Multi-source data acquisition monitoring robot based on Internet of Things

Abstract

The invention relates to the technical field of remote monitoring, in particular to a multi-source data acquisition monitoring robot based on the Internet of Things, which comprises a morpheme trigger recognition module, a task compression judgment module, a resource tilt execution module, a difference channel analysis module and a path state injection module. According to the method, dynamic switching of voice interaction states is realized through morpheme response distance and silence zone recognition, priority allocation of task resources is completed in combination with a task delay labeling mechanism, an auxiliary path is released through linkage of node states and communication port fields, resource scheduling flexibility is improved, fluctuation trend and structural offset are fused in abnormal recognition, and abnormal recognition accuracy is improved. And in path control, label field double write-in and state register linkage is adopted, a closed-loop mapping relation between a task path and a control state is constructed, and the accuracy and synchronization consistency of path regulation and control are improved.

Description

Technical Field

[0001] This invention relates to the field of remote monitoring technology, and in particular to a multi-source data acquisition and monitoring robot based on the Internet of Things. Background Technology

[0002] The field of remote monitoring technology encompasses methods for collecting physiological parameters, managing health data, and assessing the status of monitored subjects through information and communication networks. The core of this technology lies in achieving real-time monitoring and data transmission of human health status or vital signs in a remote environment. Its content involves multiple aspects, including sensor signal acquisition, physiological data encoding, encrypted data transmission, remote storage, and doctor-end parsing systems. The development of this technology relies on multiple fundamental supports, such as information technology, sensing devices, network communication protocols, and embedded system integration. It is widely used in scenarios such as home care, elderly care management, chronic disease follow-up, outpatient rehabilitation, and disaster emergency rescue. It exhibits interdisciplinary integration characteristics and reflects a development trend evolving from single-point monitoring to multi-source integration, from passive recording to proactive push notifications, and from local terminals to Internet of Things (IoT) structures.

[0003] Among them, the IoT-based multi-source data acquisition and monitoring robot refers to an intelligent mobile platform that integrates multiple physiological signal acquisition devices and environmental sensing components, and achieves data aggregation and remote reporting through an IoT communication structure. This patent addresses the problems of single data acquisition dimensions and limited monitoring range in health monitoring. It integrates multiple sensing methods such as heart rate detection, body temperature sensing, blood oxygen monitoring, indoor location positioning, and air quality acquisition, uses wireless communication protocols for data transmission, and manages the synchronization and caching of each data channel through embedded control logic. The system also includes data attribution binding based on patient identification and data acquisition scheduling logic based on time series settings to meet the continuous monitoring needs of specific populations in specific environments. This patent completes the collaborative work of the sensing layer, network layer, and data layer in remote monitoring tasks through a module-built-in scheduling instruction set and remote communication process control logic.

[0004] Existing technologies typically rely on fixed monitoring patterns and simple data processing strategies, making them ill-suited to adapting to complex and changing monitoring environments. Particularly in multi-parameter monitoring scenarios, dependence on a single data source and the lack of effective data fusion mechanisms often lead to limitations and errors in monitoring results. For example, relying solely on heart rate or blood oxygen monitoring may fail to comprehensively assess a patient's health status, overlooking changes in other key physiological parameters, such as body temperature fluctuations, thus affecting the timeliness and accuracy of diagnosis and treatment. Furthermore, existing technologies often lack effective data analysis methods when handling channel anomalies, failing to combine time series and frequency changes in data for comprehensive judgment, easily leading to missed or misjudged abnormal states, impacting the effectiveness and safety of monitoring. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a multi-source data acquisition and monitoring robot based on the Internet of Things.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A multi-source data acquisition and monitoring robot based on the Internet of Things includes: The morpheme trigger recognition module is used to obtain the operation record of the voice component in the respiratory monitoring task, extract the keyword morpheme position and timestamp, analyze the keyword response distance and silent zone distribution, and switch the channel state to the monitoring mode after identifying abnormal segments to obtain the voice channel monitoring mode registration record. The task compression and judgment module is used to extract the scheduling tag and running trajectory of the respiratory monitoring task based on the voice channel monitoring mode registration record, analyze the task delay status and attach the tag to be guaranteed, and obtain the labeled respiratory monitoring task identifier. The resource tilt execution module is used to extract the auxiliary path status and node distribution based on the labeled respiratory detection task identifier, release transferable resources and update the belonging path expression to obtain the adjusted resource channel distribution table. The difference channel analysis module is used to read path collection data and analyze fluctuation differences based on the adjusted resource channel distribution table, mark abnormal channel numbers, and generate an abnormal path identification list. The path status injection module is used to locate the abnormal task interface and write control tags based on the abnormal path identifier list, adjust the priority and update the status register to obtain the integrated result of the operating status after the path control of the monitoring robot.

[0007] As a further aspect of the present invention, the voice channel monitoring mode registration record includes sparse segment identifiers, non-response time period ranges, channel permission switching status, and monitoring mode write timestamps. The labeled breathing detection task identifiers include mounting status category, delay marker type, scheduling trajectory positioning results, and stage interval labels. The adjusted resource channel distribution table includes auxiliary path release markers, home path update information, node port matching results, and resource distribution structure numbers. The abnormal path identifier list includes abnormal channel numbers, fluctuation segment distribution characteristics, structure offset type, and mutation behavior label values. The integrated result of the monitoring robot's operational status after path adjustment includes path prompt identifier records, task monitoring area interference labels, channel priority label status, and status register closed-loop structure.

[0008] As a further aspect of the present invention, the morpheme triggering recognition module includes: The semantic tag extraction submodule is used to obtain the voice interaction records of the voice receiving component, semantic extractor and voice channel permission control node during the continuous operation cycle of the respiratory monitoring task, extract the timestamps and position order of the tagged morphemes in the sentences, and obtain the morpheme time sorting list. The morpheme rhythm recognition submodule is used to extract the time interval between adjacent morphemes based on the morpheme time sorting list, identify the difference features between the response rhythm and sentence structure between morphemes, and filter out rhythmically disjointed segments by combining the coverage of silent segments in the semantic segment, and generate a set of response discontinuous segments. The channel state switching submodule is used to call the set of discontinuous response segments, and the voice channel permission control node performs permission change operation to set the corresponding voice channel state to listening, and write the change record into the registration area of ​​the main control logic module to obtain the voice channel listening mode registration record.

[0009] As a further aspect of the present invention, the task compression determination module includes: The task trajectory recognition submodule is used to register records based on the voice channel monitoring mode, retrieve the scheduling tag and running trajectory information of the respiratory monitoring task, extract the task start time field and mounting status field, organize the sequential mapping relationship between task cycle segments and stage tags, and generate task stage matching structure values. The progress interruption analysis submodule is used to call the task stage matching structure value, extract the stage dwell segment and interruption mark field in the tag sequence, calculate the ratio between the total time length covered by the stage dwell segment and the total number of segments, and obtain the tag interruption delay interval group value. The abnormal state labeling submodule is used to compare the delay segment with the state start and end nodes in the task instruction trigger sequence based on the value of the label interruption delay interval group, extract the state consistent segment, mark the corresponding segment as the state to be guaranteed, and write it into the control bus identification area to obtain the labeled respiratory detection task identifier.

