Distributed indoor temperature and humidity data processing system and method

Abstract

The invention relates to the technical field of environment management, in particular to a distributed indoor temperature and humidity data processing system and method, and the system comprises a node data initialization module, a state difference extraction module, a multiple offset judgment module, a fluctuation priority labeling module and a data processing scheduling module. According to the method, matching difference analysis is carried out on temperature and humidity sampling values of a plurality of nodes in an indoor area, a binding structure is established, and a state difference ratio sequence is constructed in combination with a historical difference mean value so as to extract a key fluctuation index, so that dynamic identification of state change in a period is realized; according to the method, fluctuation salient region nodes are screened by means of comparison of an offset ratio and a response threshold, a to-be-processed region is determined in a spatial distribution dimension, a map basis is formed, data units with high priorities and data units with common priorities are distinguished by comparing a data mean value of a continuous period with a sensitive threshold, hierarchical scheduling execution and time sequence processing are realized, and the scheduling efficiency is improved. And the recognition precision and the regulation and control efficiency of the environment state change are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental management, and in particular to a distributed indoor temperature and humidity data processing system and method. Background Art

[0002] The field of environmental management technology involves monitoring, controlling, and optimizing environmental parameters within living or working spaces, including the data collection, transmission, and analysis of key environmental factors such as temperature, humidity, air quality, lighting, and noise. This is used to support comprehensive management objectives such as building energy management, comfort adjustment, and safety control. It systematically covers sensor network deployment, environmental data management processes, and control strategy execution, forming an environmental information processing framework that integrates perception, calculation, and scheduling. Among them, the indoor temperature and humidity data processing system refers to a system used to collect and process indoor temperature and humidity information to assist in environmental regulation. The technical issues addressed are how to efficiently collect, centrally process, and determine the status of distributed temperature and humidity data in multiple spatial areas. Fixed-point sensors are usually connected to a central processing device via a bus. The collected data is transmitted to a master control device using a temperature and humidity acquisition circuit and a serial communication protocol. The master control device then uses a threshold comparison method to determine and record the temperature and humidity status.

[0003] In the existing indoor temperature and humidity data process, fixed-point sensors connected centrally through a bus mainly transmit the collected data to the main control device for judgment and processing. In scenarios with multiple nodes concurrently or with wide spatial distribution, data update lags are easily caused by the concentration of collection paths and the single judgment method. The threshold comparison method cannot effectively identify the state trend deviation caused by periodic subtle changes. If some nodes gradually deviate from the normal state in a continuous period but do not cross the fixed threshold, they may not be identified and recorded in time, affecting the timeliness and accuracy of subsequent environmental adjustment decisions. In living or working spaces with high fluctuation sensitivity requirements, there is a clear lack of support for actual comfort and energy efficiency control. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a distributed indoor temperature and humidity data processing system and method.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A distributed indoor temperature and humidity data processing system includes: The node data initialization module obtains the node number of the indoor area, collects the temperature sampling value and humidity sampling value of the current period, establishes a one-to-one corresponding data set based on the node number pair, and binds the node number pair with the corresponding temperature and humidity data to generate a node difference initialization data set; The state difference extraction module calculates the difference of the temperature and humidity sampling values ​​of each node pair based on the node difference initialization data set, binds them to the node number pairs in sequence to form a difference recording unit, performs a difference operation on the difference recording unit and the average difference of the node's three historical cycles, extracts the maximum value of the temperature difference ratio and the humidity difference ratio, and obtains the state difference indicator sequence between the nodes; The multiple offset determination module selects all node pair numbers whose maximum ratio values ​​exceed the response threshold multiple according to the inter-node state difference indicator sequence, and establishes a corresponding area number mapping to obtain a multiple offset node distribution map within the period; The fluctuation priority labeling module calculates the average temperature and humidity data of each node in the current cycle and the previous two cycles based on the multiple offset node distribution map within the cycle. If it exceeds the fluctuation sensitivity threshold, it is marked as a high-priority data unit, otherwise it is marked as a normal priority unit, and a node-level temperature and humidity fluctuation priority identification set is generated.

[0006] As a further solution of the present invention, the node difference initialization data set includes a node number pair mapping relationship, a temperature and humidity sampling data set within a period, and a data set binding structure; the state difference indicator sequence includes a temperature difference ratio sequence between nodes, a humidity difference ratio sequence between nodes, and a ratio difference maximum value record item; the multiple offset node distribution map within the period includes node pair numbers exceeding the threshold, a node area number mapping relationship, and an abnormal distribution map structure within the period; the node-level temperature and humidity fluctuation priority identification set includes the node temperature and humidity mean, the comparison result with the fluctuation sensitivity threshold, and the priority identification label.

[0007] As a further solution of the present invention, the node data initialization module includes: The node number extraction submodule obtains all adjacent node numbers in the indoor area node number table, identifies each pair of node numbers with a direct connection relationship, and establishes a number pair set to generate a node number pair set; The environmental data acquisition submodule reads the temperature sampling value and humidity sampling value corresponding to each pair of node numbers in the current cycle based on the node number pair set, extracts the environmental sampling value of each node respectively, and combines and constructs two sets of environmental data sets under the node pair to obtain two sets of environmental sampling value sets; The difference data generation submodule extracts the sequence number, cycle number and environmental data combination information of each pair of nodes based on the two sets of environmental sampling values, unifies the structured organization, binds each node number pair with its corresponding environmental data set, and generates a node difference initialization data set.

[0008] As a further solution of the present invention, the state difference extraction module includes: The difference record generation submodule is based on the node difference initialization data set, extracts the current period temperature sampling value and humidity sampling value corresponding to each group of node number pairs, establishes the temperature difference value and humidity difference value of each pair of nodes as a set of difference data, unifies all record formats and classifies them into a cache set, and establishes node environment difference records; The period difference comparison submodule reads the temperature difference and humidity difference historical data of the node number pair in the previous three consecutive periods according to the node environment difference record, using the node number pair and the current period number as the index key, and performs difference ratio processing on the temperature difference value of the current period and the average of the temperature difference values ​​of the previous three periods in turn. At the same time, the same operation is performed on the humidity difference value of the current period and the average of the humidity difference values ​​of the previous three periods, and respectively calculates the period temperature difference ratio and the period humidity difference ratio, and uniformly binds the node number pair and the period number to obtain a node difference ratio sequence; The difference index extraction submodule extracts the temperature difference ratio and humidity difference ratio record values ​​of each group of node pairs in the current cycle according to the node difference ratio sequence, extracts the maximum value, binds it with the node number pair to form a record structure, and stores it in the indicator sequence set to establish a state difference indicator sequence.

