record, retrieve the current task queue status of each node, extract the task arrangement order and occupation identifier, and generate a node task queue occupation table. S312: Based on the node task queue occupancy table, identify the node numbers whose task queuing position is lagging or whose average waiting time exceeds a preset threshold, extract the response sequence parameter in the task queue, compare it with other node numbers under the same control area, filter the node numbers with priority in response order and current task occupancy rate of less than 50%, classify and aggregate them according to control channel number, and generate a set of schedulable channel numbers. S313: Based on the set of schedulable channel numbers, according to the number index of the lagging node in the node task queue occupancy table, reconstruct the original task path, replace the execution channel with the number corresponding to the idle position in the schedulable channel, establish a one-to-one mapping relationship between the updated node grouping information and the adjusted path, and generate a control queuing order allocation list. 8.The cloud-computing-based intelligent building electrical management method according to claim 1, wherein, The steps for obtaining the task distribution path form are as follows: S411: Obtain the execution channel number marked as idle position in the control queuing order allocation list, extract the path identifier corresponding to each channel number, retrieve the path structure to which all tasks belong in the original path set, match the current channel number with the target node number in the original path, identify whether the node pointed to by the original path is a delayed response node, add an identifier label to the matching path, and generate a delayed task path location result set. S412: Based on the delayed task path location result set, extract the task number and original node number associated with each path, retrieve the idle channel number corresponding to the task in the control queuing order allocation list, perform task path adjustment operation according to the task number as index, replace the original target node in the path structure with the new node number pointed to by the corresponding idle channel number, and generate a task path update mapping table. S413: Based on the task path update mapping table, integrate all path structures with completed node replacements, aggregate and rearrange according to task number, extract the order relationship and node number of each level of nodes in the updated path, establish a serialized binding structure between task number and updated path, and generate a task distribution path form. 9.The cloud-computing-based intelligent building electrical management method according to claim 1, wherein, The steps for obtaining the intelligent building electrical management command network are as follows: S511: Obtain all updated records in the task distribution path form, extract the task number, path node sequence and current belonging node information from each record, aggregate path data by task number, identify the registration status of the current path-pointing node in the control channel, extract the number of the path node that has been registered as a control node, establish the correspondence between task number and effective path node, and generate a task belonging path structure mapping table. S512: Based on the task attribution path structure mapping table, extract the attribution node corresponding to each task, retrieve the pre-set control node logical path structure in the cloud scheduling center, and perform a matching operation between the attribution node and the node number in the logical path according to the task number as the index. Eliminate path data that do not have a control path mapping relationship and generate a task logical path mapping list. S513: Based on the task logical path mapping list, extract the node numbers in all mapping paths, construct the mapping topology according to the task number, control node number and path structure, generate the path instruction flow link between control nodes according to the task configuration standard of the cloud scheduling center, and mark the node response status and task forwarding relationship to generate the intelligent building electrical management instruction network.

10. A cloud computing based intelligent building electrical management system, characterized in that, The system is used to implement the cloud computing-based intelligent building electrical management method according to any one of claims 1-9, comprising: The access record construction module is used to execute S1: obtain the power consumption status data uploaded by the building power distribution terminal, extract the device identifier and corresponding time information, establish the corresponding record item in the cloud dispatch center, detect whether each access behavior triggers the status reading operation, if it is triggered, record the corresponding trigger path of the behavior, and generate a list of regional access records. The path scheduling update module is used to execute S2: count the trigger frequency corresponding to each record in the area access record list, obtain the total number of times the same data node is accessed within a unit time period, determine whether there is a concentrated access situation, adjust the node according to the judgment result, and generate a path scheduling table adjustment list. The control sequence extraction module is used to perform S3: statistically analyze the node distribution in the path scheduling table adjustment list, detect the current control task queue arrangement order of the nodes, mark the task occupancy status in the queue, filter execution channels with available slots, and generate a control queuing order allocation list; The task path distribution module is used to execute S4: according to the control queuing order allocation list, the execution channel with an available position is located, the original task belonging path is located, and it is checked whether the original path points to the lagging node. If they match, the task belonging path is reset, the task is transferred to the update node, and a task distribution path form is generated. The instruction network establishment module is used to execute S5: based on all updated records in the task distribution path form, it summarizes all currently effective paths and task ownership status, maps the ownership list to the logical path of the control node in the cloud scheduling center, completes the confirmation of task execution and scheduling relationship, and establishes an intelligent building electrical management instruction network.

Images