[0010] As a further aspect of the present invention, the formula for calculating the ratio between the total time length covered by the stage dwell section and the total number of segments is as follows: ; in, The ratio of the total time length covered by the representative stage's dwell segment to the total number of segments. Representative stage The number of stops Representing the The end time of each stop segment, Representing the The start time of each stop segment, Representing the The stage label amplitude value corresponding to each dwell segment Representative stage The average label amplitude across all dwelling segments Representing the The disturbance offset of each dwelling segment Representative stage The average disturbance offset of all dwelling segments Represents the amplitude deviation adjustment coefficient. This represents the disturbance fluctuation adjustment coefficient.

[0011] As a further aspect of the present invention, the resource-skewed execution module includes: The resource path extraction submodule is used to call the task-corresponding path information in the resource channel configuration table based on the labeled respiratory detection task identifier, extract the communication port number field and node resource address segment, combine them to generate the resource mapping structure of the path, and store the structure field and channel number in the buffer to generate a task path resource combination list. The node allocation ratio judgment submodule is used to call the task path resource combination list, extract the paths with the status field as standby in the auxiliary monitoring tasks, obtain the corresponding communication node usage field and path ownership number, calculate the node usage rate range based on the resource allocation ratio warning threshold, filter the set of paths whose node usage rate falls into the range of the beginning segment of the range, and generate a list of transferable node occupancy. The path ownership update submodule is used to mark the ownership change of the path number field based on the list of occupancy of transferable nodes, update the original node ownership path field to the current respiratory monitoring task path, write it into the distribution structure segment of the resource channel configuration table and register the synchronization status, so as to obtain the adjusted resource channel distribution table.

[0012] As a further aspect of the present invention, the formula for calculating the node utilization rate interval is as follows: ; in, Representing the The node utilization range in the path, Representing the The first path The current utilization rate of each communication node. Representing the The first path Resource allocation warning thresholds for each communication node Representing the Resource usage adjustment factor in the path, Representing the The first path The node priority weight of each communication node Representing the The average utilization rate of all communication nodes in the path. Representing the The communication interference adjustment ratio in each path Representing the The total number of communication nodes included in the path. This is the loop variable number for the communication nodes within the path.

[0013] As a further aspect of the present invention, the difference channel analysis module includes: The channel data extraction submodule is used to read the continuous acquisition records at the end of the body temperature channel, oxygen saturation channel, and motion monitoring channel based on the adjusted resource channel distribution table, extract the time series and numerical series of the corresponding acquisition segments in the channel, divide the continuous segments into signal segment sets and archive them, and generate a channel acquisition segment sequence set. The fluctuation segment calculation submodule is used to call the segment sequence set acquired by the channel, analyze the periodicity and amplitude variation characteristics of the numerical changes in the segment, combine the response rhythm reference value set by the channel, calculate the offset ratio of the frequency change amplitude in the continuous segment, and filter the segments with prominent offset rates to obtain the fluctuation abnormal frequency interval value. The abnormal path marking submodule is used to identify the channel number of the segment that is inconsistent with the stable response characteristics based on the fluctuation abnormal frequency interval value, write it into the abnormal path list structure as the interference channel identifier number, and synchronously record it to the interference path index area to obtain the abnormal path identifier list.

[0014] As a further aspect of the present invention, the formula for calculating the offset ratio of the frequency change amplitude in a continuous segment is as follows: ; in, Representing the The offset ratio of the frequency variation amplitude in a continuous segment Representing the The number of paragraphs within a segment Representing the In the segment The magnitude of frequency variation in paragraphs. Representing the The frequency response rhythm of the channel settings in the clip references a baseline value. Representing the Fragment No. The frequency channel response weighting factor of the paragraph. Representing the The average of the frequency variation values ​​of all paragraphs in the segment. The constant offset stabilization factor is used to prevent the denominator from being zero.

[0015] As a further aspect of the present invention, the path state injection module includes: The path number positioning submodule is used to call the path number index table on the main control node based on the abnormal path identifier list, extract the running status field corresponding to the abnormal channel number, retrieve the task interface segment and channel control number field in the status field, and generate an abnormal channel task interface index set. The tag field insertion submodule is used to call the abnormal channel task interface index set, extract the status overview field and monitoring tag field within the task interface segment, insert a path prompt identifier into the status overview field, insert an interference tag into the monitoring field, register field changes, and generate a path control tag writing record. The status register synchronization submodule is used to write records based on the path control tag, extract the channel number corresponding to the inserted field, perform priority field update and status field synchronization operations in the master control node, write the status content into the robot status register, and obtain the integrated result of the operating status after the monitoring robot path adjustment.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, dynamic switching of voice interaction state is achieved through morpheme response distance and silent zone recognition. Task resource priority allocation is completed by combining task delay labeling mechanism. Auxiliary path is released by linking node state and communication port field to improve resource scheduling flexibility. Fluctuation trend and structural offset are integrated in anomaly recognition to enhance the accuracy of anomaly judgment. In path control, dual writing of label field and linkage of state register are adopted to build a closed-loop mapping relationship between task path and control state, thereby improving the accuracy and synchronization consistency of path control. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Please see Figure 1 A multi-source data acquisition and monitoring robot based on the Internet of Things, comprising: The morpheme trigger recognition module is used to acquire voice interaction records during the continuous operation cycle of the voice receiving component, semantic extractor, and voice channel permission control node in the respiratory monitoring task. It extracts the location and corresponding timestamp of the marked key word morphemes in the sentences, analyzes the response distance between keywords, marks the response distance exceeding the preset threshold as morpheme sparse segments, analyzes the range of the continuous coverage of the semantic activity area during the silent time, identifies morpheme sparse segments and non-response time periods and hands them over to the permission control node to perform channel switching, writes the monitoring mode to the main control logic module, and obtains the voice channel monitoring mode registration record. The task compression and judgment module is used to register records based on the voice channel monitoring mode, retrieve the scheduling tags and task running trajectory of the respiratory monitoring task, identify the start time marked in the task record and the current task mounting status, analyze the correspondence between the running tag interruption duration and the corresponding task progress abnormal tag type, analyze the stability of the current state by comparing the trigger interval trend between the task stage identifier and the task instruction sequence, mount the identification result as a tag to be guaranteed and transmit it to the control bus to obtain the labeled respiratory monitoring task identifier. The resource tilt execution module is used to call the path information of the respiratory detection task in the resource channel configuration table based on the labeled respiratory detection task identifier, match the number of communication ports occupied and the node resource address, extract the path status of auxiliary monitoring tasks, identify the path with the status field as standby and the communication node occupation not exceeding the resource allocation warning threshold, mark it as a release object and update the node's belonging path label, summarize and write it into the new distribution structure to obtain the adjusted resource channel distribution table. The differential channel analysis module is used to read the end-collection records of the body temperature channel, oxygen saturation channel, and motion monitoring channel based on the adjusted resource channel distribution table. It compares the continuous data segments of each type of channel, identifies abnormal data segments that do not conform to the stable acquisition mode, analyzes the distribution characteristics of the fluctuation segment within the response period, and classifies the frequency change pattern as a mutation behavior when there is a structural deviation from the stable characteristic curve. The channel number with abnormal state performance is written into the interference list structure to generate an abnormal path identification list. The path status injection module is used to extract the running status field of the channel by calling the corresponding path number index table on the main control node based on the abnormal path identifier list, locate the task interface of the abnormal channel, perform running-level status replacement, write the path prompt identifier into the status overview area, write the interference label into the task monitoring area and adjust the channel priority label synchronously, and perform state closed-loop reconstruction in the robot execution status register to obtain the integrated result of the running status after the path control of the monitoring robot.