[0009] As a further solution of the present invention, the multiple shift determination module includes: The threshold comparison submodule extracts the temperature difference ratio value and the humidity difference ratio value of the current period from the node difference ratio sequence based on the temperature difference ratio and the humidity difference ratio in the inter-node state difference indicator sequence, calculates and obtains the offset exceeding threshold intensity value, and screens the node pairs whose offset exceeds the trend according to whether the intensity value is greater than zero, and generates an offset exceeding limit node number set; The regional mapping submodule divides the regions to which the node numbers belong based on the offset overrun node number set and the number prefixes of the node numbers, extracts the region identifiers carried in the numbers as the region mapping index keys, clusters all overrun node pairs based on the region numbers, and obtains the regional offset node index structure; The node map generation submodule obtains the node number pair set under each area according to the area offset node index structure, generates node connection edges according to the area number as the index, constructs an undirected graph structure according to the order of the node pairs, registers the graph structure with the period as the index number, and generates a node distribution map with multiple offsets within the period.

[0010] As a further solution of the present invention, the fluctuation priority marking module includes: The periodic data extraction submodule extracts the original temperature and humidity data uploaded by each node in the current period and the previous two periods based on the node number in the multiple offset node distribution map within the period, classifies and organizes the data by node number, generates a temperature and humidity data sequence indexed by period according to the node number, and establishes a three-period temperature and humidity data structure; The threshold judgment submodule reads the temperature and humidity data corresponding to the current cycle on a node-by-node basis based on the three-cycle temperature and humidity data structure, compares the same data from the previous two cycles, performs a difference magnitude confirmation operation on each type of data, sets standard values ​​for temperature and humidity fluctuation judgment, determines whether they exceed the perceptible range, identifies and archives the node priority status, and obtains a node fluctuation status label set; The priority identification generation submodule extracts the node numbers and status identification contents marked as fluctuation states in the current cycle according to the node fluctuation state label set, establishes a time index in combination with the cycle number, outputs the status result information of each node in a structured manner, maps and integrates the node numbers and priority identifications, and establishes a node-level temperature and humidity fluctuation priority identification set.

[0011] As a further embodiment of the present invention, the system further comprises: The data processing scheduling module inserts the node-level temperature and humidity fluctuation priority identification set into the priority scheduling processing pool and the common scheduling processing pool according to the priority type, dequeues the data in chronological order and performs data processing in sequence, and establishes a distributed indoor temperature and humidity data processing record table; The distributed indoor temperature and humidity data processing record table includes node number record items, scheduling time point information, processing resource type identifier, and task execution order record items.

[0012] As a further solution of the present invention, the data processing scheduling module includes: The priority pool division submodule writes the data units corresponding to the high-priority node numbers into the priority scheduling processing pool and the data units corresponding to the ordinary priority node numbers into the ordinary scheduling processing pool based on the priority tag information of all nodes in the node-level temperature and humidity fluctuation priority identification set. The data units are sorted in ascending order according to the timestamps carried by the node numbers, and data entries with time conflicts are eliminated. The data sets of each priority type are cached separately in a queue structure to generate a priority scheduling data set; The time queue management submodule sets two scheduling processing channels based on the priority scheduling data set and the scheduling priority type. It dequeues the data unit at the head end of the queue in each scheduling cycle, records the current system timestamp for each processing and uses the time as the scheduling time point of the data unit, records the dequeue sequence number, organizes the records with the node number as the primary key, and obtains the data processing scheduling sequence record table; The scheduling record generation submodule arranges all fields in ascending order according to the node number, scheduling time point, processing resource type and scheduling sequence number information of each record in the data processing scheduling sequence record table, and uses the cycle number and processing time point to jointly generate a unique primary key index to distinguish the task scheduling behaviors at different time points in the same cycle, and establish a distributed indoor temperature and humidity data processing record table.

[0013] A distributed indoor temperature and humidity data processing method comprises the following steps: S1: Obtain the node number of the indoor area, collect the temperature sampling value and humidity sampling value of the current period respectively, establish a one-to-one corresponding data set based on the node number pair, and bind the node number pair with the corresponding temperature and humidity data to generate a node difference initialization data set; S2: Based on the node difference initialization data set, the temperature and humidity sampling values ​​of each node pair are calculated, and the difference is sequentially bound to the node number pair to form a difference recording unit. The difference recording unit is then subjected to a difference operation with the average difference of the node's three historical cycles. The maximum values ​​of the temperature difference ratio and the humidity difference ratio are extracted to obtain a sequence of state difference indicators between nodes. S3: Based on the inter-node state difference indicator sequence, filter all node pair numbers whose maximum ratio value exceeds the response threshold multiple, and establish a corresponding area number mapping to obtain a node distribution map of the multiple offset within the period; S4: Based on the node distribution map of multiple offsets within the cycle, the average temperature and humidity data of each node in the current cycle and the previous two cycles is calculated. If it exceeds the fluctuation sensitivity threshold, it is marked as a high-priority data unit; otherwise, it is marked as a normal priority unit. The node-level temperature and humidity fluctuation priority identification set is generated; S5: According to the node-level temperature and humidity fluctuation priority identification set, insert them into the priority scheduling processing pool and the ordinary scheduling processing pool according to the priority type, dequeue them in chronological order and perform data processing in sequence to establish a distributed indoor temperature and humidity data processing record table.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, paired difference analysis is performed on the temperature and humidity sampling values ​​of multiple nodes in the indoor area and a binding structure is established. The state difference ratio sequence is constructed in combination with the historical difference mean to extract key fluctuation indicators, thereby realizing dynamic identification of state changes within a period. The nodes in the area with significant fluctuations are screened by comparing the offset ratio with the response threshold. The area to be processed is clarified in the spatial distribution dimension and a map basis is formed. By comparing the data mean of consecutive periods with the sensitive threshold, high-priority and ordinary-priority data units are distinguished, and hierarchical scheduling execution and time series processing are realized to ensure that the data processing of nodes with abnormal states has timely response and resource adaptability. In scenarios with wide spatial distribution and frequent state fluctuations, the recognition accuracy and control efficiency of environmental state changes are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the node data initialization module of the present invention; Figure 3This is a flow chart of the state difference extraction module of the present invention; Figure 4 This is a flow chart of the multiple shift determination module of the present invention; Figure 5 This is a flow chart of the fluctuation priority marking module of the present invention; Figure 6 This is a flow chart of the data processing scheduling module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0018] See also Figure 1 , a distributed indoor temperature and humidity data processing system includes: The node data initialization module obtains each pair of adjacent node numbers in the indoor area node number table, collects the temperature sampling value and humidity sampling value of the current period respectively, establishes a one-to-one corresponding data set based on the node number pair, and binds each group of node number pairs with the corresponding temperature and humidity data set to generate a node difference initialization data set; The state difference extraction module uses the node difference initialization data set to calculate the difference between the temperature and humidity sampling values ​​of each node pair. The difference results are sequentially bound to the node number pairs to form a difference record unit. The difference record unit is then subjected to the difference operation with the mean difference of the node's three historical cycles. A difference ratio sequence is constructed, and the maximum value of the temperature difference ratio and the humidity difference ratio is extracted to obtain the state difference indicator sequence between nodes. The multiple offset determination module compares the maximum temperature and humidity difference ratios of each node pair in the inter-node state difference indicator sequence with the response threshold multiple (the abnormal fluctuation determination threshold defined in the building environment monitoring standard: 1.8 times the historical mean difference). It then filters all node pairs that exceed the threshold and establishes a corresponding regional number mapping to form a node set for processing. This generates a distribution map of multiple offset nodes within the period. The fluctuation priority labeling module extracts the temperature and humidity data uploaded by each node in the current cycle and the previous two cycles based on the node number in the multiple offset node distribution map within the cycle. It calculates the average value and compares it with the fluctuation sensitivity threshold (the critical value of temperature and humidity changes that can be perceived by the human body as specified in the thermal environment measurement standard, temperature ±0.5°C / humidity ±3%RH). If it exceeds the threshold, it is marked as a high-priority data unit; otherwise, it is marked as a normal priority unit, and a node-level temperature and humidity fluctuation priority identification set is generated. The data processing scheduling module identifies all high-priority and normal-priority data units in the node-level temperature and humidity fluctuation priority set, and inserts them into the priority scheduling processing pool (high-priority data processing queue defined by the Internet of Things system architecture standard: response delay ≤100ms) and the normal scheduling processing pool (regular processing queue defined by the edge computing standard: response delay ≤500ms) according to the priority type. The data processing is performed in chronological order, and the node number, scheduling time point, processing resource type and execution order of each processing task are recorded to establish a distributed indoor temperature and humidity data processing record table.

