Multi-channel information collaborative access system based on high-concurrency water information service gateway

Abstract

The application relates to the technical field of industrial Internet of Things, in particular to a multi-channel information collaborative access system based on a high-concurrency water affair information service gateway, which comprises a water affair gateway and an access module; the water affair gateway is used for receiving water affair data of Internet of Things nodes and supplementing and recording data uploaded by a mobile terminal after the Internet of Things nodes are disconnected; the water affair gateway is provided with a temporary storage library, a fault library and a standard library; the temporary storage library stores water affair data of all Internet of Things nodes in a previous time slice, the fault library stores a water affair data interval corresponding to a sudden fault, and the standard library stores a water affair data standard interval of the Internet of Things nodes at different time slices in a single day; the access module determines the uploading mode of the supplementing and recording data of all Internet of Things nodes by comparison with the temporary storage library, the fault library and the standard library; for the Internet of Things disconnection situation, the high-concurrency data processing capacity of the system is ensured, and the data processing efficiency can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) technology, and in particular to a multi-channel information collaborative access system based on a high-concurrency water information service gateway. Background Technology

[0002] The Industrial Internet of Things (IIoT) is developing rapidly, and IoT technology is also being widely applied in the water sector. Currently, water data is mostly collected by deploying edge-side water gateways to receive real-time data from IoT nodes (such as flow meters and water quality sensors), and then forwarded to the cloud platform via protocols such as MQTT / CoAP.

[0003] For example, Chinese patent application publication number CN105892364A discloses a centralized water management system, including: TCP series water data acquisition and control intelligent terminals, NC series water data acquisition and control intelligent terminals, IO series water data acquisition and control intelligent terminals, a water data receiving gateway device, a water data acquisition gateway device, a remote monitoring system, and a centralized water measurement and control gateway device. The centralized water measurement and control gateway device receives control commands and converts them into corresponding control strategies, which are then distributed to each data acquisition and control intelligent terminal to achieve remote control. It employs dedicated embedded intelligent terminal devices and data gateway devices to implement a hierarchical data acquisition system, making it suitable for various network environments and capable of collecting data from various systems, equipment, and instruments.

[0004] However, the above-mentioned technical solutions cannot provide timely feedback to avoid further accidents in the event of IoT disconnection. In high-concurrency, multi-node IoT disconnection scenarios, multiple maintenance steps are required before recovery, affecting the stability of information reception and the security of water services. Summary of the Invention

[0005] To address this, the present invention provides a multi-channel information collaborative access system based on a high-concurrency water information service gateway, which overcomes the problems of existing technologies being unable to provide timely feedback to avoid further accidents in the event of IoT disconnection, and requiring multiple maintenance steps to restore service in high-concurrency, multi-node IoT disconnection scenarios, thus affecting the stability of information reception and the security of water services.

[0006] To address the aforementioned issues, this invention provides a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway. The system includes a water affairs gateway and an access module. The water affairs gateway receives water affairs data from IoT nodes and supplementary data uploaded by mobile devices after IoT nodes disconnect. The water affairs gateway is equipped with a temporary storage library, a fault library, and a standard library. The temporary storage library stores water affairs data from each IoT node within the previous time slice, the fault library stores water affairs data intervals corresponding to sudden faults, and the standard library stores standard intervals of water affairs data from IoT nodes in different time slices of a single day. The access module includes a verification unit, an analysis unit, and an upload unit.

[0007] The verification unit is used to perform pre-disconnection verification on the supplementary data of each IoT node; the analysis unit selects the following upload methods for each IoT node based on the pre-disconnection verification results:

[0008] Retain the upload mode and upload the complete supplementary data;

[0009] Simplify the upload mode, uploading only a portion of the representative data from the supplementary data;

[0010] The upload unit uploads supplementary data to the cloud platform based on the supplementary data priority and upload method;

[0011] The pre-disconnection verification includes: when uploading supplementary data, the mobile terminal inputs a water data segment of the corresponding IoT node in the time slice before the disconnection, retrieves the corresponding water data segment from the temporary storage library for comparison, and generates the accuracy of the supplementary data based on the difference between the two; and generates a supplementary data priority based on the deviation of the supplementary data from the standard interval of water data corresponding to the current time slice in the standard library, the accuracy, and the spatial representative value of the IoT node.