[0021] The voice channel monitoring mode registration record includes sparse segment identifiers, non-response time period ranges, channel permission switching status, and monitoring mode write timestamps. The labeled breathing detection task identifiers include mounting status category, delay marker type, scheduling trajectory positioning results, and stage interval labels. The adjusted resource channel distribution table includes auxiliary path release markers, home path update information, node port matching results, and resource distribution structure numbers. The abnormal path identifier list includes abnormal channel numbers, fluctuation segment distribution characteristics, structural offset types, and mutation behavior annotation values. The integrated results of the monitoring robot's operational status after path adjustment include path prompt identifier records, task monitoring area interference labels, channel priority label status, and status register closed-loop structure.

[0022] Please see Figure 1 and Figure 2 The morpheme trigger recognition module includes: The semantic tag extraction submodule acquires the voice interaction records of the voice receiving component, semantic extractor, and voice channel permission control node during the continuous operation cycle of the respiratory monitoring task, extracts the timestamps and position order of the tagged morphemes in the sentences, and obtains a morpheme time sorting list. First, the task assignment of each component is confirmed. The voice receiving component collects the patient's voice signal stream and performs real-time transcription using the semantic extractor's internal dictionary structure. During transcription, the transcription time point and sentence sequence number must be recorded synchronously. For example, when a patient calls out "chest tightness, heavy breathing," the system marks "chest tightness" as the trigger keyword at point T1, followed by "heavy breathing" as the keyword at point T2. The voice channel permission control node then marks this dialogue trigger event chain number as event path R07, and the corresponding voice channel number is set to VC003. This process is repeated weekly. The continuous segments are distinguished by seconds. If each recording cycle is 30 seconds, the total sequence of morphemes transcribed within that cycle is arranged in ascending order of time to form a sorting unit S. The position identifiers P1 and P2 of T1 and T2, and the timestamp identifiers Ts1 and Ts2 are extracted and aggregated. If the cycle contains 6 transcribed segments and 3 sets of valid keywords, the results are formed into a triple (A, B, C) based on the transcribed sequence A, the time sequence B, and the channel number sequence C. Then, the results are sorted using the timestamp as the primary key. If multiple keywords have a time interval of less than 2 seconds or exhibit sequential order, the sorting process is not performed. If the sequence crosses, it is marked as a non-standard paragraph. During sorting, the time logic is forced to be linear, and the event chain is reconstructed by splicing paragraphs. The reconstructed paragraph groups are then written into the monitoring structure registration area. After one data cycle, a complete temporal logical arrangement of morphemes can be obtained. For example, if case number Z01 has three sets of morpheme markers collected continuously between 9:31:20 and 9:31:50, namely 9:31:22, 9:31:26 and 9:31:48, then the sorting group is [9:31:22, P1], [9:31:26, P2], [9:31: [48, P3] Finally, the morpheme list number YL-Z01-VC003 can be generated based on the recording component. This list records the one-to-one correspondence between the path channel number and the task voice event sequence in the system storage structure. The sorting structure is composed of timestamps and position identifiers. The first and last segments of the logical chain are used to distinguish whether the complete interaction cycle standard is met. If it is not met, the morpheme sorting list is marked as a non-closed record and written to a temporary buffer for subsequent supplementary sampling. The sorting result will be used as the pre-input of semantic trigger density judgment to uniformly generate the morpheme time sorting list.

[0023] The morpheme rhythm recognition submodule extracts the time interval between adjacent morphemes based on the morpheme time sorting list, identifies the difference features between the response rhythm and sentence structure between morphemes, and combines the coverage of silent segments in the semantic segment to screen out rhythmically disjoint segments and generate a set of response discontinuous fragments. First, extract the timestamps corresponding to any two consecutive morphemes in the list. By reading the morpheme number sequence and the corresponding time label, construct a sequentially arranged time difference sequence group. Based on the response events within the patient's voice channel, each time interval is used as an expression window for semantic rhythm analysis. For example, in a certain respiratory monitoring, the keywords "shortness of breath," "rapid breathing," and "breathing pause" occurred at 9:01:04, 9:01:08, and 9:01:22 respectively. Therefore, the first interval is 4 seconds, and the second interval is 14 seconds. This time difference represents a basic unit for measuring the strength of semantic continuity. In the actual system, the lower limit of the rhythm reference interval is set to 2 seconds, and the upper limit to 10 seconds. Within this interval, the semantic rhythm is considered normal. The response between 9:01:04 and 9:01:08 is considered a valid rhythmic chain, while the response time between 9:01:08 and 9:01:22 is considered a broken rhythmic chain because it exceeds the upper limit of the reference interval. Based on the above judgment logic, it is necessary to iterate and filter the time intervals between all adjacent morphemes in the sorted list, and to address the issues exceeding the rhythm reference interval. The interval positions are marked, and then, combined with the position of the corresponding morpheme segment in the sentence structure, it is determined whether the response occurs in the subject structure, predicate structure, or sentence-end pause segment. If it occurs in the predicate segment and the interruption time exceeds twice the average of the normal segment, an additional semantic break marker needs to be added. For example, the 14-second interval mentioned above corresponds to the segment between "rapid breathing" and "breathing pause," which semantically constitutes a sudden change in intonation. The system records it as the second break segment. At the same time, it is necessary to further extract the coverage area of ​​the corresponding silent segment in the sentence segment. If the coverage area begins at... If the silent segment ends within 1 second earlier than the end time of the previous morpheme and ends more than 1 second later than the start time of the next morpheme, then the silent segment is judged to completely cover the semantic gap segment. For example, if there is no keyword response from 9:01:09 to 9:01:21, then it constitutes complete coverage. The system merges this segment with the rhythm break segment and marks them as discontinuous response segments. Finally, it extracts all the break segments, the corresponding silent segments, and the merged semantic interruption structure fragments from the time sorting list, writes them into the buffer according to the channel number to form a set of rhythm discontinuous identifier segments, and generates a set of response discontinuous fragments.