[0019] The node difference initialization data set includes the node number pair mapping relationship, the temperature and humidity sampling data set within the period, and the data set binding structure. The state difference indicator sequence includes the temperature difference ratio sequence between nodes, the humidity difference ratio sequence between nodes, and the ratio difference maximum value record item. The multiple offset node distribution map within the period includes the node pair numbers exceeding the threshold, the node area number mapping relationship, and the abnormal distribution map structure within the period. The node-level temperature and humidity fluctuation priority identification set includes the node temperature and humidity mean, the comparison result with the fluctuation sensitivity threshold, and the priority identification label. The distributed indoor temperature and humidity data processing record table includes the node number record item, scheduling time point information, processing resource type identification, and task execution order record item.

[0020] See also Figure 2 , the node data initialization module includes: The node number extraction submodule obtains all adjacent node numbers in the indoor area node number table, identifies each pair of node numbers with a direct connection relationship, and establishes a number pair set to generate a node number pair set; To obtain all adjacent node numbers in the indoor area node number table, you first need to extract the node number information based on the building floor plan and the node deployment plan list. For example, the node numbers are arranged in order from N01 to N20, representing the various measurement points arranged in the first floor area. The node coordinate positioning information on the drawing can be used to determine whether there is a spatial connectivity relationship between the nodes. The specific standard is that the straight-line distance between the nodes is less than or equal to 2 meters, and there are no obstructions in the middle. This type of judgment needs to be combined with the measurement data. For example, nodes N01 to N05 are arranged in channel A1, and are 1.5 meters apart from each other. According to the on-site deployment diagram and the node coordinate set, the adjacent relationship can be judged in turn, and node pairs such as N01-N02 and N02-N03 can be identified. The node pairs that meet the conditions need to construct a number pair structure, which can be a two-dimensional array, such as [['N01', 'N02'], ['N02', 'N03'], ['N03', 'N04'], ['N04', 'N05']]. During the recognition process, if overlapping numbers or number jumps are found, such as when N03-N05 and N03-N04 appear at the same time, they need to be screened and cleaned up, and the actual physical connection relationship pairs are retained. The node number is obtained by initializing the synchronization log file of the sensor, and the actual position is screened and verified by the index number field. If the sensor number field is missing or repeated, it is not included in the number pair set. The final extracted adjacent node pair data is shown in Table 1: Table 1 Node number extraction example data table

[0021] As shown in Table 1, the continuous node pairs formed by N01 to N05 all meet the 1.5-meter connection distance and no-obstruction conditions, and can be added to the number set as adjacent nodes to generate a node number pair set.

[0022] The environmental data acquisition submodule reads the temperature sampling value and humidity sampling value corresponding to each pair of node numbers in the current cycle based on the node number pair set, extracts the environmental sampling value of each node respectively, and combines them to construct a double set of environmental data sets under the node pair to obtain a double set of environmental sampling value sets; Based on the node number pair set, it is necessary to further collect the temperature and humidity raw data of the corresponding node in the current detection cycle. The specific operation is to call the data cache structure indexed by the node number in the data acquisition system, and read the environmental sampling values ​​corresponding to the node number in sequence. The sampling time is unified based on the period window of 09:00 on August 8, 2024. In this period, the temperature value of node N01 is 24.1℃ and the humidity is 47.8%; the temperature of node N02 is 24.6℃ and the humidity is 49.2%. Then the combination is [24.1, 24.6, 47.8, 49.2], which is the environmental parameter data group of the current period of nodes N01-N02. All sampling values ​​must meet the condition that the data timestamp difference is less than 20 seconds to be considered as data of the same period. Otherwise, the data group will be discarded to avoid cross-period errors, such as N If the timestamp record of one of 03 and N04 is 09:01:02 and the other is 09:02:33, the time difference exceeds the set threshold and is not counted as valid data for that period. After the sampling values ​​are read, they must be stored in a cache table. Its structure is a triple: {node number pair, period number, parameter group array}, for example, {N01-N02, T_2024080809, [24.1, 24.6, 47.8, 49.2]}, where the period number T_2024080809 represents the sampling period of 09:00 on August 8, 2024. Each set of data storage structures must verify the parameter integrity and value range. The temperature must be between 15°C and 35°C, and the humidity must be between 30% and 80%. Data out of range must also be discarded. All structures that pass the verification constitute a set of two sets of environmental sampling values.

[0023] The difference data generation submodule extracts the sequence number, cycle number and environmental data combination information of each pair of nodes based on the two sets of environmental sampling values, unifies the structured organization, binds each node number pair with its corresponding environmental data set, and generates a node difference initialization data set; Based on the two sets of environmental sampling values, the data must be structured and organized into a unified data set. First, each set of data must be deconstructed into field values ​​such as node number pair, period number, node i temperature, node j temperature, node i humidity, and node j humidity, and assigned clear field identifiers. For example, for node pair N01-N02, the four corresponding sampling values ​​in period T_2024080809 are 24.1, 24.6, 47.8, and 49.2. The structure can be reorganized into a sextuple [N01-N02, T_2024080809, 24.1, 24.6, 47.8, 49.2]. , then all six-tuples need to be uniformly included in the structured cache set, and a field index table is constructed. The index field is the node pair and the cycle number field, which is used for subsequent fast retrieval and matching operations. Before data import, field integrity and format uniformity verification must be performed to ensure that all six fields have been assigned values ​​and the value type is correct (floating point number). Periodic archiving processing is performed based on the cycle number, and all six-tuples are classified and archived in units of cycles. Finally, a unified data structure is generated for subsequent modules to perform data difference analysis and processing between nodes. After all structured data are imported, they are uniformly stored in the data pool to form a node difference initialization data set.

