A surveying internet of things cloud data real-time acquisition management method and system

Abstract

The present application relates to the technical field of Internet of Things data management, in particular to a surveying Internet of Things cloud data real-time collection management method and system; the method comprises the following steps: obtaining a surveying project and original surveying data to construct a surveying anchor point identifier, configuring an anchor point event template, controlling an Internet of Things collection terminal to perform event boundary detection and generate an anchor point time slice structure; associating surveying data in a time slice with context information, generating an anchor point time slice cloud data unit and writing it into a cloud end temporary storage area; constructing a three-domain quality state machine on the cloud end, determining the quality state of the anchor point time slice cloud data unit, generating an anchor point quality evolution track and a quality lock band index, and writing them into a corresponding storage area according to the quality level; based on the quality lock band index and the anchor point quality evolution track, performing time slice screening and quality filtering on a surveying cloud data query request, and outputting a query result. The present application realizes a surveying anchor point-oriented Internet of Things cloud data quality grading collection management method and system.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) data management technology, specifically to a method and system for real-time acquisition and management of IoT cloud data. Background Technology

[0002] During the engineering survey process, with the application of IoT technology and cloud computing platforms, various survey terminals such as drilling equipment, in-situ testing instruments and environmental monitoring sensors have gradually acquired the ability to collect data online and upload it remotely. The multi-source monitoring data of the survey project is usually continuously aggregated to the cloud data center through the field gateway and communication link, and is uniformly stored and managed in the cloud according to the dimensions of project, equipment, time, etc., to support the subsequent sorting of survey results, parameter selection and design calculation. In the process of IoT-based data collection and cloud management in engineering surveying, there are still problems such as the difficulty in managing surveying cloud data according to fine spatial segments and time slices, and the difficulty in clearly identifying abnormal segments. Specifically, on the one hand, although the data collected on site can be uploaded to the cloud by project and time, data from different borehole locations, different depth segments, different processes, and different equipment states within the same project are often stored together. There is a lack of data organization methods based on specific survey locations and work periods, making it difficult to quickly distinguish which segments of the data can be directly used and which segments need to be used with caution or removed when performing load-bearing capacity calculations, deformation analysis, or verification of test results. On the other hand, once communication jitter, abnormal equipment status, or sudden changes in survey indicators occur during surveying operations, the existing cloud management mechanism can usually only roughly record the approximate time range of the anomaly. There is a lack of technical means to centrally mark and index the anomaly center time and the affected segments before and after it based on time series. It is difficult for engineers to extract a whole segment of data related to the anomaly in a timely and accurate manner when tracing quality, arranging supplementary measurements, and verifying designs. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for real-time acquisition and management of survey IoT cloud data, in order to solve the problems mentioned in the background art, such as the difficulty in managing survey cloud data according to fine spatial segments and time slices, and the difficulty in clearly identifying abnormal segments.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for real-time acquisition and management of IoT cloud data, comprising: S1. Obtain exploration project information and raw exploration data, construct exploration anchor point identifiers and configure anchor point event templates, control the IoT acquisition terminal to perform event boundary detection, and generate anchor point time slice structures according to event boundaries; Among them, the exploration anchor point identifier is a combination of identifier fields consisting of project identifier, exploration work point identifier, and borehole segment identifier; the anchor point time slice structure is a data record structure that identifies the time interval attribute of the exploration anchor point in a single time slice, using the exploration anchor point identifier and the start and end time of the time slice as indexes. S2. Based on the anchor point time slice structure, associate the original exploration data and context information in each time slice, generate anchor point time slice cloud data units for each exploration indicator in the time slice, and write the anchor point time slice cloud data units into the cloud temporary storage area. Among them, the anchor point time slice cloud data unit is a cloud data recording unit that records a single exploration indicator data using the anchor point time slice structure as an index. S3. Construct a three-domain quality state machine in the cloud based on anchor point time slice cloud data units, determine the quality status and perform state transition for each anchor point time slice cloud data unit of the same exploration anchor point, generate quality locking index and anchor point quality evolution trajectory, and write the anchor point time slice cloud data units into the storage area corresponding to different quality levels according to the quality status. Among them, the three-domain quality state machine is a quality state modeling structure composed of three quality domains: the acquisition link domain, the equipment state domain, and the operating condition semantic domain, as well as their corresponding quality states and state transition relationships. S4. Based on the quality locking index and anchor point quality evolution trajectory, the anchor point time slice is filtered, the quality status is filtered, and the storage area is located for the exploration cloud data query request, and the exploration cloud data query results are generated.

[0005] Preferably, in step S1, the survey anchor point identifier is used to identify the survey spatial object and the survey location segment; the anchor point event template is a predefined set of event descriptions for survey anchor point related events, and the fields of the anchor point event template include an event type field, an event triggering condition field, and an event priority field.

[0006] Preferably, in S1, the event boundary detection is a detection process that continuously monitors the original survey data based on the anchor point event template, and determines the start and end times of the survey anchor point event when the event triggering conditions are met to obtain the event boundary; the anchor point time slice structure is used to define the time slices divided by the event boundaries on the time axis; the event boundary includes time boundary pairs formed by the start and end times of adjacent survey anchor point events; wherein, the time boundary pairs further serve as the basis for pre-setting event boundary priority rules, maximum no-event duration threshold, and fixed time slice supplementary rules.

[0007] Preferably, in S2, the context information within each time slice is a collection of time slice context fields. The time slice context fields include the spatial location field of the survey location, the process field of the survey process type, the device status field of the IoT acquisition terminal, and the link diagnosis field of the acquisition link. The time slice context fields are associated with the corresponding anchor time slice structure and are shared by all anchor time slice cloud data units within that time slice.

[0008] Preferably, in step S2, the anchor point time slice cloud data unit is recorded as a single-index granularity record, recording the data content of a single exploration index, and establishing a reference relationship with the corresponding anchor point time slice structure through the index field of the anchor point time slice structure to share the time slice context field set; the cloud temporary storage area is a data storage area located in the cloud, used to store anchor point time slice cloud data units generated based on the anchor point time slice structure that have not yet been written to the storage areas corresponding to different quality levels according to the quality status; the cloud temporary storage area also groups and stores the anchor point time slice cloud data units according to the exploration anchor point identifier and the start and end time of the time slice, forming a sequence of anchor point time slice cloud data units of the same exploration anchor point on the time axis.

[0009] Preferably, in step S3, the three-domain quality state machine is used to determine the quality status of each anchor point time slice cloud data unit of the same exploration anchor point based on the acquisition link domain, equipment status domain, and working condition semantic domain, and organizes the three-domain quality status information on the time axis; the input sources of the three-domain quality state machine include the link diagnosis field, equipment status field, and exploration index data associated in the anchor point time slice cloud data unit; the three-domain quality state machine outputs a three-domain quality status result for each anchor point time slice cloud data unit, which includes the quality status of the acquisition link domain, the quality status of the equipment status domain, and the quality status of the working condition semantic domain.