[0024] The channel state switching submodule calls the response set of discontinuous segments, and the voice channel permission control node performs permission change operation, sets the corresponding voice channel state to listening, and writes the change record to the registration area of ​​the main control logic module to obtain the voice channel listening mode registration record; First, all labeled discontinuous segment numbers and their corresponding speech channel numbers are extracted from the dataset. The current attribute field of the channel status is then read and decoded. Discontinuous segment numbers, such as D001, D002, and D003, represent speech segments identified as rhythmic breaks or silent coverage within three monitoring periods. These numbers typically correspond to channel numbers such as VC01 and VC02. Upon receiving this dataset, the access control node sequentially registers and compares the status of each segment according to its channel. If VC01 is currently in interactive mode and has been marked with a D-class label for two consecutive periods, the access control node initiates a permission change process. It retrieves the channel's status identifier field from the access configuration table and changes the original value "ACT" to "MON," indicating that the channel has transitioned from active response mode to listening mode. The triggering condition for this operation depends on the correspondence between the segment interruption count statistics and the control strategy's set threshold. For example, if a patient experiences three speech response interruptions between 9:10 and 9:10:30, the control system will correlate these interruption counts with the current path. The system compares the warning criteria. If the number of consecutive periodic interruptions reaches the switching condition, a switching command is immediately triggered. The command content is written to the control command cache queue with the path code and the new permission status. Next, the permission control node registers the updated path status and the corresponding trigger time in the permission switching log area of ​​the main control logic module. The log structure must include five fields: channel number, switching timestamp, trigger source identifier, old permission value, and new permission value. For example, the record for switching the VC01 channel at 9:10:31 is: [VC01, 9:10:31, Class D centralized trigger, ACT, MON]. After registration, the data synchronization command is issued by the main control module to the permission synchronization end to achieve consistent writing of the status fields between the main and auxiliary logic modules. A channel switching flag is added to the execution status register so that the channel is in a passive state in the next round of channel scheduling. Finally, the system generates an updated channel status structure record by comparing the channel status configuration table with the main control status registration table, and writes it uniformly into the permission identification area and behavior policy area to obtain the voice channel monitoring mode registration record.

[0025] Please see Figure 1 and Figure 2 The task compression determination module includes: The task trajectory recognition submodule is based on the voice channel monitoring mode registration record, retrieves the scheduling tag and running trajectory information of the respiratory monitoring task, extracts the task start time field and mounting status field, sorts out the sequential mapping relationship between task cycle segments and stage tags, and generates task stage matching structure value. First, the channel number and task mapping structure fields marked in the registration record need to be extracted. The task type field pointed to by the corresponding identifier needs to be confirmed by the task scheduling list to belong to the respiratory monitoring task category. Then, the scheduling tag section is entered, and the tag code, task activation time, expected duration, and attachment position fields in each scheduling information are extracted in sequence. Then, the running timeline structure bound to the task is matched in the running trajectory log. This structure divides the periodic segments by minute granularity and attaches the corresponding status identifier. The status identifier field usually uses a two-bit mark, such as "ST" for the resting stage, "AC" for the active execution stage, and "RT" for the task response stage. For example, in a task with the number RM019, the channel listening identifier is written at 9:00:00. The task scheduling tag shows that "AC" is activated at 9:01. The running trajectory shows that the task status switch occurs at 9:01, 9:05, 9:12, etc., corresponding to the task stages of start, monitoring, and pause. The system needs to record the task tag and the running trajectory event timeline. A one-to-one mapping is performed to establish a task scheduling event chain. Then, the task start field is read, which points to the time node when the task is first awakened or mounted. This node data needs to be confirmed through the mounting status field. If the mounting status field is "ACT", the start time is the awakening time. If the status field is "IDLE", the start time is delayed after the time set by the scheduling tag. After confirming the start time, the mounting status and the current cycle number are written into the structure list. The stage number is expanded by combining the cycle segment length (e.g., 5 minutes per segment) to form number groups P01-P06-P12, etc. Finally, each cycle segment of the task and the running status tag are arranged in order, and the sequential correspondence between the cycle segment and the tag is marked according to the task number. For example, in task RM019, the stage tags from P01 to P06 are "AC", "RT", "RT", "PA", "AC", and "RT" respectively. The system summarizes this relationship into the matching structure mapping table for subsequent analysis of state continuity and interruption trends, and generates task stage matching structure values.

[0026] The progress interruption analysis submodule calls the task stage matching structure value, extracts the stage dwell segment and interruption mark field from the tag sequence, calculates the ratio between the total time length covered by the stage dwell segment and the total number of segments, and obtains the tag interruption delay interval group value. The formula for calculating the ratio between the total time length covered by each stage's dwell area and the total number of stages is as follows: ; in, The ratio of the total time length covered by the representative stage's dwell segment to the total number of segments. Representative stage The number of stops Representing the The end time of each stop segment, Representing the The start time of each stop segment, Representing the The stage label amplitude value corresponding to each dwell segment Representative stage The average label amplitude across all dwelling segments Representing the The disturbance offset of each dwelling segment Representative stage The average disturbance offset of all dwelling segments Represents the amplitude deviation adjustment coefficient. Represents the disturbance fluctuation adjustment coefficient; In a specific example, let and (24-hour system), that is Hour; Assumption (Unit: times / hour), and (Unit: times / hour) ; set up ,and ,but ; set up and ; The calculation steps of the formula are as follows: Calculate the sum of the durations of each segment: One hour, assuming there are 5 segments; Calculate the total adjustment for label amplitude deviation: ; Calculate the sum of squares and the square root of the disturbance offset: ; Calculate the numerator: ; Calculate the denominator: ; The final value of the calculation formula: ; The result indicates that the phase The average dwell ratio is 2.315. This value reflects the weighted ratio of the effective duration of the dwell segment, the change in label amplitude, and the impact of disturbances relative to the total number of segments within a given time period. This value can be used to analyze the efficiency or stability of a specific stage and has important guiding significance for managing or optimizing operational processes.