[0024] See also Figure 3 , the state difference extraction module includes: The difference record generation submodule initializes the data set based on the node difference value, extracts the current period temperature sampling value and humidity sampling value corresponding to each node number pair, establishes the temperature difference value and humidity difference value of each pair of nodes into a set of difference data, unifies all record formats and classifies them into a cache set, and establishes the node environment difference record; Initialize the data set based on the node difference. First, extract each set of node number pairs and the corresponding period number, and separate the temperature sampling value and humidity sampling value in the current period from the environmental data set. Use the joint index of the node number and the sampling timestamp to locate the corresponding node data. For example, in the period of 09:00 on August 8, 2024, the temperature collected by node N01 is 24.8℃ and the humidity is 46.3%. The temperature collected by node N02 is 24.3℃ and the humidity is 48.4%. The temperature difference of the corresponding node pair N01-N02 in this period is 0.5℃ and the humidity difference is -2.1%. The calculation of the difference is: The method of subtracting node j from node i is used for execution. The direction uniformity is determined by the system's preset node order. The difference structure is unified into a four-field form: {node pair number, cycle number, temperature difference value, humidity difference value}. For example, the node pair above is constructed as {N01-N02, T_2024080809, 0.5, -2.1}. During the recording process, if it is detected that a node sample is missing or the sampling is abnormal and out of range (such as the temperature is not between 10℃ and 40℃, and the humidity is not between 10% and 90%), the data of this pair will be invalidated and the difference record set will be eliminated to ensure data quality. The final difference record item structure is as follows: Table 2 Example table of node environment difference records

[0025] As shown in Table 2, the node difference records of each group in the current cycle have been organized according to a unified structure, which serves as the basic data source for measuring the degree of offset between nodes and establishing node environment difference records.

[0026] The cycle difference comparison submodule uses the node number pair and the current cycle number as the index key based on the node environment difference record to read the temperature difference and humidity difference historical data of the node number pair in the previous three consecutive cycles, and performs difference ratio processing on the temperature difference value of the current cycle and the average of the temperature difference values ​​of the previous three cycles. At the same time, the same operation is performed on the humidity difference value of the current cycle and the average of the humidity difference values ​​of the previous three cycles. The formula is: ; and ; The periodic temperature difference ratio and periodic humidity difference ratio are calculated separately, and the node number pairs and period numbers are uniformly bound to obtain the node difference ratio sequence, where: Represents a node pair In the cycle The temperature difference in degrees Celsius, Indicates the average temperature difference of the node pair in the previous three cycles, in degrees Celsius. Indicates the sample standard deviation of the temperature difference within the corresponding period, in degrees Celsius; 、 、 They are the humidity difference of the current period, the mean humidity difference of the previous three periods, and the standard deviation of the humidity difference, in percentage. 、 They represent the temperature difference ratio and the humidity difference ratio respectively; Call the node environment difference record, use the node pair number as the main index, and the cycle number as the time positioning key to obtain the original data sequence of the temperature difference and humidity difference in the current cycle and the previous three cycles to form a sequence array structure. The data sampling cycle is set to once per hour, and the total number of extracted samples is 4 groups, of which the first three groups are historical cycles. The current group is used to calculate the offset and ratio. For example, the temperature difference history sequence of node pair N01-N02 is [0.4, 0.7, 0.5]℃, and the humidity difference history is [1.8, 2.0, 2.2]%. The temperature difference in the current cycle is 0.5℃, and the humidity difference is 2.1%. The average temperature difference is ℃, the average humidity difference is %; Then calculate the standard deviation, temperature difference standard deviation , standard deviation of wet difference %; Substitute the above calculation results into the normalization formula: ; ; Therefore, the current period temperature difference ratio of node pair N01-N02 is 0.1965, and the humidity difference ratio is 0.5, which is used to characterize the degree of difference between the current state and the history and obtain the node difference ratio sequence.

[0027] The periodic temperature difference ratio and the periodic humidity difference ratio are used to measure the degree of deviation of the temperature difference and humidity difference of a node in the current detection period compared with its historical stable state. The periodic temperature difference ratio reflects the intensity of change in the temperature difference between two adjacent nodes in the current period relative to the average temperature difference of the previous three periods. If the ratio is large, it means that the temperature distribution of the current period is significantly different from the historical state. Similarly, the periodic humidity difference ratio indicates the degree of deviation of the humidity difference in the current period from the historical mean. The higher the ratio, the more unstable the humidity state between the nodes. These two ratios are formed into corresponding indicators by normalizing the difference between the current period and the historical period. They are used to uniformly measure the volatility and change trend of temperature and humidity in the process of time evolution, providing quantitative support for dynamic monitoring and difference analysis of node status.

[0028] The formula's operational logic is based on the concept of normalized deviation. It measures the relative change between the current node state and the historical stable state by standardizing the temperature and humidity differences. The numerator is the difference between the temperature difference (or humidity difference) of the current period and the mean of the differences over the previous three periods, reflecting the degree of deviation of the current value from the historical reference level. The denominator is the sample standard deviation of the historical three-period difference, which measures the fluctuation of historical data. A smaller standard deviation indicates more stable historical data and a more significant impact of the deviation. The entire fractional term is in the form of a standard fraction, whose physical meaning is to reflect whether the degree of anomaly of the current state exceeds the historical fluctuation range. The absolute value operation is added to both sides of the ratio to screen out directional influences and uniformly measure the deviation intensity. Whether the change direction is increasing or decreasing, it is considered a valid deviation. After independently calculating the temperature difference ratio and humidity difference ratio, they are used as two different dimensional indicators for parallel analysis to avoid logical conflicts caused by directly merging indicators with different dimensions. This establishes a standardized difference indicator system for multidimensional state monitoring.

[0029] The difference index extraction submodule extracts the temperature difference ratio and humidity difference ratio record values ​​of each node pair in the current cycle according to the node difference ratio sequence, extracts the maximum value, binds it with the node number pair to form a record structure, and stores it in the indicator sequence set to establish the state difference indicator sequence; According to the node difference ratio sequence, the temperature difference ratio field and humidity difference ratio field under each group of node numbers are aggregated in turn. After sorting by period number, the field values ​​are judged as extreme values. The ratio item with the largest value in each group is selected as the representative state deviation indicator. For example, the period sequence temperature difference ratio of node pair N01-N02 is [0.15, 0.21, 0.1965], with a maximum temperature difference ratio of 0.21. The humidity difference ratio sequence is [0.3, 0.5, 0.48], with a maximum humidity difference ratio of 0.5. The corresponding period identifier is recorded as the time index point where the maximum deviation occurs. A six-field structure {node number pair, period number, maximum temperature difference ratio, maximum humidity difference ratio, temperature difference sequence, humidity difference sequence} is constructed. This structure is used as the input item of the subsequent fluctuation node screening module. All results are summarized by node number pair and entered into a unified data pool to establish a state difference indicator sequence.