[0010] Preferably, the acquisition link domain, device state domain, and operating condition semantic domain of the three-domain quality state machine are used to determine the quality state and perform state migration through cross-domain state constraint rules and state migration priority rules. The quality state determination and state migration are based on a preset quality level set. The quality state determination maps the anchor time slice cloud data unit to the target quality level in the preset quality level set according to the input source of the three-domain quality state machine. The state migration updates the preset quality level corresponding to each anchor time slice cloud data unit on the time axis according to the cross-domain state constraint rules and state migration priority rules.

[0011] Preferably, in step S3, the quality locking band index is a set of time slice indexes composed of the target anchor point time slice and the anchor point time slice identifiers of several anchor point time slices before and after it, used to identify the affected time slice segments around the target anchor point time slice during the quality status analysis and storage control process; the anchor point quality evolution trajectory is a serialized data structure composed of the time slice sequence of a single exploration anchor point and the corresponding quality status of each time slice, used to record the quality status change process of the exploration anchor point on the time axis; the target anchor point time slice is the anchor point time slice that meets the preset abnormal conditions in the quality status results output by the three-domain quality state machine, used as the time slice reference for generating the quality locking band index and identifying the center time slice during the quality status analysis and storage control process.

[0012] Preferably, in step S3, the quality locking band index is generated according to a preset number of forward expansion segments and backward expansion segments of the locking band. Specifically, for each target anchor time slice, the time slice sequence position of the target anchor time slice in the corresponding anchor quality evolution trajectory is obtained. Anchor time slices no more than the number of forward expansion segments of the locking band are selected forward in the time slice sequence, and anchor time slices no more than the number of backward expansion segments of the locking band are selected backward in the time slice sequence. The anchor time slice identifiers of the target anchor time slice, the forward-selected anchor time slices, and the backward-selected anchor time slices are added to the quality locking band index. The number of forward expansion segments and the number of backward expansion segments of the locking band are configurable integer parameters.

[0013] On the other hand, the present invention provides a survey IoT cloud data real-time acquisition and management system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the aforementioned survey IoT cloud data real-time acquisition and management method.

[0014] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: 1. In this invention, IoT-collected data is organized hierarchically based on survey anchor point identifiers, anchor point time slice structures, and anchor point time slice cloud data units. This allows for the direct management of survey indicator data in the cloud according to specific hole locations, corresponding depth segments, and collection periods. Simultaneously, process type, equipment status, and link diagnostic information are also included. This enables the rapid screening of usable data and the elimination of data from abnormal equipment or abnormal link periods when selecting calculation data or verifying test results. 2. In this invention, an anchor point quality evolution trajectory is generated by a three-domain quality state machine, and a quality locking band index is constructed. Combined with data management by storage areas divided according to quality level, abnormal time slices and their affected sections before and after the occurrence of communication jitter, equipment failure, or abnormal fluctuations in survey indicators can be automatically marked. During the cloud query stage, time slices are filtered and data is located according to quality level and locking band range, realizing centralized retrieval and comparative analysis of abnormal related data sections, improving the efficiency of survey data quality traceability and subsequent design review. Attached Figure Description

[0015] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0016] Example 1, as Figure 1 As shown, the present invention proposes a method for real-time acquisition and management of IoT cloud data, the specific implementation steps of which are as follows: S1. Obtain exploration project information and raw exploration data, construct exploration anchor point identifiers and configure anchor point event templates, control the IoT acquisition terminal to perform event boundary detection, and generate anchor point time slice structures according to event boundaries; Among them, the exploration anchor point identifier is a combination of identifier fields consisting of project identifier, exploration work point identifier, and borehole segment identifier; the anchor point time slice structure is a data record structure that identifies the time interval attribute of the exploration anchor point in a single time slice, using the exploration anchor point identifier and the start and end time of the time slice as indexes. S2. Based on the anchor point time slice structure, associate the original exploration data and context information in each time slice, generate anchor point time slice cloud data units for each exploration indicator in the time slice, and write the anchor point time slice cloud data units into the cloud temporary storage area. Among them, the anchor point time slice cloud data unit is a cloud data recording unit that records a single exploration indicator data using the anchor point time slice structure as an index. S3. Construct a three-domain quality state machine in the cloud based on anchor point time slice cloud data units, determine the quality status and perform state transition for each anchor point time slice cloud data unit of the same exploration anchor point, generate quality locking index and anchor point quality evolution trajectory, and write the anchor point time slice cloud data units into the storage area corresponding to different quality levels according to the quality status. Among them, the three-domain quality state machine is a quality state modeling structure composed of three quality domains: the acquisition link domain, the equipment state domain, and the operating condition semantic domain, as well as their corresponding quality states and state transition relationships. S4. Based on the quality locking index and anchor point quality evolution trajectory, the anchor point time slice is filtered, the quality status is filtered, and the storage area is located for the exploration cloud data query request, and the exploration cloud data query results are generated.

[0017] In this embodiment S1, the exploration anchor point identifier is used to identify the exploration spatial object and the exploration location segment; the anchor point event template is a predefined set of event descriptions for exploration anchor point related events, and the fields of the anchor point event template include an event type field, an event triggering condition field, and an event priority field.

[0018] In this embodiment S1, before the project commences, the exploration management terminal first establishes exploration project information records. The exploration project information includes project number, project name, construction unit, exploration route or area name, exploration object type, and exploration site planning information, etc. During the exploration execution process, raw exploration data is continuously received. The raw exploration data includes drilling process parameters, sampling records, in-situ test values, test loading and unloading parameters, and operation logs and diagnostic data related to the IoT acquisition terminal. The raw exploration data can be automatically uploaded through the IoT acquisition terminal, or it can be manually entered or imported in batches through the on-site operation terminal. In this embodiment, a unified record structure is used to manage the exploration project information and raw exploration data. The encoding and storage methods can be implemented using existing data management technologies. The steps of constructing exploration anchor point identifiers and configuring anchor point event templates are completed based on existing data encoding and rule configuration technologies.