[0012] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the verification unit, during the pre-disconnection verification process, determines the priority of data supplementation by:

[0013] A priority score is generated based on the accuracy of the supplementary data, the degree of deviation, and the spatial representative value of the IoT node.

[0014] Based on priority scores and real-time available bandwidth, the first priority supplementary dataset is determined and uploaded immediately after determination. The first priority supplementary dataset also includes supplementary data that matches the fault database.

[0015] The remaining supplementary data is divided into several levels of upload groups according to priority scores, and each upload group is uploaded in sequence according to priority scores;

[0016] The priority score is positively correlated with the accuracy, the degree of deviation, and the spatial representative value of the IoT node.

[0017] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the verification unit includes the following process for determining the spatial representative value of IoT nodes:

[0018] Determine the distance matrix for all IoT nodes based on their geographic coordinates;

[0019] Clustering is performed using density or distance thresholds to obtain several cluster sets;

[0020] Define cluster score and cluster sparsity factor;

[0021] The spatial representative value of the IoT node is determined by weighted fusion of the cluster score and the cluster sparsity factor.

[0022] The spatial representation value of the IoT node represents the spatial representation capability of the collected data of the IoT node for the water affairs scenario.

[0023] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the verification unit is configured to execute the following process to determine the accuracy of the supplementary data:

[0024] After receiving the water data segment of the time slice before the disconnection uploaded by the mobile terminal, the verification unit retrieves the historical water data segment uploaded by the corresponding IoT node through the IoT before the disconnection from the temporary storage, and performs verification based on the data consistency of the two.

[0025] In response to the consistency verification, the degree of trend matching is judged by comparing the consistency of the direction, magnitude and rate of change of the supplementary data with the historical water data.

[0026] The verification unit generates a corresponding accuracy grading result based on the data consistency and the degree of trend matching, wherein accuracy is positively correlated with both data consistency and the degree of trend matching.

[0027] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the analysis unit is configured with the following upload mode determination process:

[0028] In response to an accuracy greater than or equal to a threshold, the reserved upload mode is selected;

[0029] If the accuracy is less than the threshold, the simplified upload mode is selected.

[0030] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the analysis unit, in the simplified upload mode, selects water affairs data segments that continuously exceed the standard range of water affairs data as partial representative data for a single supplementary data entry.

[0031] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the temporary storage of the water affairs gateway uses a ring queue structure to store water affairs data.

[0032] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the upload unit establishes a long connection channel with the cloud platform through the MQTT protocol, and uses binary compression encoding to transmit some representative data in the simplified upload mode.

[0033] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the verification unit adds HMAC-SHA256 signature verification to the water affairs data fragments input by the mobile terminal before disconnection, and triggers the mobile terminal re-authentication and data input process when the data consistency verification is inconsistent or the signature authentication is inconsistent.

[0034] As a preferred technical solution for a multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, the fault database of the water affairs gateway uses a key-value pair structure to store water affairs data ranges corresponding to sudden faults.

[0035] Compared with existing technologies, the advantages of this invention lie in the fact that, through the system configuration of mobile terminal data entry, on-site personnel can perform data entry via mobile terminals when the Internet of Things (IoT) is down. Simultaneously, to avoid erroneous data entry, on-site personnel input data segments from the previous time slice for identity and location verification, preventing invalid data entry from affecting the cloud platform when personnel are absent. This also effectively prevents malicious attacks and data interference to the water system through the data entry port, significantly improving system robustness. Furthermore, in the event of multiple nodes simultaneously going offline, to avoid fluctuations in the working status of the gateway and cloud platform, multi-level partitioning and priority analysis of the upload method ensures that critical data is uploaded in a timely manner, guaranteeing high consistency and reliability of the data received by the cloud platform. This provides a solid data foundation for subsequent water data analysis and decision-making. The system ensures high-concurrency data processing capabilities and effectively improves data processing efficiency, meeting the needs of large-scale water facility management and monitoring, and has broad application prospects.