[0027] The abnormal state labeling submodule compares the delay interval group value of the label interruption with the state start and end nodes in the task instruction trigger sequence, extracts the state consistent segment, marks the corresponding segment as the state to be guaranteed, and writes it into the control bus identification area to obtain the labeled respiratory detection task identifier. First, the start time, end time, and corresponding stage label of each interrupt segment are extracted. Then, they are compared one by one with all the start and end nodes of the status recorded in the task instruction trigger sequence. The status nodes are generally identified by four categories: "Active," "Hold," "Paused," and "Terminated." Each category of status has a clear start and end time mark. For example, in a task instruction trigger record, the status node "Active" starts at 09:12:00 and ends at 09:14:30, while the interrupt segment label shows 09:13:00 to 09:15:00. The system needs to determine the coverage relationship between the interrupt segment and the status node. If the start time of the interrupt segment is inside the status segment or coincides with its end time, it is considered a status overlap. The system further extracts the set of intersection segments of all interrupt segments and task status segments, and includes those with an intersection duration of more than 30 seconds in the valid candidate group. Then, the system identifies the consistent status segments in the candidate group. For segments marked as "active" or "paused" in both the task instruction record and the interruption label, their corresponding task number and stage identifier fields are extracted. Then, the status of each identifier segment is supplemented according to the preset guarantee status instruction template in the instruction structure. For example, in task number TM021, stage P04 is in an instruction record interruption state from 09:18:10 to 09:19:50, and is also marked as a pending area in the interruption label. This stage is then uniformly assigned the "pending guarantee" label and written into the identifier field segment in the control bus. The bus address structure records that this identifier segment must include four parameters: task number, stage number, delay interval length, and label writing time. The system summarizes all eligible task segments and writes them to the task control data area via the task status aggregator, ultimately completing the aggregation operation of all pending guarantee segments and obtaining the labeled respiratory detection task identifier.

[0028] Please see Figure 1 and Figure 2 The resource-slanted execution module includes: The resource path extraction submodule, based on the labeled respiratory detection task identifier, calls the task-corresponding path information in the resource channel configuration table, extracts the communication port quantity field and node resource address range, combines them to generate the resource mapping structure of the path, and stores the structure field and channel number in the buffer to generate a task path resource combination list. First, extract the task number field from the identifier and task association structure to confirm the path allocation record of the task in the resource channel configuration table. When calling the path information field, prioritize extracting the communication channel number and task type fields bound to the task. Retrieve the resource item corresponding to the "respiratory detection" type in the table structure. Based on the node code to which the task belongs, such as nodes ND105 and ND108, the system reads the number of communication ports bound to it. Generally, this field is automatically set during the task initialization phase. For example, task number RM056 occupies 4 communication ports, namely P01 to P04. The node resource address segment points to the current task's location identifier in the resource network, used to determine whether it needs channel migration processing across nodes. The address segment format is usually like 10.0.3.X, where X is the port branch number. This address and the communication channel number form a one-to-one correspondence. Next, reconstruct the path mapping lookup table by combining the extracted port number values ​​and node resource addresses according to the combination rules. Each set of reference units is composed of three segments: "task number - channel number - port address". For example, the port mapping configured for task RM056 under channel VC102 is: RM056-VC102-P01: 10.0.3.4, RM056-VC102-P02: 10.0.3.5, etc. All mapping structures are written into the resource channel buffer structure, and a path number index structure is generated synchronously for the lower-level scheduling nodes to perform resource status traversal and release strategy judgment. Each path structure also needs to be accompanied by metadata fields such as its channel number, node number, and port mapping value. During the writing process, the system verifies the integrity of the field structure. If there is a duplicate port or a missing address in a task path, the path is marked as an unschedulable path and is not written to the buffer list. After the remaining path information is successfully written, it forms a complete path structure combination block, which is cached in the path structure management area to complete the encapsulation and organization of the resource structure and obtain the task path resource combination list.

[0029] The node allocation ratio judgment submodule calls the task path resource combination list, extracts the paths with the status field as standby in the auxiliary monitoring tasks, obtains the corresponding communication node usage field and path ownership number, calculates the node usage rate range based on the resource allocation ratio warning threshold, filters the set of paths whose node usage rate falls into the starting segment of the range, and generates a list of transferable node occupancy. The specific formula for calculating the node utilization rate interval is as follows: ; in, Representing the The node utilization range in the path, Representing the The first path The current utilization rate of each communication node. Representing the The first path Resource allocation warning thresholds for each communication node Representing the Resource usage adjustment factor in the path, Representing the The first path The node priority weight of each communication node Representing the The average utilization rate of all communication nodes in the path. Representing the The communication interference adjustment ratio in each path Representing the The total number of communication nodes included in the path. The loop variable number for the communication nodes within the path; Assuming the path contains Each of the communication nodes has a current utilization rate. Data is collected periodically through network-based real-time monitoring devices, with a monitoring frequency set to once per minute. During the current observation period, the utilization rate of the five nodes is: , , , , ; Resource allocation warning threshold Based on historical load extreme value regression calculations, the objective is to limit the minimum load standard for each node, as shown below: , , , , ; Resource usage adjustment values The load fluctuation over the past 7 days is derived from the load standard deviation and fitted. In this example, it is set as: ; Node priority weight The node scheduling frequency and task level are evaluated and quantified as a percentage, then converted into a ratio between 0 and 1.5. The conversion standard is to assign a weight greater than 1 to high-frequency tasks and less than 1 to low-frequency tasks. The corresponding values ​​are: , , , , ; Average utilization rate of communication nodes The usage rate of all monitoring nodes is calculated as the arithmetic mean: ; Communication interference adjustment value Derived from path channel congestion statistics, and normalized to the interval of 0 to 2 after fitting a linear model of bit error rate and collision packet ratio, this path is set as

[0030] Substitute the calculation process from Part 1: ; ; Substitute into the calculation process in Part Two: ; ; ; ; ; Substitute into the formula: ; The result indicates that the first The evaluation value of the node utilization rate interval for each path is 95.98. This value comprehensively reflects the degree of offset between node utilization and resource allocation, as well as the distribution intensity of scheduling priority. It serves as an important basis for subsequent screening of path sets where node utilization falls into the starting segment of the interval. Adjustment and allocation strategies should be given priority when generating the list of transferable node occupancy.

[0031] The path ownership update submodule, based on the list of occupancy of transferable nodes, marks the ownership change in the path number field, updates the original node ownership path field to the current respiratory monitoring task path, writes it into the distribution structure segment of the resource channel configuration table and registers the synchronization status, and obtains the adjusted resource channel distribution table. First, the path number field, its corresponding communication node resource number, and current occupancy status flag need to be extracted from the list. After locking the path node group with the status field set to "Releasable" in the traversal structure, the system performs an ownership identifier retrieval operation for each path number. This operation locates the original node ownership field of the current path in the resource channel configuration table. For example, path number P023 currently points to node ND107. In this task reassignment, its ownership needs to be adjusted to node ND115 and bound to the respiratory monitoring task number RM071. The ownership change marking process is implemented through field overwriting. The original ownership field content "ND107" is overwritten by the new value "ND115", and a new label field "RM071" is added to mark that the path has been assigned to a task. Subsequently, the system performs a field synchronization operation to ensure that the ownership information is written to the "node mapping structure segment" and "task mapping segment" in the resource channel configuration table. This structure segment is two-dimensional. The mapping matrix uses the path number as the primary index and the node number as a secondary field. Synchronous writing ensures that the subsequent task scheduling module can directly read the latest path attribution relationship from the configuration table. In the example, path P023 was originally bound to task RM063 and node ND107; after the change, it becomes RM071-ND115. Furthermore, the synchronization status registration module needs to extract the change occurrence time and synchronization record number, and record the primary key information of each path's attribution change in the change log. The change log format is as follows: [Path number: P023, Original attribution: ND107, Current attribution: ND115, Bound task: RM071, Change time: 09:23:45]. The above content will be uniformly written into the synchronization task record list and used as the basis for data recovery and channel migration backtracking. After all field structures are verified, the path is officially incorporated into the current task resource structure, completing the transfer operation of resource occupancy records and generating an adjusted resource channel distribution table.