[0019] In this embodiment S1, the exploration anchor point is a spatial reference object within the same exploration project, where a specific exploration spatial object and its individual location segments are used as one-to-one data index units. It is uniquely represented by a combination of project identifier, exploration work point identifier, and borehole segment identifier, serving as the binding basis for original exploration data, time-slice structures, and cloud data records on the timeline and in space. The exploration anchor point identifier is constructed based on exploration project information and exploration work point planning information. It consists of three fields: project identifier, exploration work point identifier, and borehole segment identifier. The project identifier uniquely identifies an exploration project object and can be formed by the project number or a combination of the project number and project stage code. The exploration work point identifier is used to mark... The project identifier identifies specific spatial objects in the exploration project, such as a borehole, a test point, or a typical work point on a line segment. These can be coded using combinations of line name and station number, or area name and grid number. The borehole segment identifier identifies specific location segments under the exploration work point, such as a depth range, a lithological segment, or a test segment. These can be coded using methods such as "starting depth – ending depth" or "stratum number". The project identifier, exploration work point identifier, and borehole segment identifier are sequentially or segmentally coded according to a preset field order to form a unique exploration anchor point identifier within the entire exploration project scope. This allows each original exploration data record to establish a one-to-one correspondence with the corresponding spatial object and location segment through the exploration anchor point identifier.

[0020] In this embodiment S1, the anchor point event template is configured based on the exploration process flow. The anchor point event template predefines a set of event description fields for typical events related to exploration anchor points, including an event type field, an event trigger condition field, and an event priority field. The event type field is used to distinguish different categories of exploration events, including hole opening, drilling, drilling stop, sampling, in-situ test start, in-situ test end, test loading start, and test loading end. The value of the event type field corresponds to the exploration process type. The event trigger condition field is used to describe the judgment conditions for the occurrence of the event. The trigger conditions are based on one or more exploration indicators such as drilling pressure, rotation speed, torque, flow rate, displacement, and test instrument readings uploaded by the IoT acquisition terminal. The target threshold conditions, change rate conditions, or duration conditions can be configured. For example, when the drilling pressure and rotation speed are simultaneously stable within a preset range and continue for more than a preset time period, it is determined as a "stable drilling" event. The event priority field is used to solve the conflict handling problem when multiple event triggering conditions are met simultaneously within the same time interval. The event priority is identified by the value or level order, which is used to select the priority event to retain when determining the event boundary in the future. The anchor point event template is stored in the cloud or edge management node in the form of a configuration table or rule base, and is associated with the corresponding set of exploration anchor point identifiers according to the project identifier or exploration point identifier. When deployed on site, the corresponding anchor point event template is synchronized to the IoT acquisition terminal or edge processing node through the configuration distribution mechanism.

[0021] In this embodiment S1, the event type field is used to distinguish at least one exploration anchor point event among the hole opening event, drilling event, drilling stop event, sampling event, test loading event, and test unloading event; the event triggering condition field is set based on the change pattern or threshold condition of one or more exploration indicators in the original exploration data; the event priority field is used to distinguish the priority relationship between different exploration anchor point events.

[0022] In this embodiment S1, the event boundary detection is a process of continuously monitoring the original survey data based on the anchor point event template, and determining the start and end times of the survey anchor point event when the event triggering conditions are met to obtain the event boundary; the anchor point time slice structure is used to define the time slices divided by the event boundaries on the time axis; the event boundary includes time boundary pairs formed by the start and end times of adjacent survey anchor point events; wherein, the time boundary pairs further serve as the basis for pre-setting event boundary priority rules, maximum no-event duration threshold, and fixed time slice supplementary rules.

[0023] In this embodiment S1, event boundary detection takes the original exploration data stream as input and performs continuous monitoring according to the configured anchor point event template. During continuous monitoring, data records belonging to the same exploration anchor point identifier are sorted according to the collection time. Exploration index data is read one by one using a streaming processing method. The current value, trend of change, and duration of each exploration index are judged according to the event trigger condition field. When the event trigger condition corresponding to a certain event type is met, the current collection time is determined as the start time of the event. When the event trigger condition is no longer met or the corresponding end condition is met, the corresponding collection time is determined as the end time of the event. For cases where multiple exploration indicators come from different sensor channels, time alignment is performed by sampling clock or unified timestamp to map the multi-channel data onto the time axis of the exploration anchor point to ensure that event boundary detection is performed based on a unified time reference. Event boundary detection can be performed on IoT acquisition terminals, nearby edge computing nodes, or cloud processing nodes. The specific deployment method is selected according to the system architecture. In this embodiment, event boundary detection is performed centrally on edge computing nodes. The matching of anchor point event templates and the determination of event start and end times are realized through a rule engine or streaming processing framework.

[0024] In this embodiment S1, for each exploration anchor point identifier, an event boundary sequence is maintained in chronological order. The start time and end time of each exploration anchor point event are paired to form a time boundary pair corresponding to the event, and a time boundary pair sequence is formed in chronological order. When multiple event triggering conditions are met simultaneously within the same time period, a decision is made based on the event priority field, and only the event type with higher priority is retained as the source of the time boundary pair to avoid interference from repeated or overlapping events on the time slice division. To reduce the frequent event opening and closing phenomenon caused by small fluctuations in exploration indicators, a minimum duration threshold and a jitter filtering strategy are introduced in the event boundary detection process. The event start time is confirmed only when the duration of the event triggering condition exceeds the minimum duration threshold, and the event end time is confirmed when the triggering condition is interrupted for more than a preset time. Stable event start and end times are obtained in the above way, and these start and end times are used as the basis for subsequent time slice division.

[0025] In this embodiment S1, the anchor point time slice structure is used on the time axis to define time slices obtained by dividing event boundaries. For each exploration anchor point identifier, the time axis is divided into several time slice segments according to the time boundary pair sequence. Initially, the start and end times of adjacent time boundary pairs are used as the boundaries of adjacent time slices, generating an anchor point time slice structure record with exploration anchor point identifier, time slice start time, and time slice end time. Based on the event boundary division, to handle segments with long periods without events, a maximum event-free duration threshold and fixed time slice supplementation rules are pre-set based on the time boundary pairs. The time interval between adjacent time boundary pairs is calculated. When the time interval is less than the maximum event-free duration threshold, the boundary formed by adjacent time boundaries is used as the basis for time slice division. When the time interval is greater than or equal to the maximum event-free duration threshold, the time interval is divided according to a preset fixed time length, splitting the long segment into multiple time slices. Each time slice generates a corresponding anchor point time slice structure. In addition to the exploration anchor point identifier and the start and end times of the time slice, the anchor point time slice structure may also include the time slice number, the time slice length, and the index fields required for association with subsequent context information, providing a unified time slice index basis for subsequently associating the original exploration data with the time slice context information and generating anchor point time slice cloud data units.