[0036] Furthermore, this invention constructs a highly reliable water data supplementation channel through a three-database collaborative architecture of the water gateway and a dynamic verification and transmission mechanism of the access module. It utilizes a temporary storage database to provide real-time historical data benchmarks, enabling cross-verification between the supplemented data and the state before the disconnection. By establishing a two-dimensional data evaluation framework through a fault database / standard database, it provides an effective and objective basis for transmission decisions.

[0037] In particular, this invention analyzes the spatial location of IoT nodes to obtain the spatial representativeness of IoT nodes, characterizing the spatial representation capability of the collected data of IoT nodes for the water affairs scenario, and determining the monitoring image area in the water affairs scenario if the collected data is lost or stopped being transmitted. The priority analysis of the spatial representativeness of nodes has higher scenario adaptability and avoids the waste of priority for supplementary data and the redundancy of computing power. Attached Figure Description

[0038] Figure 1 This is a structural block diagram of the multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to the present invention. Detailed Implementation

[0039] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0040] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0041] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0042] Please see Figure 1 The diagram shown is a structural block diagram of the multi-channel information collaborative access system based on a high-concurrency water information service gateway according to the present invention. The system includes a water gateway and an access module. The water gateway receives water data from IoT nodes and supplementary data uploaded by mobile devices after IoT nodes disconnect. The water gateway has a temporary storage library, a fault library, and a standard library. The temporary storage library stores water data from each IoT node within the previous time slice (60 minutes in this embodiment), the fault library stores water data intervals corresponding to sudden faults, and the standard library stores standard intervals of water data from IoT nodes in different time slices of a single day. The access module includes a verification unit, an analysis unit, and an upload unit.

[0043] The verification unit is used to perform pre-disconnection verification on the supplementary data of each IoT node; the analysis unit selects the following upload methods for each IoT node based on the pre-disconnection verification results:

[0044] Retain the upload mode and upload the complete supplementary data;

[0045] Simplify the upload mode, uploading only a portion of the representative data from the supplementary data;

[0046] The upload unit uploads supplementary data to the cloud platform based on the supplementary data priority and upload method;

[0047] The pre-disconnection verification includes: when the mobile terminal uploads the supplementary data, it inputs the water data segment of the corresponding IoT node in the time slice before the disconnection, retrieves the corresponding water data segment from the temporary storage library for comparison, and generates the accuracy of the supplementary data based on the difference between the two; and generates the supplementary data priority based on the deviation of the supplementary data from the standard interval of water data corresponding to the current time slice in the standard library, the accuracy, and the spatial representative value of the IoT node.

[0048] It should be understood that, in specific implementation, the IoT nodes may include, but are not limited to, IoT sensor nodes of various types such as water quality sensors, flow sensors, water pressure sensors, liquid level sensors, temperature sensors, turbidity sensors, residual chlorine sensors, and conductivity sensors. In scenarios where the IoT node goes offline, the supplementary data uploaded by the mobile terminal and the water data normally received through the IoT both include the following information items: the unique identifier of the IoT node, the time slice number corresponding to the supplementary data, and the original water parameter values ​​collected by each sensor. For the supplementary data, it should also include: the timestamp information collected or entered by the mobile terminal, historical data segments of the time slice before the disconnection (used for pre-disconnection verification, and the historical data segments should not be too long; in this embodiment, historical data of 10 consecutive timestamps within the time slice before the disconnection are selected for pre-disconnection verification), and the identification information of the collector or the input terminal. Of course, the information items of the supplementary data can be flexibly configured under different business scenarios to support the verification and fusion of different types of water monitoring data, ensuring that the system still has data integrity and traceability during the offline period of the IoT node.

[0049] In detail, the water gateway's temporary database uses a circular queue structure to store water data. The water gateway's fault database uses a key-value pair structure to store the water data range corresponding to sudden faults.

[0050] The upload unit establishes a long-term connection channel with the cloud platform via the MQTT protocol, and uses binary compression encoding to transmit some representative data in simplified upload mode.