[0032] Please see Figure 1 and Figure 2 The differential channel analysis module includes: The channel data extraction submodule reads the continuous acquisition records at the end of the body temperature channel, oxygen saturation channel, and motion monitoring channel based on the adjusted resource channel distribution table, extracts the time series and numerical series of the corresponding acquisition segments in the channel, divides the continuous segments into signal segment sets and archives them to generate a channel acquisition segment sequence set. First, the body temperature channel, oxygen saturation channel, and motion monitoring channel marked as "active acquisition status" are selected sequentially from the table. The acquisition time field and data writing segment at the end of each channel are located. For each channel, the data packet group from the most recent complete acquisition cycle is retrieved from the acquisition record buffer. The body temperature channel data structure records temperature value changes in seconds. For example, 60 sets of data are continuously recorded between 10:03:00 and 10:04:00, with a starting temperature of 36.5℃ and an ending temperature of 37.3℃. The intermediate data sequence contains several small increments, which are defined as "fluctuating increment" segments. The system marks this acquisition segment as a complete continuous segment. The oxygen saturation channel has even shorter acquisition intervals, recording once every 0.5 seconds. If Sp appears consecutively within a 10-second interval... If the downward trend does not recover to the previous value, the system marks it as a "trend variation type" segment. When classifying this segment, the start timestamp and segment length fields must be attached. The motion monitoring channel is mainly based on two dimensions: vibration amplitude and movement frequency. If the motion amplitude jumps regularly within 5 consecutive seconds (e.g., from 0.3g to 0.6g, then to 0.9g), accompanied by an increase in frequency, the system classifies the data segment of this channel as a "rhythm disturbance type" segment. The acquisition records of each type of channel are decomposed into time series and numerical series in the system processing module, and respectively constructed into a two-dimensional time data structure TSi and a corresponding numerical structure VSi. The system merges the same structure according to the channel type to form a signal segment set SP, and generates a unique segment number for each record and classifies it. When archiving, the "channel number + segment number" must be written into the data archiving area as the structural primary key, and additional information such as its source task, node, and transmission path must be recorded. Finally, the cleaning and structuring of all acquisition record segments are completed to generate a channel acquisition segment sequence set.

[0033] The fluctuation segment calculation submodule calls the channel to collect segment sequence sets, analyzes the periodicity and amplitude variation characteristics of numerical changes in the segment, combines the response rhythm reference value set by the channel, calculates the offset ratio of frequency change amplitude in continuous segments, and filters segments with prominent offset rates to obtain the fluctuation abnormal frequency interval value. The formula for calculating the offset ratio of frequency variation amplitude in a continuous segment is as follows: ; in, Representing the The offset ratio of the frequency variation amplitude in a continuous segment Representing the The number of paragraphs within a segment Representing the In the segment The magnitude of frequency variation in paragraphs. Representing the The frequency response rhythm of the channel settings in the clip references a baseline value. Representing the Fragment No. Frequency channel response weights of paragraphs, Representing the The average of the frequency variation values ​​of all paragraphs in the segment. The constant offset stabilization factor is used to prevent the denominator from being zero.

[0034] Assume the following specific numerical example: Segment Number: ; Number of paragraphs: ; Frequency variation range: , , ; average value: ; Frequency response rhythm reference value: ; Frequency channel response weighting factor: , , , ; Constant offset stability factor: ; Calculation process: Calculate the offset value for each paragraph: Paragraph 1: ; Paragraph 2: ; Paragraph 3: ; Paragraph 4: ; Calculate the average offset ratio: ; The results indicate that the average offset ratio of frequency variation in the first consecutive segment is 0.473, reflecting the overall deviation level of the segment frequency values ​​from the rhythmic reference value. This value is calculated by taking the difference between each segment frequency value and the reference value, and then normalizing it by combining the difference between the segment frequency values ​​and the average value.

[0035] The abnormal path marking submodule identifies the channel number of segments that are inconsistent with the stable response characteristics based on the frequency range of fluctuation anomalies. It writes the channel number as the interference channel identifier number into the abnormal path list structure and records it synchronously to the interference path index area to obtain the abnormal path identifier list. First, the frequency fluctuation ratio, channel number, and time period identifier of each segment are extracted. These are then compared in parallel with the system's preset stable response characteristic parameters. The stable response characteristics are statistically derived from the baseline data of each channel. The stability threshold for the body temperature channel is set to a fluctuation of no more than ±0.2℃ per minute, the oxygen saturation channel is set to a change of no more than ±1% within 3 seconds, and the motion monitoring channel is set to a movement of no more than ±0.4g within 5 seconds. When the system detects that the change of a segment within the above time window exceeds the stable range of the corresponding channel, it is identified as an inconsistent segment. The channel number and start and end time identifiers of the segment are then extracted. For example, if the temperature record of body temperature channel VC031 from 10:12:00 to 10:12:30 fluctuates by 0.6℃, far exceeding the stable range, then... If the upper limit is reached, the channel number VC031 is recorded as an anomaly and written into the anomaly path list structure as an interference channel identifier. The structure records fields including channel number, anomaly type identifier, segment start time, segment end time, and anomaly level number. Subsequently, the system constructs an interference path index area based on the running identifier information of all channels in the current task structure, extracts the anomaly path number and its corresponding task number for dual index embedding, writes it into the index area segment, and sorts it according to priority. Each interference record must also be marked with a path credibility level downgrade instruction in the task scheduling module. The system uniformly pushes the identifier number of the above-mentioned anomaly channel to the monitoring master control terminal and stores it in the anomaly status structure cache for subsequent channel priority adjustment module to call, completes the archiving of the anomaly path structure, and obtains the anomaly path identifier list.