[0026] In this embodiment S1, the event boundary priority rule means that when the time interval between adjacent exploration anchor point events is less than the maximum event-free duration threshold, only the corresponding time boundary pair is used as the time slice division boundary to generate the anchor point time slice structure. The maximum event-free duration threshold means the maximum continuous time length on the time axis that allows no exploration anchor point events to occur. The fixed time slice supplement rule means that when the time interval between adjacent exploration anchor point events is greater than or equal to the maximum event-free duration threshold, multiple anchor point time slice structures are generated within this time interval according to a preset fixed duration.

[0027] In this embodiment S2, the context information within each time slice is a collection of time slice context fields. The time slice context fields include the spatial location field of the survey location, the process field of the survey process type, the device status field of the IoT acquisition terminal, and the link diagnosis field of the acquisition link. The time slice context fields are associated with the corresponding anchor time slice structure and are shared by all anchor time slice cloud data units within that time slice.

[0028] In this embodiment S2, for the anchor point time slice structure, the original exploration data under the same exploration anchor point identifier are sorted according to the acquisition time. The original exploration data whose acquisition time falls within the time interval between the start time and the end time of a certain anchor point time slice structure are included in the data set to which the time slice belongs. The original exploration data are segmented and aggregated by the exploration anchor point identifier and the start and end time of the time slice carried by the anchor point time slice structure. After completing the time slice-level aggregation, a time slice context field set is constructed for each time slice, so that all the original exploration data in the time slice can be uniformly managed under the same spatial location, process status, equipment status and link status description.

[0029] In this embodiment S2, the time slice context field set is generated in units of anchor point time slice structures. The spatial location field is parsed based on the project identifier, exploration work point identifier, and borehole segment identifier in the exploration anchor point identifier. The project's line or region, work point coordinate information, borehole number, and borehole segment depth range are encoded into spatial location description data. The process field is derived by consulting the exploration and construction process plan or by inferring the exploration process type corresponding to the current time slice based on the event type record within the time slice. Drilling, drilling stop, sampling, in-situ testing, loading, unloading, and other processes are uniformly mapped into standard process codes. The equipment status field is generated based on the self-test results and operation logs of the IoT acquisition terminal within the corresponding time slice. The terminal power status, sensor connection status, and self-test alarm status are summarized into equipment operation status codes. The link diagnosis field is based on the communication logs of the acquisition link within the time slice to count diagnostic indicators such as the number of link interruptions, reconnections, round-trip delay, and data retransmission ratio. The diagnostic results are graded and encoded to form link diagnosis description data that can reflect the communication quality status within the time slice.

[0030] In this embodiment S2, the time slice context field set is bound to the anchor time slice structure through the field reference relationship within the anchor time slice structure. The anchor time slice structure is set with a context index field for indexing the time slice context field set or directly embeds storage space location field, process field, equipment status field and link diagnosis field. When generating anchor time slice cloud data units from the original survey data within the time slice, the anchor time slice cloud data units automatically inherit the time slice context field set by referencing the anchor time slice structure. This allows anchor time slice cloud data units corresponding to different survey indicators within the same time slice to share completely consistent spatial location description, process type description, equipment operating status description and acquisition link diagnosis description, thereby forming a unified context environment at the time slice level.

[0031] In this embodiment S2, the anchor point time slice cloud data unit is recorded as a single indicator granularity record, recording the data content of a single exploration indicator. It establishes a reference relationship with the corresponding anchor point time slice structure through the index field of the anchor point time slice structure to share the time slice context field set. The cloud temporary storage area is a data storage area located in the cloud, used to store anchor point time slice cloud data units generated based on the anchor point time slice structure that have not yet been written to the storage area corresponding to different quality levels according to the quality status. The cloud temporary storage area also groups and stores the anchor point time slice cloud data units according to the exploration anchor point identifier and the start and end time of the time slice, forming a sequence of anchor point time slice cloud data units of the same exploration anchor point on the time axis.

[0032] In this embodiment S2, after the original exploration data is aggregated into segments according to the anchor point time slice structure and a time slice context field set is generated, the data in each time slice is grouped according to the exploration index type. Representative values ​​of different exploration indices such as drilling pressure, rotation speed, torque, pump pressure, flow rate, borehole diameter, loading force, and displacement reading are statistically analyzed within the time slice. For example, the average value, extreme value, or other statistical quantity of the index within the time slice is calculated. Based on each index group, a separate anchor point time slice cloud data unit is generated. Each anchor point time slice cloud data unit records only the statistical result or characteristic value of one exploration index within the time slice, and records the exploration index type identifier, statistical method identifier, and necessary unit information, so that the anchor point time slice cloud data unit maintains a single index granularity in data modeling.

[0033] In this embodiment S2, the anchor time slice cloud data unit establishes a reference relationship with the corresponding anchor time slice structure through the index field of the anchor time slice structure. The anchor time slice structure provides index fields such as the survey anchor point identifier, time slice start time, time slice end time, and time slice number. The anchor time slice cloud data unit sets the corresponding index field value to point to the anchor time slice structure to which it belongs. The anchor time slice cloud data unit does not repeatedly store spatial location fields, process fields, equipment status fields, and link diagnosis fields. Instead, it indirectly associates with the anchor time slice structure through the index field and accesses the time slice context field set, forming a one-to-many reference relationship. That is, one anchor time slice structure corresponds to multiple anchor time slice cloud data units with different survey indicators. Each anchor time slice cloud data unit forms a stable reference link with the anchor time slice structure through the index field.

[0034] In this embodiment S2, the cloud temporary storage area is constructed in the cloud-side data storage system in the form of logical partitions or independent data tables. It is used to receive and store anchor time slice cloud data units generated based on the anchor time slice structure that have not yet been divided into different quality level storage areas according to the quality status. When the anchor time slice cloud data units are written to the cloud temporary storage area, they are grouped according to the exploration anchor point identifier. Within the group, they are arranged and stored sequentially according to the start time of the time slice or the time slice number. The cloud temporary storage area establishes a composite index through the exploration anchor point identifier and the start and end time of the time slice, so that the anchor time slice cloud data units corresponding to the same exploration anchor point form a data sequence arranged in ascending order along the time axis in the storage structure. For each exploration anchor point, an anchor time slice cloud data unit sequence composed of multiple time slices connected in sequence is generated, and the index integrity between the cloud data units corresponding to different exploration indicators in each time slice is maintained in the sequence.

[0035] In this embodiment S3, the three-domain quality state machine is used to determine the quality status of each anchor point time slice cloud data unit of the same exploration anchor point based on the acquisition link domain, equipment status domain, and working condition semantic domain, and organizes the three-domain quality status information on the time axis. The input sources of the three-domain quality state machine include the link diagnosis field, equipment status field, and exploration index data associated in the anchor point time slice cloud data unit. The three-domain quality state machine outputs a three-domain quality status result for each anchor point time slice cloud data unit, which includes the quality status of the acquisition link domain, the quality status of the equipment status domain, and the quality status of the working condition semantic domain.