[0051] Specifically, during the pre-disconnection verification process, the verification unit determines the priority of data re-entry, including:

[0052] The first step is to determine the accuracy of the supplementary data:

[0053] After receiving the water data segment of the time slice before the disconnection uploaded by the mobile terminal, the verification unit retrieves the historical water data segment uploaded by the corresponding IoT node through the IoT before the disconnection from the temporary storage, and performs verification based on the data consistency of the two.

[0054] In response to the consistency verification, the degree of trend matching is judged by comparing the consistency of the direction, magnitude and rate of change of the supplementary data with the historical water data.

[0055] The validation unit generates corresponding accuracy grading results based on the degree of data consistency and trend matching, where accuracy is positively correlated with both data consistency and trend matching.

[0056] For example, when a mobile device uploads supplementary data for an IoT node, it also uploads water data segments of 10 consecutive timestamps within the time slice before the connection loss. After receiving these data segments, the verification unit retrieves historical data segments actually uploaded by the IoT device via the network within the same time slice before the connection loss from the water gateway's temporary storage, forming the basis for data comparison.

[0057] The verification unit compares the two data segments field by field. If the proportion of the different fields is within the tolerance range of ±5%, the verification is determined to be consistent. After the verification is determined to be consistent, the process of judging the degree of trend matching begins. At the same time, the verification unit adds HMAC-SHA256 signature verification to the water data segment before the disconnection input by the mobile terminal. If the data consistency verification is inconsistent or the signature authentication is inconsistent, the mobile terminal re-authentication and data input process is triggered.

[0058] Comparison of direction of change: Determine whether the growth or decline trend of the supplemented data is consistent with that of the historical data at three consecutive time points; Comparison of magnitude of change: Calculate whether the magnitude of the change in value per unit time is within a reasonable range; Comparison of rate of change: Determine whether the derivative of the supplemented data in the time series remains relatively stable with the rate of change of the historical data.

[0059] The degree of data consistency matching is converted into a consistency score C, where C is 1 when there is complete consistency and 0.9 when there are differences but they are within the tolerance range. The degree of trend matching is converted into a trend score T, where T is 1 when the direction, magnitude, and rate of change are all consistent; 0.8 when two are consistent; 0.6 when one is consistent; and 0.3 when none are consistent. The accuracy A of the supplementary data is finally determined as A = α. C+(1-α) T,α is the weighting coefficient, which is 0.6 in this embodiment.

[0060] Based on the above embodiments, the following upload mode determination process is configured for the analysis unit:

[0061] In response to an accuracy greater than or equal to a threshold (0.8 in this embodiment), the retain upload mode is selected;

[0062] If the accuracy is less than the threshold, a simplified upload mode is selected.

[0063] Furthermore, for a single piece of supplementary data, the analysis unit selects water data segments that continuously exceed the standard range of water data as partial representative data in the simplified upload mode.

[0064] The second step is to determine the degree of deviation between the supplementary data and the water resources data standard interval corresponding to the current time slice in the standard library:

[0065] In this embodiment, the standard range for water resources data is the historical data statistics of the node in the current time slice, defined by the maximum-minimum value or the upper and lower quartiles. In this embodiment, the maximum-minimum value is defined under fault-free conditions. For each water resources parameter field in the supplementary data, the verification unit determines whether it exceeds the standard range and calculates the corresponding deviation value k based on the deviation magnitude.

[0066] Where x is the field value in the supplementary data, and [L,U] is the standard range of water data. Finally, the deviation degree D is obtained by integrating each deviation value k, accumulating and normalizing it.

[0067] The third step is to determine the spatial representation value of the IoT nodes (this can be determined in advance before the system runs):

[0068] Obtain the geographic coordinates of all deployed IoT nodes, calculate the Euclidean distance between any two nodes, and form a distance matrix Dist(i,j);

[0069] The maximum distance threshold (300 meters in this embodiment) is used as the clustering criterion to spatially cluster all nodes, forming multiple cluster sets. The clustering adopts a custom threshold clustering method, which forms local neighborhoods with the maximum distance threshold as a constraint. Each cluster set represents a geographically close subset of data, which is beneficial for local water status analysis and characterization capability assessment.