[0036] Please see Figure 1 and Figure 2 The path state injection module includes: The path number location submodule, based on the abnormal path identifier list, calls the path number index table on the main control node, extracts the running status field corresponding to the abnormal channel number, retrieves the task interface segment and channel control number field in the status field, and generates an abnormal channel task interface index set. First, the abnormal channel number information is extracted from the list item by item. The number field is used as the main retrieval index to call the path number index table in the master control node. The index table structure uses the channel number as the first field and maps sub-items such as path status segment, running status field, and bound task identifier. First, the system extracts the running status field of the corresponding channel by matching the abnormal number with the matching field of the number registered in the index table. This field generally contains the current channel running mode (such as "listening", "responding", "interrupted", etc.) and its switching flag. In the extraction example, the channel number is VC047, its running status is "listening" and the flag field is "dynamic abnormal". Then, the task interface segment nested in the status field is parsed. This segment contains the task interface number and interface type field of the current channel. For example, the interface number IF108 indicates that it corresponds to the respiratory monitoring interface, and the "TYPE" field is marked as "BREATHE". Next, the channel control number field is extracted. This field is used to identify the current channel. The control path is bound to a logical number, which is in the format of CTL-XX-XXX. For example, CTL-A-143 indicates that the control strategy belongs to module A, number 143. The system combines the path number, task interface number, and control number to form a complete path control mapping structure. Each structure contains five pieces of information: channel number, running status, interface number, interface type, and control number. The above five-tuple structure is then written into the task interface index structure cache. The cache structure groups and categorizes each path structure according to the control module, which is used to quickly locate the target field when the module writes status prompts and priority update instructions. For example, the five-tuple constructed for the abnormal path VC047 is: [VC047, listen, IF108, BREATHE, CTL-A-143]. After the system completes the extraction of all abnormal path number structures and the generation of mapping structures, it writes them into the path interface cache and simultaneously registers the task calling module to generate an abnormal channel task interface index set.

[0037] The tag field insertion submodule calls the abnormal channel task interface index set, extracts the status overview field and monitoring tag field within the task interface segment, inserts a path prompt identifier into the status overview field, inserts interference tags into the monitoring field, registers field changes, and generates path control tag writing records. First, the task interface segment list is extracted by grouping according to the control strategy number. Then, the status overview field and monitoring tag field in the nested fields of the interface structure are read sequentially. In the status overview field, the system retrieves the field segment start mark "PATH_STATUS". After confirming that it is a writable area, the path prompt identification content is constructed according to the channel number structure. The identification structure is composed of the "PTH" prefix and the abnormal path number. For example, if the abnormal channel number is VC092, the generated path prompt identification is "PTH-VC092" and inserted at the end of the "PATH_STATUS" field. The "INTF_DISTURB" field segment is located. The corresponding segment is the preset write area used by the system to mark the interference status. The interference tag content is composed of the abnormality type field and the path number. For example, if the oxygen saturation channel is marked due to abnormal fluctuation frequency, it is written to "SAT-DIS-VC0". After the insertion of "92", the system calls the field to write the instruction recorder, registering the change status, written content, timestamp, and execution control number of the two fields in a unified manner. The record is completed in the log buffer in an entry structure. For example, if task interface IF207 is bound to the abnormal channel VC092, its status field is written as "PTH-VC092", its monitoring field is written as "SAT-DIS-VC092", the timestamp is 10:45:32, and the control number is CTL-A-067, then the record is: [IF207, VC092, PTH-VC092, SAT-DIS-VC092, 10:45:32, CTL-A-067]. After all the written entries are completed, they are archived to the control instruction update log segment and indexed by instruction number to form a traceable field operation chain. After all the abnormal interface fields are written, a path control tag write record is generated.

[0038] The status register synchronization submodule writes records based on path control tags, extracts the channel number corresponding to the inserted fields, performs priority field update and status field synchronization operations in the master control node, writes the status content into the robot status register, and obtains the integrated result of the operating status after the monitoring robot path adjustment. First, the channel number corresponding to the inserted field in each record is extracted, and the task path index structure bound to that channel is loaded in the master control node control module. The corresponding control segment area is located by retrieving the path mapping table through the channel number, and the field update list is read. The "PATH_STATUS" field is used for path status description, and the "INTF_DISTURB" field is used for label-level interference marking. The system first performs a priority judgment operation on each channel number according to the control logic rules, and updates the "PRIORITY_INDEX" field according to the task interface category and the current control strategy level value. For example, if the original priority index is "2-B", and the interference identifier is "SAT-DIS-VC102", it is updated to "3-C". In this index rule, the number represents the control level, and the letter represents the processing sequence. The field update operation is directly located in the path control table using the channel number as the primary key. After the status field is updated, the new value is written into the status of the master control node. The system then enters a synchronization process, synchronizing the state structure to the robot's state register. Each channel's state in the register is recorded by a five-element structure: channel number, priority index, path state value, interference tag, and write timestamp. For example, the structure corresponding to VC102 is [VC102, 3-C, LISTENING, SAT-DIS-VC102, 10:53:46]. After the synchronization operation is completed, each field write information must also be synchronized to the state change registration area. This registration structure is used to record the update sequence of the robot's running state, ensuring that subsequent state call modules can perform execution control logic judgments based on the latest write record. After confirming that all fields are successfully synchronized, the system archives all register state structures, writes them to the running state integration cache, and clears the path number task queue marked as "completed" in this write task. It then integrates the state fields of all channels in the register to obtain the integrated running state result after the monitoring robot's path adjustment.

[0039] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-source data acquisition and monitoring robot based on the Internet of Things, characterized in that: The multi-source data acquisition and monitoring robot includes: The morpheme trigger recognition module is used to obtain the operation record of the voice component in the respiratory monitoring task, extract the keyword morpheme position and timestamp, analyze the keyword response distance and silent zone distribution, and switch the channel state to the monitoring mode after identifying abnormal segments to obtain the voice channel monitoring mode registration record. The task compression and judgment module is used to extract the scheduling tag and running trajectory of the respiratory monitoring task based on the voice channel monitoring mode registration record, analyze the task delay status and attach the tag to be guaranteed, and obtain the labeled respiratory monitoring task identifier. The resource tilt execution module is used to extract the auxiliary path status and node distribution based on the labeled respiratory detection task identifier, release transferable resources and update the belonging path expression to obtain the adjusted resource channel distribution table. The difference channel analysis module is used to read path collection data and analyze fluctuation differences based on the adjusted resource channel distribution table, mark abnormal channel numbers, and generate an abnormal path identification list. The path status injection module is used to locate the abnormal task interface and write control tags based on the abnormal path identifier list, adjust the priority and update the status register to obtain the integrated result of the operating status after the path control of the monitoring robot.

2. The IoT-based multi-source data acquisition and monitoring robot according to claim 1, characterized in that: The voice channel monitoring mode registration record includes sparse segment identifiers, non-response time period ranges, channel permission switching status, and monitoring mode write timestamps. The labeled breathing detection task identifiers include mounting status category, delay marker type, scheduling trajectory positioning results, and stage interval labels. The adjusted resource channel distribution table includes auxiliary path release markers, belonging path update information, node port matching results, and resource distribution structure numbers. The abnormal path identifier list includes abnormal channel numbers, fluctuation segment distribution characteristics, structure offset type, and mutation behavior label values. The integrated result of the monitoring robot's operational status after path adjustment includes path prompt identifier records, task monitoring area interference labels, channel priority label status, and status register closed-loop structure.