[0036] In this embodiment S3, for the same exploration anchor point, after writing the anchor point time slice cloud data unit into the cloud temporary storage area, all anchor point time slice cloud data units corresponding to the exploration anchor point are organized according to the start time of the time slice to construct a time-ordered time slice data sequence. The three-domain quality state machine takes this time slice data sequence as input, extracts the link diagnosis field, equipment status field, and exploration index data from each anchor point time slice cloud data unit, and maps the link diagnosis field to the acquisition link field, the equipment status field to the equipment status field, and the exploration index data to the working condition semantic field according to the division of the acquisition link field, equipment status field, and working condition semantic field. The three-domain quality state judgment processing is performed for each time slice, and the quality state result record containing the acquisition link quality state, equipment operation quality state, and working condition semantic quality state is output on each time slice. The above quality state results are organized into a three-domain quality state sequence arranged in ascending order of time axis according to the time slice order.

[0037] In this embodiment S3, the quality status determination of the acquisition link domain is based on communication indicators such as the number of link interruptions, reconnections, round-trip time, packet loss ratio, and retransmission count recorded in the link diagnostic field. Through preset link diagnostic rules, these communication indicators are compared with corresponding threshold ranges to classify the link status into quality status categories such as normal link, slight link fluctuation, severe link fluctuation, and link interruption. The quality status determination of the device status domain is based on operational information such as power status, self-test results, sensor connection status, device temperature status, and alarm flags recorded in the device status field. Through a preset set of device status rules, the combination patterns of the device status fields are mapped to quality status categories such as normal device operation, suspicious device operation, abnormal device operation, and device failure. This allows for... The equipment fault quality status is maintained within a continuous time slice when the equipment fault is not resolved. The quality status determination of the working condition semantic domain is based on the statistical characteristics of the survey index data within the time slice and the degree of deviation between the data and the reference distribution formed by the time slice cloud data units of the same anchor point in history. The mean, extreme values, fluctuation range, and trend of the survey index within the time slice are compared with the statistical interval of the historical normal working condition samples. When the survey index characteristics fall within the normal range, the working condition semantic is determined to be normal. When the survey index characteristics show a slight deviation but are still within the acceptable range, the working condition semantic is determined to be suspicious. When the survey index characteristics deviate significantly from the normal distribution or show a change pattern that does not match the process type, the working condition semantic is determined to be abnormal. When the survey index characteristics are highly consistent with the characteristics of historical fault samples, the working condition semantic is determined to be faulty.

[0038] In this embodiment S3, after completing the above three-domain quality status determination, for each time slice, the quality status of the acquisition link domain, the quality status of the equipment status domain, and the quality status of the working condition semantic domain are combined into a three-domain quality status record. Combined with the corresponding anchor point time slice cloud data unit index field, a three-domain quality status sequence structure with time slice order as the main index is constructed. In this three-domain quality status sequence structure, for the same exploration anchor point, each time slice has a unique time slice position and a corresponding three-domain quality status triplet. Through this sequence structure, the quality status evolution process of the same exploration anchor point on the time axis is explicitly expressed, providing unified quality status basic data for subsequent execution of cross-domain state constraints, state migration updates, and time slice-based quality locking with index generation and anchor point quality evolution trajectory construction.

[0039] In this embodiment S3, the acquisition link domain determines the acquisition link quality status based on the link diagnostic field associated in the anchor time slice cloud data unit, the equipment status domain determines the equipment operation quality status based on the equipment status field associated in the anchor time slice cloud data unit, and the working condition semantic domain determines the working condition semantic consistency quality status based on the survey index data in the anchor time slice cloud data unit and its statistical characteristics with historical similar anchor time slice cloud data units.

[0040] In this embodiment S3, the acquisition link domain, device state domain, and operating condition semantic domain of the three-domain quality state machine perform quality state determination and state migration through cross-domain state constraint rules and state migration priority rules. The quality state determination and state migration are based on a preset quality level set. The quality state determination maps the anchor time slice cloud data unit to the target quality level in the preset quality level set according to the input source of the three-domain quality state machine. The state migration updates the preset quality level corresponding to each anchor time slice cloud data unit on the time axis according to the cross-domain state constraint rules and state migration priority rules.

[0041] In this embodiment S3, the three-domain quality state machine performs quality state determination and state transition based on a preset quality level set. The preset quality level set includes four quality levels: normal level, suspicious level, abnormal level, and fault level. Each quality level corresponds to a different level of data reliability and usability. The normal level indicates that the data meets the working condition requirements and can be directly used for subsequent analysis and design. The suspicious level indicates that the data has slight anomalies or incomplete conditions and needs to be further verified in conjunction with adjacent time slices or other information. The abnormal level indicates that the data has obvious anomalies or does not conform to the expected working conditions and needs to be treated as a key diagnostic object and restricted to direct use in formal calculations. The fault level indicates that the data source has serious problems or the acquisition link and equipment status do not meet the requirements of the survey operation and are not used as a basis for normal analysis. The quality state determination process maps the intra-domain quality states obtained from the acquisition link domain, equipment status domain, and working condition semantic domain to the quality level identifiers in the preset quality level set, forming the quality level value corresponding to each time slice in each quality domain. Based on this, the initial comprehensive quality level of the time slice is generated according to the combination relationship of the three-domain quality states.

[0042] In this embodiment S3, cross-domain state constraint rules are used to establish constraint relationships between the quality status of the acquisition link domain, the equipment status domain, and the working condition semantic domain. When a quality domain experiences a severe quality level, the quality levels of other quality domains are forcibly downgraded or restricted. The cross-domain state constraint rules include the following constraint types: when the equipment status domain is determined to be at a fault level in a certain time slice, the quality level of that time slice in the acquisition link domain and the working condition semantic domain is restricted to no higher than the abnormal level, and the overall quality level of that time slice is directly set to the fault level; when the working condition semantic domain is determined to be at a fault level in multiple consecutive time slices at a certain survey anchor point... When an abnormal or faulty status is assigned and the data acquisition link domain remains at a normal level for an extended period, the quality level of the device status domain in adjacent time slots is adjusted from normal to suspicious to reflect the possibility of device calibration deviation or configuration issues. When a data acquisition link domain is determined to be at a faulty level in a certain time slot, the overall quality level of that time slot is directly set to the faulty level, and the anchor time slot cloud data unit of that time slot is marked as a data anomaly caused by a communication failure. Through the above cross-domain status constraint rules, serious quality problems occurring in a single quality domain can generate a linkage effect among the quality states of the three domains, thereby reflecting the causal and dependency relationships between different quality problems.