[0030] See also Figure 4 , the multiple offset determination module includes: The threshold comparison submodule extracts the temperature difference ratio and humidity difference ratio of the current period from the node difference ratio sequence based on the temperature difference ratio and humidity difference ratio in the node state difference index sequence, using the formula: ; The operation obtains the offset exceeding threshold intensity value, and based on whether the intensity value is greater than zero, the node pairs whose offset exceeds the trend are screened to generate the offset exceeding limit node number set, where: Indicates the node pair within the cycle The temperature difference ratio, It represents the average temperature difference ratio in the historical period, 、 Respectively represent the humidity difference ratio and its mean value within the period and the historical period, is the offset super-threshold intensity value, The response threshold multiple is set to 1.8; Based on the temperature difference ratio and humidity difference ratio of each node pair within the cycle, the temperature difference ratio and humidity difference ratio values ​​of all node pairs in the current cycle are first extracted, and then the historical ratio mean obtained by the sliding window is called as the comparison benchmark. The preset threshold value is 1.8, which is used as the multiple offset judgment benchmark value in building environment monitoring. Then, the ratio ratio difference operation is performed on each group of node pairs, and the multiple obtained by dividing the temperature difference ratio of the current cycle by its corresponding historical mean is calculated respectively, and then the humidity difference ratio of the current cycle is divided by the historical humidity difference mean. Multiples, sum the two and take their absolute value, then deduct the threshold value 1.8 from the result. If the difference result is greater than 0, the node pair is judged to exceed the allowable fluctuation threshold range and is set as the offset exceeding limit node pair. Finally, all node pairs that meet this condition are screened out and stored in the offset node set. In actual operation, suppose the current cycle temperature difference ratio of node pair N01-N02 is 0.65, the corresponding historical temperature difference average is 0.42, the humidity difference ratio is 0.88, and the historical humidity difference average is 0.49, then its temperature difference multiple is , the humidity difference multiple is , and the sum of the two is , take the absolute value and subtract the threshold , because the result is greater than 0, the node pair is marked as an offset node. Similar operations are performed on other node pairs. The execution data is as follows: Table 3 Example data table for multiple offset judgment

[0031] As shown in Table 3, the formula is calculated as follows: Node pair N01-N02: →Overrun; Node pair N02-N03: →Overrun; Node pair N03-N04: →Overrun; The above judgment shows that all node pairs are identified as having fluctuating offsets, and the set of node numbers with offset exceeding the limit is obtained.

[0032] The offset exceeding threshold intensity value is a quantitative indicator used to measure whether the node pair's comprehensive state offset within the current detection cycle exceeds a preset judgment threshold. Its specific significance lies in the following: after normalizing and comparing the temperature difference ratio and humidity difference ratio of the current cycle with their historical cycle averages, the sum of the two multiples is calculated and the absolute value is taken. The result is then deducted from the set threshold multiple threshold. The result is the offset exceeding threshold intensity value. This value reflects the overall degree to which the state change in the current cycle exceeds the historical level in terms of temperature and humidity. If this value is greater than zero, it indicates that the node pair's environmental state offset within the current cycle has exceeded the acceptable normal fluctuation range and falls into the category of abnormal fluctuation. Therefore, this intensity value is not only used to determine whether the offset is exceeded, but also can be used to distinguish the offset levels of different node pairs, thereby constructing a spatial distribution map and dynamic evolution sequence of the offset degree.

[0033] The calculation logic of the formula is to make a standardized judgment by comparing the state deviation intensity of the current cycle with its historical stable state, so as to identify whether the node pair has abnormal changes within the cycle. Specifically, the formula first calculates the temperature difference ratio of the current cycle. Divide by its average value over three historical periods , and similarly the wet difference ratio Divide by its corresponding historical mean The two ratios represent the multiple changes in the temperature and humidity dimensions of the current period compared with the historical state. The two dimensionless ratios are then added to comprehensively consider the overall deviation of the temperature and humidity dimensions. The absolute value operation is used to shield the positive and negative directions and uniformly measure the overall deviation intensity, which is then compared with the preset threshold multiple. (Here is 1.8) to perform difference calculation to determine whether it exceeds the set fluctuation limit. If the final result is a positive value, it is determined that the node pair has a multiple offset in the current period.

[0034] The regional mapping submodule divides the regions according to the number prefix of the node number pair based on the offset overrun node number set, extracts the region identifier carried in the number as the regional mapping index key, clusters all overrun node pairs according to the region number, and obtains the regional offset node index structure; Based on the offset overrun node number set, the prefix number field in each node pair is extracted item by item to identify the monitoring area to which it belongs. The first two digits of the node number naming rule represent the area code. If the node pair is numbered N01-N02, the area number to which it belongs is R01, and if it is numbered N02-N03, it is R02. All node pairs are numbered and grouped according to this rule. When encountering a cross-area node pair such as N01-N04, its mapping records in the two areas R01 and R04 are recorded respectively. Then, the node pairs are aggregated according to the area number to generate a one-to-many mapping data structure from area to node pair. A mapping table is generated with the area number as the primary key index. After the area binding operation is completed, the offset node pair index information under all areas is recorded. For example, the node pairs N01-N02 and N01-N03 are mapped to the R01 area respectively, and the node pair N03-N04 is mapped to the R03 area. Finally, the regional offset node index structure is obtained.

[0035] The node graph generation submodule obtains the node number pair set under each region based on the regional offset node index structure, generates node connection edges based on the regional number as the index, constructs an undirected graph structure based on the order of the node pairs, registers the graph structure with the period as the index number, and generates a node distribution graph with multiple offsets within the period; Call the regional offset node index structure, first extract all the offset node pairs contained in each group of regional numbers, and parse the starting node number and target node number in the node pair number as the two endpoint nodes in the graph, establish an undirected edge connection relationship, and build an edge set structure for each region during the execution process, using edges to represent the connection between nodes with offset behavior, then create a blank graph structure object and use the current cycle number as the graph structure time index key, and classify the extracted node edge set into the graph structure according to the region to form an undirected graph structure. In the process, if the region number is R01, which contains the offset node pairs N01-N02 and N01-N03, then the graph structure should contain three nodes N01, N02, and N03, and two edges N01-N02 and N01-N03. Further, the offset node pairs N03-N04 and N04-N05 are detected in the R03 region, and the edges N03-N04 and N04-N05 are formed in the graph, with nodes N03, N04, and N05. This constitutes the offset connection relationship graph under this region. The sample data is as follows: Table 4 Regional graph node edge set data table

[0036] As shown in Table 4, the offset node graph formed by region R01 in the current period contains three nodes and two edges, and the graph formed by region R03 contains three nodes and two edges. All nodes and edges are archived with the current period number. The above structure is recorded in the graph sequence, and the construction of the graph in the current period is completed. Finally, the distribution graph of multiple offset nodes within the period is obtained.