3. The IoT-based multi-source data acquisition and monitoring robot according to claim 1, characterized in that: The morpheme trigger recognition module includes: The semantic tag extraction submodule is used to obtain the voice interaction records of the voice receiving component, semantic extractor and voice channel permission control node during the continuous operation cycle of the respiratory monitoring task, extract the timestamps and position order of the tagged morphemes in the sentences, and obtain the morpheme time sorting list. The morpheme rhythm recognition submodule is used to extract the time interval between adjacent morphemes based on the morpheme time sorting list, identify the difference features between the response rhythm and sentence structure between morphemes, and filter out rhythmically disjointed segments by combining the coverage of silent segments in the semantic segment, and generate a set of response discontinuous segments. The channel state switching submodule is used to call the set of discontinuous response segments, and the voice channel permission control node performs permission change operation to set the corresponding voice channel state to listening, and write the change record into the registration area of ​​the main control logic module to obtain the voice channel listening mode registration record.

4. The IoT-based multi-source data acquisition and monitoring robot according to claim 1, characterized in that: The task compression determination module includes: The task trajectory recognition submodule is used to register records based on the voice channel monitoring mode, retrieve the scheduling tag and running trajectory information of the respiratory monitoring task, extract the task start time field and mounting status field, organize the sequential mapping relationship between task cycle segments and stage tags, and generate task stage matching structure values. The progress interruption analysis submodule is used to call the task stage matching structure value, extract the stage dwell segment and interruption mark field in the tag sequence, calculate the ratio between the total time length covered by the stage dwell segment and the total number of segments, and obtain the tag interruption delay interval group value. The abnormal state labeling submodule is used to compare the delay segment with the state start and end nodes in the task instruction trigger sequence based on the value of the label interruption delay interval group, extract the state consistent segment, mark the corresponding segment as the state to be guaranteed, and write it into the control bus identification area to obtain the labeled respiratory detection task identifier.

5. The IoT-based multi-source data acquisition and monitoring robot according to claim 4, characterized in that: The formula for calculating the ratio between the total time length covered by the stage dwell segment and the total number of segments is as follows: ； in, The ratio of the total time length covered by the representative stage's dwell segment to the total number of segments. Representative stage The number of stops Representing the The end time of each stop segment Representing the The start time of each stop segment, Representing the The stage label amplitude value corresponding to each dwell segment Representative stage The average label amplitude across all dwelling segments Representing the The disturbance offset of each dwelling segment Representative stage The average disturbance offset of all dwelling segments Represents the amplitude deviation adjustment coefficient. This represents the disturbance fluctuation adjustment coefficient.

6. The IoT-based multi-source data acquisition and monitoring robot according to claim 1, characterized in that: The resource-slant execution module includes: The resource path extraction submodule is used to call the task-corresponding path information in the resource channel configuration table based on the labeled respiratory detection task identifier, extract the communication port number field and node resource address segment, combine them to generate the resource mapping structure of the path, and store the structure field and channel number in the buffer to generate a task path resource combination list. The node allocation ratio judgment submodule is used to call the task path resource combination list, extract the paths with the status field as standby in the auxiliary monitoring tasks, obtain the corresponding communication node usage field and path ownership number, calculate the node usage rate range based on the resource allocation ratio warning threshold, filter the set of paths whose node usage rate falls into the range of the beginning segment of the range, and generate a list of transferable node occupancy. The path ownership update submodule is used to mark the ownership change of the path number field based on the list of occupancy of transferable nodes, update the original node ownership path field to the current respiratory monitoring task path, write it into the distribution structure segment of the resource channel configuration table and register the synchronization status, so as to obtain the adjusted resource channel distribution table.

7. The IoT-based multi-source data acquisition and monitoring robot according to claim 6, characterized in that: The specific formula for calculating the node utilization rate interval is as follows: ; in, Representing the The node utilization range in the path, Representing the The first path The current utilization rate of each communication node. Representing the The first path Resource allocation warning thresholds for each communication node Representing the Resource usage adjustment factor in the path, Representing the The first path The node priority weight of each communication node Representing the The average utilization rate of all communication nodes in the path. Representing the The communication interference adjustment ratio in each path Representing the The total number of communication nodes included in the path. This is the loop variable number for the communication nodes within the path.

8. The IoT-based multi-source data acquisition and monitoring robot according to claim 1, characterized in that: The difference channel analysis module includes: The channel data extraction submodule is used to read the continuous acquisition records at the end of the body temperature channel, oxygen saturation channel, and motion monitoring channel based on the adjusted resource channel distribution table, extract the time series and numerical series of the corresponding acquisition segments in the channel, divide the continuous segments into signal segment sets and archive them, and generate a channel acquisition segment sequence set. The fluctuation segment calculation submodule is used to call the segment sequence set acquired by the channel, analyze the periodicity and amplitude variation characteristics of the numerical changes in the segment, combine the response rhythm reference value set by the channel, calculate the offset ratio of the frequency change amplitude in the continuous segment, and filter the segments with prominent offset rates to obtain the fluctuation abnormal frequency interval value. The abnormal path marking submodule is used to identify the channel number of the segment that is inconsistent with the stable response characteristics based on the fluctuation abnormal frequency interval value, write it into the abnormal path list structure as the interference channel identifier number, and synchronously record it to the interference path index area to obtain the abnormal path identifier list.

9. The IoT-based multi-source data acquisition and monitoring robot according to claim 8, characterized in that: The formula for calculating the offset ratio of the frequency change amplitude in the continuous segment is as follows: ； in, Representing the The offset ratio of the frequency variation amplitude in a continuous segment Representing the The number of paragraphs within a segment Representing the In the segment The magnitude of frequency variation in paragraphs. Representing the The frequency response rhythm of the channel settings in the clip references a baseline value. Representing the Fragment No. The frequency channel response weighting factor of the paragraph. Representing the The average of the frequency variation values ​​of all paragraphs in the segment. The constant offset stabilization factor is used to prevent the denominator from being zero.

10. The IoT-based multi-source data acquisition and monitoring robot according to claim 1, characterized in that: The path status injection module includes: The path number positioning submodule is used to call the path number index table on the main control node based on the abnormal path identifier list, extract the running status field corresponding to the abnormal channel number, retrieve the task interface segment and channel control number field in the status field, and generate an abnormal channel task interface index set. The tag field insertion submodule is used to call the abnormal channel task interface index set, extract the status overview field and monitoring tag field within the task interface segment, insert a path prompt identifier into the status overview field, insert interference tags into the monitoring field, register field changes, and generate path control tag writing records. The status register synchronization submodule is used to write records based on the path control tag, extract the channel number corresponding to the inserted field, perform priority field update and status field synchronization operations in the master control node, write the status content into the robot status register, and obtain the integrated result of the operating status after the monitoring robot path adjustment.

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