[0043] In this embodiment S3, the state transition priority rule is used to dynamically update the quality level of each time slice on the time axis. It comprehensively considers the quality state of the current time slice, the quality states of adjacent time slices, and the results of cross-domain state constraints to form a smooth and continuous quality evolution process. The state transition priority rule includes quality level severity priority and time continuity priority. The quality level severity priority stipulates that the fault level has a higher priority than the anomaly level, the anomaly level has a higher priority than the suspicious level, and the suspicious level has a higher priority than the normal level. When an isolated short-term anomaly occurs in a single time slice while multiple time slices before and after it are at the normal level and cross-domain state constraints do not cause serious problems, the overall quality of the isolated anomalous time slice can be adjusted according to the time continuity priority. The quality level is adjusted from an abnormal level to a questionable level to reduce the impact of noise fluctuations on the quality evolution results. When multiple consecutive time slices remain at a questionable or abnormal level in a certain quality domain or overall quality level, the overall quality level of this consecutive time slice segment is upgraded to a higher severity level based on the temporal continuity priority, such as from a questionable level to an abnormal level, to reflect the cumulative effect of persistent quality problems. By updating the quality levels in the preset quality level set on the time axis according to the above state transition priority rules, the final quality level of each time slice after temporal smoothing and cross-domain constraint correction is obtained, providing a stable and reliable quality status input for subsequent execution of quality level-based storage partitioning and generation of quality-locked indexes.

[0044] In this embodiment S3, the cross-domain state constraint rule includes at least the following: when the quality status of the device status domain reaches a preset fault level, the quality status of the acquisition link domain and the quality status of the operating condition semantic domain of the cloud data unit at the corresponding anchor time slice are restricted to no higher than a preset degradation level; when the quality status of the operating condition semantic domain reaches a preset severe anomaly level in multiple consecutive anchor time slices, the quality status of the device status domain in adjacent anchor time slices is corrected from a normal level to a suspicious level; the quality level set includes at least a normal level, a suspicious level, an anomaly level, and a fault level.

[0045] In this embodiment S3, the quality locking band index is a set of time slice indexes composed of the target anchor point time slice and the anchor point time slice identifiers of several anchor point time slices before and after it. It is used to identify the affected time slice segments around the target anchor point time slice during the quality status analysis and storage control process. The anchor point quality evolution trajectory is a serialized data structure composed of the time slice sequence of a single exploration anchor point and the corresponding quality status of each time slice. It is used to record the quality status change process of the exploration anchor point on the time axis. The target anchor point time slice is the anchor point time slice that meets the preset abnormal conditions in the quality status results output by the three-domain quality state machine. It is used as the time slice reference for generating the quality locking band index and identifying the center time slice during the quality status analysis and storage control process.

[0046] In this embodiment S3, after performing three-domain quality status determination, cross-domain state constraints, and state transition priority updates on the anchor time slice cloud data unit of the same exploration anchor point, based on the updated final quality level, all time slices corresponding to the exploration anchor point are arranged sequentially according to the time slice order. The anchor time slice identifier of each time slice is combined and recorded with the corresponding final quality level to construct the anchor point quality evolution trajectory. The anchor point quality evolution trajectory is represented in the data structure as a serialized data structure with the time slice order as the main index and the time slice quality status as the recorded content. This is used to completely record the quality status change process of the exploration anchor point in the time dimension and provide a unified temporal basis for subsequent quality locking index generation and quality status analysis.

[0047] In this embodiment S3, the target anchor point time slice is determined based on the final quality level of each time slice in the anchor point quality evolution trajectory and its combination relationship in the three-domain quality state. The preset abnormal conditions include at least one or more combinations of the following types: time slices with an abnormal or faulty overall quality level, time slices with an abnormal or faulty quality state in the working condition semantic domain and non-faulty quality states in the acquisition link domain and equipment status domain, time slices with a faulty quality state in the acquisition link domain or equipment status domain, and time slices marked as the center of the segment that needs to be focused on after being corrected by cross-domain status constraint rules. The time slice that meets any preset abnormal condition is determined as the target anchor point time slice, and the time slice sequence position and corresponding abnormal type identifier of the target anchor point time slice are recorded in the anchor point quality evolution trajectory as the time slice benchmark and abnormal type basis when generating the quality locking index.

[0048] In this embodiment S3, the quality locking band index is generated for each target anchor time slice. The time slice sequence position of the target anchor time slice is obtained in the anchor quality evolution trajectory. Centered on this sequence position, anchor time slices located before and after the target anchor time slice are selected according to the pre-configured number of forward expansion slices and backward expansion slices of the locking band. The anchor time slice identifiers of the target anchor time slice, as well as the anchor time slice identifiers obtained from forward expansion and backward expansion, are collected into a time slice index set. The anchor time slice identifiers in the time slice index set are sorted and deduplicated according to the time slice sequence. At the same time, the target anchor time slice and non-target anchor time slices are distinguished in the quality locking band index record, so that the quality locking band index can accurately identify the affected time slice segments around the target anchor time slice during the quality status analysis and storage control process.

[0049] In this embodiment S3, for the same exploration anchor point, when there are multiple target anchor point time slices that meet the preset anomaly conditions in the anchor point quality evolution trajectory, a corresponding quality locking band index is generated for each target anchor point time slice. Within the same anchor point range, multiple quality locking band indices generated from different target anchor point time slices are overlapped. For quality locking band indices that overlap in the time slice sequence interval, a merged quality locking band index covering the entire overlapping segment is formed by merging the time slice index sets. At the same time, all corresponding anomaly type identifiers and their target anchor point time slice sets are recorded in the merging result, so that the locking band segments corresponding to different anomaly types can be distinguished by exploration anchor point in the subsequent quality status analysis, anomaly diagnosis and storage control process, and the same time slice can be avoided being processed repeatedly.

[0050] In this embodiment S3, the preset abnormal conditions are as follows: First, at least two of the quality states of the acquisition link domain, the equipment status domain, and the working condition semantic domain output by the three-domain quality state machine reach their respective abnormal levels or fault levels on the same anchor point time slice; Second, in the anchor point quality evolution trajectory, the quality state of a certain quality domain reaches an abnormal level or fault level on a consecutive preset number of anchor point time slices.

[0051] In this embodiment S3, the quality locking band index is generated according to the preset number of forward expansion segments and backward expansion segments of the locking band. Specifically, for each target anchor time slice, the time slice sequence position of the target anchor time slice in the corresponding anchor quality evolution trajectory is obtained. Anchor time slices no more than the number of forward expansion segments of the locking band are selected forward in the time slice sequence, and anchor time slices no more than the number of backward expansion segments of the locking band are selected backward in the time slice sequence. The anchor time slice identifiers of the target anchor time slice, the forward-selected anchor time slices, and the backward-selected anchor time slices are added to the quality locking band index. The number of forward expansion segments and the number of backward expansion segments of the locking band are configurable integer parameters.