[0037] See also Figure 5 , the fluctuation priority annotation module includes: The periodic data extraction submodule extracts the original temperature and humidity data uploaded by each node in the current period and the previous two periods based on the node number in the multiple offset node distribution map within the period, classifies and organizes the data by node number, generates a temperature and humidity data sequence indexed by period according to the node number, and establishes a three-period temperature and humidity data structure; Based on the node number in the multiple offset node distribution map within the cycle, first perform an index extraction operation on all node numbers involved in the current cycle, then access the original temperature and humidity records uploaded by each node in the current cycle, the previous cycle, and the previous two cycles, and perform a data matching process to confirm the data attribution time. The time is distinguished according to the cycle number, and at the same time, ensure that the node number has unique identification capabilities within the three cycles. Then, use the cycle number as the index label to perform period mapping on the temperature and humidity records of each node, sort them by the upload time within the cycle, select the record with the most stable sampling frequency in each cycle as the representative value, and remove missing items or abnormal outliers. For example, a node number N01 uploaded a total of 12 records in the current cycle, which were collected at different hours. 10 and 9 data were uploaded in the previous cycle and the previous two cycles respectively. After screening out the abnormal values, 10, 9, and 8 valid records were retained in each cycle. Then, the average temperature and average humidity of each record in the cycle were calculated according to the cycle to form a periodic time series data matrix of temperature and humidity. Then, the mean data of the three cycles were integrated with the node number as the primary key to generate the following structure: Table 5 Three-cycle temperature and humidity average data table

[0038] As shown in Table 5, for node N01, the three-cycle mean temperature is 25.4°C, 24.8°C, and 25.0°C, and the mean humidity is 56.2%, 52.8%, and 53.5%. Each row in the table contains three-cycle temperature and humidity data records for a node number. The temperature and humidity values ​​are derived from the original data uploaded by the node, which are obtained through period grouping and record cleaning to obtain the three-cycle temperature and humidity data structure.

[0039] The threshold judgment submodule reads the temperature and humidity data corresponding to the current cycle on a node-by-node basis based on the three-cycle temperature and humidity data structure, compares the same data from the previous two cycles, and performs a difference magnitude confirmation operation on each type of data. It then sets the standard value for temperature and humidity fluctuation judgment to determine whether it exceeds the perceptible range, identifies and archives the node priority status, and obtains the node fluctuation status label set. According to the periodic temperature and humidity data structure, the difference calculation is performed on the current cycle temperature value of each node and its temperature values ​​of the previous cycle and the previous two cycles to identify its inter-cycle change trend in the temperature dimension. The processing flow of the humidity dimension is the same. The judgment standard is based on the temperature and humidity change perception threshold proposed in the thermal environment measurement standard, that is, the temperature threshold is ±0.5℃, and the humidity threshold is ±3%RH. If the current cycle value of any dimension exceeds the deviation range between the threshold and the historical cycle mean, the node is judged to have an obvious fluctuation trend. The judgment process does not involve accumulation or weighted processing, but only relies on the simple comparison difference of the two cycle means. For example, the current cycle temperature of node N01 in Table 5 is The temperature is 25.4°C, the average of the previous two cycles is (24.8+25.0) / 2=24.9°C, and the temperature deviation is 0.5°C, which is on the edge of the perceptible critical point. The humidity in the current cycle is 56.2%, and the average of the previous two cycles is (52.8+53.5) / 2=53.15%. The humidity deviation is 3.05%, exceeding the set threshold. Therefore, node N01 is determined to be a fluctuating node. If the temperature of node N03 in the current cycle is 24.7°C, the historical average is 24.85°C, the deviation is 0.15°C, and the humidity deviation is also within 1%, then N03 is a stable node. After all nodes are compared, status labels are assigned according to node numbers to finally generate the node fluctuation status label set.

[0040] The priority identification generation submodule extracts the node numbers and status identification contents of the nodes marked as fluctuating in the current cycle based on the node fluctuation state label set, establishes a time index based on the cycle number, outputs the status result information of each node in a structured manner, maps and integrates the node numbers and priority identifications, and establishes a node-level temperature and humidity fluctuation priority identification set; For all node numbers contained in the node fluctuation status label set, an identification structure is constructed and the index is set according to the cycle number. The node numbers that are judged to have temperature and humidity fluctuation status in the current cycle are extracted. The nodes of this type are bound to their corresponding label information and archived. The labels are divided into two categories: high priority and normal priority. No specific numerical calculation is involved. The labels are directly inherited according to the aforementioned fluctuation judgment criteria to generate a cycle-node number-priority label triple structure, and the node identification set is stored in a hash structure for subsequent calls. During the structure construction process, if nodes N01 and N02 are marked as high priority, they are recorded in the set as N01: high priority, N02: high priority. Conversely, if N03 is marked as normal priority, it is recorded as N03: normal priority. After the sorting is completed, a node-level temperature and humidity fluctuation priority identification set is established.

[0041] See also Figure 6 , the data processing scheduling module includes: The priority pool division submodule is based on the priority label information of all nodes in the node-level temperature and humidity fluctuation priority identification set. It writes the data units corresponding to the high-priority node numbers into the priority scheduling processing pool, and writes the data units corresponding to the normal priority node numbers into the normal scheduling processing pool. It sorts the nodes in ascending order according to the timestamps carried by the node numbers, eliminates data entries with time conflicts, and caches the data sets of each priority type in a queue structure to generate a priority scheduling data set. Based on the classified high priority and normal priority label fields in the node-level temperature and humidity fluctuation priority identification set, first extract all node numbers and divide the nodes into two groups according to their label fields. Then obtain the data unit corresponding to each node number, where the data unit must contain the cycle number, timestamp, temperature and humidity value and label identification field. After the acquisition is completed, the high priority data unit is written into the high priority queue cache structure, which must meet the system setting of response delay not exceeding 100 milliseconds. The normal priority data unit is written into the regular scheduling queue to meet the response limit within 500 milliseconds. The write operation is based on the time of each data unit. The timestamp field is sorted in ascending order to eliminate invalid or duplicate records. During this process, data with an upload interval of less than 10 milliseconds must be considered as duplicate records and merged. For example, the upload record timestamps of node N01 in the two priority queues are 14:00:01.004 and 14:00:01.006 respectively. They are considered to be records within the same period, and the former is selected to be retained. After all data is processed in the above manner, two complete scheduling pools are formed. In addition to the timestamp, the scheduling pool structure also needs to record the node number and data content, and append a priority tag field for subsequent processing reference. Finally, the following structure is established: Table 6 Priority scheduling pool sample structure

[0042] As shown in Table 6, priority labels and time sequences have formed a clear classification. On this basis, they are uniformly encapsulated into two structure sets: priority scheduling processing pool and common scheduling processing pool, and the priority scheduling data set is obtained.