[0052] In this embodiment S3, the number of forward extension segments and the number of backward extension segments of the locking band are set as configurable integer parameters during system initialization or project configuration. They can be configured uniformly globally or configured with different parameter combinations according to the type of exploration project or anomaly. The number of forward extension segments of the locking band is used to determine the number of time slices that need to be included in the quality analysis and storage control range before the target anchor point time slice, so as to reflect the precursory change segment before the anomaly occurs. The number of backward extension segments of the locking band is used to determine the number of time slices that need to be included in the quality analysis and storage control range after the target anchor point time slice, so as to reflect the continuous segment that may have an impact after the anomaly occurs. Both the number of forward extension segments and the number of backward extension segments of the locking band are recorded in the system configuration table in the form of integer parameters and can be adjusted according to engineering experience or trial operation results.

[0053] In this embodiment S3, the generation of the quality locking band index is based on the anchor point quality evolution trajectory as a time series. For each target anchor point time slice, the time slice sequence position index value corresponding to the target anchor point time slice is first retrieved in the anchor point quality evolution trajectory. Then, the forward start position index and the backward end position index are calculated according to the number of forward expansion slices and the number of backward expansion slices of the locking band, respectively. When the forward start position index is less than the first time slice sequence position of the anchor point quality evolution trajectory, the forward start position index is truncated to the first time slice sequence position of the trajectory. When the backward end position index is greater than the last time slice sequence position of the anchor point quality evolution trajectory, the backward end position index is truncated to the last time slice sequence position of the trajectory. After completing the above boundary truncation, the entire time slice sequence position within the range of the corrected forward start position index to the backward end position index is used as the traversal interval. The anchor point time slice identifiers of each time slice in the interval are obtained in turn. These anchor point time slice identifiers are added to the time slice index set corresponding to the quality locking band index, while maintaining the order of the time slice sequence in the time slice index set.

[0054] In this embodiment S3, the quality lock band indexes generated for all target anchor point time slices within the same exploration anchor point range are managed uniformly. When writing or updating the quality lock band index, overlap detection is performed on lock bands belonging to the same anchor point and whose index intervals partially or completely overlap. Overlap detection determines the overlapping interval by comparing the forward start position index and the backward end position index of different lock bands. A merging strategy is adopted for the detected overlapping lock bands, and the time slice index sets of overlapping lock bands are combined to form continuous or segmented time slice index intervals. At the same time, the corresponding target anchor point time slice set and anomaly type identifier set are merged to generate a merged quality lock band index record, so as to avoid repeatedly maintaining multiple lock band index entries for the same time slice and maintain the compactness and consistency of the quality lock band index structure.

[0055] In this embodiment S3, the quality locking band index establishes a reference relationship with the storage areas corresponding to different quality levels and the subsequent exploration cloud data query process. The quality locking band index is used to limit the time slice segments that need to be focused on during the quality status analysis and storage control process. When the anchor point time slice cloud data unit is written to the storage area corresponding to different quality levels according to the final quality level, the target anchor point time slice and its forward and backward extended time slices can be marked with special tags or index entries in the corresponding quality level storage area according to the quality locking band index to support the rapid retrieval of anomaly-related segments in the future. At the same time, in the subsequent exploration cloud data query requests initiated for exploration anchor points or specific anomaly types, the quality locking band index can be used as a constraint condition for time slice filtering, limiting the query range to the time slice interval covered by the locking band, thereby realizing centralized analysis and retrieval of cloud data records related to the target anchor point time slice and its affected time slice segments.

[0056] In this embodiment S4, the cloud receives a data query request from the exploration cloud. It parses the query conditions in the request, such as project identifier, exploration anchor point range, query time range, exploration indicator type set, and quality level constraints, into a standardized set of query condition fields. Based on the project identifier, it determines the corresponding anchor point quality evolution trajectory set and quality locking index set. Under the constraints of the exploration anchor point range and query time range, it selects the target anchor point set and its corresponding anchor point quality evolution trajectory subsequence and quality locking index subsequence for subsequent filtering. For each target exploration anchor point, it first selects a time within the query time range based on its anchor point quality evolution trajectory subsequence. The set of base anchor time slices whose slice order positions fall within the query time range is determined. Then, the quality-locked band index subset is used to determine whether the query request requires the inclusion of the target anchor time slice and its affected time slice segments. When the query request carries an extended query flag based on the quality-locked band, all anchor time slice identifiers covered in the quality-locked band index that have an intersection or inclusion relationship with the base anchor time slice set are added to the candidate anchor time slice set. When the query request does not carry an extended query flag, only the base anchor time slice set is retained as the candidate anchor time slice set. After completing the above time range expansion or limitation, the candidate anchor time slice set is deduplicated and sorted by time slice order.

[0057] In this embodiment S4, after obtaining the candidate anchor point time slice set for each target exploration anchor point, quality status filtering is performed based on the final quality level of each time slice recorded in the anchor point quality evolution trajectory subsequence. The final quality level of each candidate anchor point time slice is compared with the quality level constraints in the query condition field set. When the query request specifies that only normal and suspicious level data are received, only anchor point time slices with a final quality level of normal or suspicious are retained. When the query request specifies that abnormal or fault level data are included, anchor point time slices that meet the constraints are retained, and anchor point time slices that do not meet the quality level constraints are discarded, thereby obtaining the target anchor point time slice set under the dual constraints of time range and quality level. For each anchor point time slice in the target anchor point time slice set, according to its The final quality level determines the corresponding quality level storage area type. Combined with the exploration anchor point identifier and time slice start and end times carried in the anchor point time slice structure, as well as the index fields carried in the anchor point time slice cloud data units, all anchor point time slice cloud data unit records belonging to that anchor point time slice are located within the corresponding quality level storage area. For each target exploration anchor point, the located anchor point time slice cloud data units are aggregated and sorted according to exploration index type in time slice order, forming a time-ordered result sequence with exploration anchor point identifier, time slice start and end times, exploration index data, and its quality level identifier. At the project level, the time-ordered result sequences of all target exploration anchor points are summarized to form the exploration cloud data query results corresponding to the exploration cloud data query request, which are returned to the upper-level exploration business system or design system for invocation.

[0058] Example 2: The present invention proposes a real-time data acquisition and management system for surveying IoT cloud data, which is applied to the real-time data acquisition and management method for surveying IoT cloud data proposed in Example 1. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the real-time data acquisition and management method for surveying IoT cloud data in Example 1.

[0059] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for real-time acquisition and management of IoT cloud data, characterized in that, Includes the following steps: S1. Obtain exploration project information and raw exploration data, construct exploration anchor point identifiers and configure anchor point event templates, control the IoT acquisition terminal to perform event boundary detection, and generate anchor point time slice structures according to event boundaries; Among them, the exploration anchor point identifier is a combination of identifier fields consisting of project identifier, exploration work point identifier, and borehole segment identifier; the anchor point time slice structure is a data record structure that identifies the time interval attribute of the exploration anchor point in a single time slice, using the exploration anchor point identifier and the start and end time of the time slice as indexes. S2. Based on the anchor point time slice structure, associate the original exploration data and context information in each time slice, generate anchor point time slice cloud data units for each exploration indicator in the time slice, and write the anchor point time slice cloud data units into the cloud temporary storage area. Among them, the anchor point time slice cloud data unit is a cloud data recording unit that records a single exploration indicator data using the anchor point time slice structure as an index. S3. Construct a three-domain quality state machine in the cloud based on anchor point time slice cloud data units, determine the quality status and perform state transition for each anchor point time slice cloud data unit of the same exploration anchor point, generate quality locking index and anchor point quality evolution trajectory, and write the anchor point time slice cloud data units into the storage area corresponding to different quality levels according to the quality status. In S3, the quality locking band index is a set of time slice indexes composed of the target anchor point time slice and the anchor point time slice identifiers of several anchor point time slices before and after it. It is used to identify the affected time slice segments around the target anchor point time slice during the quality status analysis and storage control process. The anchor point quality evolution trajectory is a serialized data structure composed of the time slice sequence of a single exploration anchor point and the corresponding quality status of each time slice. It is used to record the quality status change process of the exploration anchor point on the time axis. The target anchor point time slice is the anchor point time slice that meets the preset abnormal conditions in the quality status results output by the three-domain quality state machine. It is used as the time slice reference for generating the quality locking band index and identifying the center time slice during the quality status analysis and storage control process. Among them, the three-domain quality state machine is a quality state modeling structure composed of three quality domains: the acquisition link domain, the equipment state domain, and the operating condition semantic domain, as well as their corresponding quality states and state transition relationships. S4. Based on the quality locking index and anchor point quality evolution trajectory, the anchor point time slice is filtered, the quality status is filtered, and the storage area is located for the exploration cloud data query request, and the exploration cloud data query results are generated.

2. The method for real-time acquisition and management of IoT cloud data according to claim 1, characterized in that: In S1, the exploration anchor point identifier is used to identify the exploration spatial object and the exploration location segment; the anchor point event template is a predefined set of event descriptions for exploration anchor point related events, and the fields of the anchor point event template include event type field, event triggering condition field and event priority field.

3. The method for real-time acquisition and management of IoT cloud data according to claim 2, characterized in that: In S1, the event boundary detection is a process of continuously monitoring the original survey data based on the anchor point event template, and determining the start and end times of the survey anchor point event when the event triggering conditions are met to obtain the event boundary; the anchor point time slice structure is used to define the time slices divided by the event boundaries on the time axis; the event boundary includes time boundary pairs formed by the start and end times of adjacent survey anchor point events; wherein, the time boundary pairs further serve as the basis for pre-setting event boundary priority rules, maximum no-event duration threshold, and fixed time slice supplementary rules.

4. The method for real-time acquisition and management of IoT cloud data according to claim 3, characterized in that: In S2, the context information within each time slice is a collection of time slice context fields. The time slice context fields include the spatial location field of the survey location, the process field of the survey process type, the device status field of the IoT acquisition terminal, and the link diagnosis field of the acquisition link. The time slice context fields are associated with the corresponding anchor time slice structure and are shared by all anchor time slice cloud data units within that time slice.

5. The method for real-time acquisition and management of IoT cloud data according to claim 4, characterized in that: In S2, the anchor point time slice cloud data unit serves as a single-index granularity record, recording the data content of a single exploration index. It establishes a reference relationship with the corresponding anchor point time slice structure through the index field of the anchor point time slice structure to share the time slice context field set. The cloud temporary storage area is a data storage area located in the cloud, used to store anchor point time slice cloud data units generated based on the anchor point time slice structure that have not yet been written to the storage areas corresponding to different quality levels according to their quality status. The cloud temporary storage area also groups and stores the anchor point time slice cloud data units according to the exploration anchor point identifier and the start and end time of the time slice, forming a sequence of anchor point time slice cloud data units for the same exploration anchor point on the time axis.

6. The method for real-time acquisition and management of IoT cloud data according to claim 5, characterized in that: In S3, the three-domain quality state machine is used to determine the quality status of each anchor point time slice cloud data unit of the same exploration anchor point based on the acquisition link domain, equipment status domain, and working condition semantic domain, and organizes the three-domain quality status information on the time axis. The input sources of the three-domain quality state machine include the link diagnosis field, equipment status field, and exploration index data associated in the anchor point time slice cloud data unit. The three-domain quality state machine outputs a three-domain quality status result for each anchor point time slice cloud data unit, which includes the quality status of the acquisition link domain, the quality status of the equipment status domain, and the quality status of the working condition semantic domain.

7. The method for real-time acquisition and management of IoT cloud data according to claim 6, characterized in that: The three-domain quality state machine uses cross-domain state constraint rules and state transition priority rules to determine the quality state and perform state transitions in the acquisition link domain, device state domain, and operating condition semantic domain of the three-domain quality state machine. Quality status determination and state transition are implemented based on a preset quality level set. Quality status determination maps anchor time slice cloud data units to target quality levels in the preset quality level set according to the input source of the three-domain quality state machine. State transition updates the preset quality level corresponding to each anchor time slice cloud data unit on the time axis according to cross-domain state constraint rules and state transition priority rules.

8. The method for real-time acquisition and management of IoT cloud data according to claim 7, characterized in that: In step S3, the quality locking band index is generated according to the preset number of forward expansion segments and backward expansion segments of the locking band. Specifically, for each target anchor time slice, the time slice sequence position of the target anchor time slice in the corresponding anchor quality evolution trajectory is obtained. Anchor time slices no more than the number of forward expansion segments of the locking band are selected forward in the time slice sequence, and anchor time slices no more than the number of backward expansion segments of the locking band are selected backward in the time slice sequence. The anchor time slice identifiers of the target anchor time slice, the forward-selected anchor time slices, and the backward-selected anchor time slices are added to the quality locking band index. The number of forward expansion segments and the number of backward expansion segments of the locking band are configurable integer parameters.

9. A real-time data acquisition and management system for surveying IoT cloud, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the real-time acquisition and management method for IoT cloud data as described in any one of claims 1-8.

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