[0043] The time queue management submodule is based on the priority scheduling data set and sets two scheduling processing channels according to the scheduling priority type. In each scheduling cycle, the data unit at the head end of the queue is dequeued. Each processing records the current system timestamp and uses the time as the scheduling time point of the data unit. The dequeuing sequence number is recorded and the records are organized with the node number as the primary key to obtain the data processing scheduling sequence record table. The data of each node in the priority scheduling data set is called and enters the two scheduling channels of high priority queue and normal priority queue respectively. The processing cycle of high priority queue is set to 20 milliseconds, and the processing cycle of normal priority queue is set to 100 milliseconds. All data units are dequeued in ascending order of timestamps. The dequeuing order is the task execution sequence number. The processing cycle unit is set according to the system clock frequency set by the task execution platform. At the beginning of scheduling, each dequeuing operation records the current system time as the scheduling time point, and marks the type of processing resources used by the task. For example, the dequeuing time of task N01 in the high priority queue is 14:00:01.020 , the processing resource type is edge node A1, the scheduling sequence number is 1, and the node number N01, time point 14:00:01.020, resource type A1, and scheduling sequence number 1 need to be bound and recorded in the scheduling record table. After the processing is completed, the record structure is immediately written into the memory structure cache. All records are dynamically supplemented in the order of scheduling time. The structure fields include task number, time point, resource type and scheduling sequence number. There is no data calculation, only the fields are archived and sequentially bound. Priority judgment is no longer performed in each round of dequeueing, and only the sequential flow processing process is executed. All records are finally collected and a data processing scheduling sequence record table is generated.

[0044] The scheduling record generation submodule arranges all fields in ascending order according to the node number, scheduling time point, processing resource type, and scheduling sequence number information of each record in the data processing scheduling sequence record table. It uses the cycle number and processing time point to jointly generate a unique primary key index to distinguish task scheduling behaviors at different time points in the same cycle, and establish a distributed indoor temperature and humidity data processing record table. According to the task field data written in the data processing scheduling sequence record table, all node numbers are used as the index primary key, and the corresponding scheduling time point, processing resource type and scheduling sequence number fields are extracted one by one, and written uniformly into the standard record structure template. In the structure template, the cycle number and scheduling time are combined to generate a unique index identifier. An example of a field combination is "20240809_140001020". This structure is used to prevent duplicate writing of scheduling records for the same period. After the fields are unified, a bidirectional index mapping structure is established between the node number field and the resource type field, that is, one node number can correspond to multiple processing resource type records, and multiple resource types can also reversely locate their corresponding node numbers. Finally, the above information is written into the table structure and arranged in index order by scheduling time point. An example of a unified output structure is as follows: Table 7 Distributed processing record sample table

[0045] As shown in Table 7, the node numbers and their scheduling behavior records have been structured and encapsulated and written uniformly into the system master record file. The record content has periodic index and scheduling sequence number information, and finally a distributed indoor temperature and humidity data processing record table is established.

[0046] A distributed indoor temperature and humidity data processing method comprises the following steps: S1: Obtain the node number of the indoor area, collect the temperature sampling value and humidity sampling value of the current period respectively, establish a one-to-one corresponding data set based on the node number pair, and bind the node number pair with the corresponding temperature and humidity data to generate a node difference initialization data set; S2: Based on the node difference initialization data set, the temperature and humidity sampling values ​​of each node pair are calculated, and the difference is sequentially bound to the node number pair to form a difference recording unit. The difference recording unit is then subtracted from the average difference of the node's three historical cycles, and the maximum value of the temperature difference ratio and the humidity difference ratio is extracted to obtain the state difference indicator sequence between the nodes. S3: Based on the inter-node state difference indicator sequence, all node pairs whose maximum ratio values ​​exceed the response threshold multiple are screened, and a corresponding area number mapping is established to obtain a distribution map of nodes with multiple offsets within the period; S4: Based on the node distribution map of multiple offsets within the cycle, the average temperature and humidity data of each node in the current cycle and the previous two cycles is calculated. If it exceeds the fluctuation sensitivity threshold, it is marked as a high-priority data unit; otherwise, it is marked as a normal priority unit. The node-level temperature and humidity fluctuation priority identification set is generated; S5: According to the node-level temperature and humidity fluctuation priority identification set, insert them into the priority scheduling processing pool and the ordinary scheduling processing pool according to the priority type, dequeue them in chronological order and perform data processing in sequence to establish a distributed indoor temperature and humidity data processing record table.

[0047] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A distributed indoor temperature and humidity data processing system, characterized in that: The system comprises: The node data initialization module obtains the node number of the indoor area, collects the temperature sampling value and humidity sampling value of the current period, establishes a one-to-one corresponding data set based on the node number pair, and binds the node number pair with the corresponding temperature and humidity data to generate a node difference initialization data set; The state difference extraction module calculates the difference of the temperature and humidity sampling values ​​of each node pair based on the node difference initialization data set, binds them to the node number pairs in sequence to form a difference recording unit, performs a difference operation on the difference recording unit and the average difference of the node's three historical cycles, extracts the maximum value of the temperature difference ratio and the humidity difference ratio, and obtains the state difference indicator sequence between the nodes; The multiple offset determination module selects all node pair numbers whose maximum ratio values ​​exceed the response threshold multiple according to the inter-node state difference indicator sequence, and establishes a corresponding area number mapping to obtain a multiple offset node distribution map within the period; The fluctuation priority labeling module calculates the average temperature and humidity data of each node in the current cycle and the previous two cycles based on the multiple offset node distribution map within the cycle. If it exceeds the fluctuation sensitivity threshold, it is marked as a high-priority data unit, otherwise it is marked as a normal priority unit, and a node-level temperature and humidity fluctuation priority identification set is generated.

2. The distributed indoor temperature and humidity data processing system according to claim 1, characterized in that: The node difference initialization data set includes a node number pair mapping relationship, a temperature and humidity sampling data set within a period, and a data set binding structure. The state difference indicator sequence includes a temperature difference ratio sequence between nodes, a humidity difference ratio sequence between nodes, and a ratio difference maximum value record item. The multiple offset node distribution map within the period includes node pair numbers exceeding the threshold, a node area number mapping relationship, and an abnormal distribution map structure within the period. The node-level temperature and humidity fluctuation priority identification set includes the node temperature and humidity mean, the comparison result with the fluctuation sensitivity threshold, and the priority identification label.

3. The distributed indoor temperature and humidity data processing system according to claim 1, characterized in that: The node data initialization module includes: The node number extraction submodule obtains all adjacent node numbers in the indoor area node number table, identifies each pair of node numbers with a direct connection relationship, and establishes a number pair set to generate a node number pair set; The environmental data acquisition submodule reads the temperature sampling value and humidity sampling value corresponding to each pair of node numbers in the current cycle based on the node number pair set, extracts the environmental sampling value of each node respectively, and combines and constructs two sets of environmental data sets under the node pair to obtain two sets of environmental sampling value sets; The difference data generation submodule extracts the sequence number, cycle number and environmental data combination information of each pair of nodes based on the two sets of environmental sampling values, unifies the structured organization, binds each node number pair with its corresponding environmental data set, and generates a node difference initialization data set.

4. The distributed indoor temperature and humidity data processing system according to claim 1, characterized in that: The state difference extraction module includes: The difference record generation submodule is based on the node difference initialization data set, extracts the current period temperature sampling value and humidity sampling value corresponding to each group of node number pairs, establishes the temperature difference value and humidity difference value of each pair of nodes as a set of difference data, unifies all record formats and classifies them into a cache set, and establishes node environment difference records; The period difference comparison submodule reads the temperature difference and humidity difference historical data of the node number pair in the previous three consecutive periods according to the node environment difference record, using the node number pair and the current period number as the index key, and performs difference ratio processing on the temperature difference value of the current period and the average of the temperature difference values ​​of the previous three periods in turn. At the same time, the same operation is performed on the humidity difference value of the current period and the average of the humidity difference values ​​of the previous three periods, and respectively calculates the period temperature difference ratio and the period humidity difference ratio, and uniformly binds the node number pair and the period number to obtain a node difference ratio sequence; The difference index extraction submodule extracts the temperature difference ratio and humidity difference ratio record values ​​of each group of node pairs in the current cycle according to the node difference ratio sequence, extracts the maximum value, binds it with the node number pair to form a record structure, and stores it in the indicator sequence set to establish a state difference indicator sequence.

5. The distributed indoor temperature and humidity data processing system according to claim 1, characterized in that: The multiple shift determination module includes: The threshold comparison submodule extracts the temperature difference ratio value and the humidity difference ratio value of the current period from the node difference ratio sequence based on the temperature difference ratio and the humidity difference ratio in the inter-node state difference indicator sequence, calculates and obtains the offset exceeding threshold intensity value, and screens the node pairs whose offset exceeds the trend according to whether the intensity value is greater than zero, and generates an offset exceeding limit node number set; The regional mapping submodule divides the regions to which the node numbers belong based on the offset overrun node number set and the number prefixes of the node numbers, extracts the region identifiers carried in the numbers as the region mapping index keys, clusters all overrun node pairs based on the region numbers, and obtains the regional offset node index structure; The node map generation submodule obtains the node number pair set under each area according to the area offset node index structure, generates node connection edges according to the area number as the index, constructs an undirected graph structure according to the order of the node pairs, registers the graph structure with the period as the index number, and generates a node distribution map with multiple offsets within the period.

6. The distributed indoor temperature and humidity data processing system according to claim 1, characterized in that: The fluctuation priority marking module includes: The periodic data extraction submodule extracts the original temperature and humidity data uploaded by each node in the current period and the previous two periods based on the node number in the multiple offset node distribution map within the period, classifies and organizes the data by node number, generates a temperature and humidity data sequence indexed by period according to the node number, and establishes a three-period temperature and humidity data structure; The threshold judgment submodule reads the temperature and humidity data corresponding to the current cycle on a node-by-node basis based on the three-cycle temperature and humidity data structure, compares the same data from the previous two cycles, performs a difference magnitude confirmation operation on each type of data, sets standard values ​​for temperature and humidity fluctuation judgment, determines whether they exceed the perceptible range, identifies and archives the node priority status, and obtains a node fluctuation status label set; The priority identification generation submodule extracts the node numbers and status identification contents marked as fluctuation states in the current cycle according to the node fluctuation state label set, establishes a time index in combination with the cycle number, outputs the status result information of each node in a structured manner, maps and integrates the node numbers and priority identifications, and establishes a node-level temperature and humidity fluctuation priority identification set.

7. The distributed indoor temperature and humidity data processing system according to claim 1, characterized in that: The system further comprises: The data processing scheduling module inserts the node-level temperature and humidity fluctuation priority identification set into the priority scheduling processing pool and the common scheduling processing pool according to the priority type, dequeues the data in chronological order and performs data processing in sequence, and establishes a distributed indoor temperature and humidity data processing record table; The distributed indoor temperature and humidity data processing record table includes node number record items, scheduling time point information, processing resource type identifier, and task execution order record items.

8. The distributed indoor temperature and humidity data processing system according to claim 7, characterized in that: The data processing scheduling module includes: The priority pool division submodule writes the data units corresponding to the high-priority node numbers into the priority scheduling processing pool and the data units corresponding to the ordinary priority node numbers into the ordinary scheduling processing pool based on the priority tag information of all nodes in the node-level temperature and humidity fluctuation priority identification set. The data units are sorted in ascending order according to the timestamps carried by the node numbers, and data entries with time conflicts are eliminated. The data sets of each priority type are cached separately in a queue structure to generate a priority scheduling data set; The time queue management submodule sets two scheduling processing channels based on the priority scheduling data set and the scheduling priority type. It dequeues the data unit at the head end of the queue in each scheduling cycle, records the current system timestamp for each processing and uses the time as the scheduling time point of the data unit, records the dequeue sequence number, organizes the records with the node number as the primary key, and obtains the data processing scheduling sequence record table; The scheduling record generation submodule arranges all fields in ascending order according to the node number, scheduling time point, processing resource type and scheduling sequence number information of each record in the data processing scheduling sequence record table, and uses the cycle number and processing time point to jointly generate a unique primary key index to distinguish the task scheduling behaviors at different time points in the same cycle, and establish a distributed indoor temperature and humidity data processing record table.

9. A distributed indoor temperature and humidity data processing method, characterized in that: The method is used to implement the distributed indoor temperature and humidity data processing system according to any one of claims 1 to 8, comprising the following steps: S1: Obtain the node number of the indoor area, collect the temperature sampling value and humidity sampling value of the current period respectively, establish a one-to-one corresponding data set based on the node number pair, and bind the node number pair with the corresponding temperature and humidity data to generate a node difference initialization data set; S2: Based on the node difference initialization data set, the temperature and humidity sampling values ​​of each node pair are calculated, and the difference is sequentially bound to the node number pair to form a difference recording unit. The difference recording unit is then subjected to a difference operation with the average difference of the node's three historical cycles. The maximum values ​​of the temperature difference ratio and the humidity difference ratio are extracted to obtain a sequence of state difference indicators between nodes. S3: Based on the inter-node state difference indicator sequence, filter all node pair numbers whose maximum ratio value exceeds the response threshold multiple, and establish a corresponding area number mapping to obtain a node distribution map of the multiple offset within the period; S4: Calculate the average temperature and humidity data of each node in the current cycle and the previous two cycles based on the multiple offset node distribution map within the cycle. If the average temperature and humidity data exceeds the fluctuation sensitivity threshold, mark it as a high-priority data unit; otherwise, mark it as a normal-priority unit, and generate a node-level temperature and humidity fluctuation priority identification set. S5: According to the node-level temperature and humidity fluctuation priority identification set, insert them into the priority scheduling processing pool and the ordinary scheduling processing pool according to the priority type, dequeue them in chronological order and perform data processing in sequence to establish a distributed indoor temperature and humidity data processing record table.

Images