[0070] Cluster score and cluster sparsity factor are defined. Cluster score is positively correlated with the degree of data fluctuation of nodes within a cluster. That is, if there is a lot of data fluctuation in a cluster, the cluster score of the nodes within that cluster will also be higher, indicating that the dynamics of the region it represents are significant. Cluster sparsity factor is negatively correlated with the node density within a cluster. The sparser the nodes, the thinner the sensor coverage in the region, and the higher the spatial representativeness of a single node. The correlation relationship that meets the requirements of this embodiment is sufficient and will not be elaborated here.

[0071] Then, the cluster score and cluster sparsity factor are weighted, fused, and normalized to obtain the spatial representative value of the IoT node.

[0072] Using the spatial clustering methods described above, the system can accurately identify nodes that are spatially representative, such as nodes located in sparse areas or nodes within clusters where water data changes drastically. When data is uploaded to supplement data after a data loss, these nodes are given higher priority, improving the robustness of the overall water monitoring network and the effectiveness of its regional coverage.

[0073] The fourth step involves generating a priority score based on the accuracy, deviation, and spatial representativeness of the IoT node in the supplementary data. The system weights and fuses the values ​​of the above three elements for each piece of supplementary data to generate a priority score, which is used to characterize its relative urgency for uploading.

[0074] Based on priority scores and real-time available bandwidth, the first priority supplementary dataset is determined (as long as the network speed is normal during the upload process of the supplementary dataset), and it is uploaded immediately after being determined. The first priority supplementary dataset also includes supplementary data that matches the fault database.

[0075] The remaining supplementary data is divided into several levels of upload groups according to priority scores, and each upload group is uploaded in sequence according to the priority scores; among them, the priority scores are positively correlated with accuracy, deviation, and the spatial representative value of IoT nodes.

[0076] In the above embodiments, the system configuration for mobile data entry allows on-site personnel to re-enter data via mobile devices when the IoT connection is down. To prevent erroneous entries, on-site personnel input data segments from the previous time slice for identity and location verification, avoiding invalid data entry when personnel are absent and its impact on the cloud platform. This also effectively prevents malicious attacks and data interference to the water system through the data entry port, significantly improving system robustness. Furthermore, to prevent fluctuations in the working status of the gateway and cloud platform when multiple nodes simultaneously fail, multi-level partitioning and priority analysis of the upload method ensures timely uploading of critical data, guaranteeing high consistency and reliability of data received by the cloud platform. This provides a solid data foundation for subsequent water data analysis and decision-making. The system ensures high-concurrency data processing capabilities and effectively improves data processing efficiency, meeting the needs of large-scale water facility management and monitoring, and has broad application prospects.

[0077] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway, characterized in that, include: The water gateway is used to receive water data from IoT nodes and supplementary data uploaded by mobile devices after IoT nodes are disconnected. The water gateway has a temporary storage library, a fault library and a standard library. The temporary storage library stores the water data of each IoT node in the previous time slice, the fault library stores the water data range corresponding to sudden faults, and the standard library stores the standard range of water data of IoT nodes in different time slices of a single day. The access module includes: The verification unit is used to verify the supplementary data of each IoT node before disconnection. The analysis unit selects the following upload methods for each IoT node based on the pre-disconnection verification results: Retain the upload mode and upload the complete supplementary data; Simplify the upload mode, uploading only a portion of the representative data from the supplementary data; The upload unit uploads supplementary data to the cloud platform based on the supplementary data priority and upload method; The pre-disconnection verification includes: when the mobile terminal uploads the supplementary data, it inputs the water data segment of the corresponding IoT node in the time slice before the disconnection, retrieves the corresponding water data segment from the temporary storage library for comparison, and generates the accuracy of the supplementary data based on the difference between the two; and generates the supplementary data priority based on the degree of deviation between the supplementary data and the standard interval of water data corresponding to the current time slice in the standard library, the accuracy, and the spatial representative value of the IoT node. The verification unit includes the following process for determining the spatial representative value of the IoT node: Determine the distance matrix for all IoT nodes based on their geographic coordinates; Clustering is performed using density or distance thresholds to obtain several cluster sets; Define cluster score and cluster sparsity factor; The spatial representative value of the IoT node is determined by weighted fusion of the cluster score and the cluster sparsity factor. Among them, the spatial representation value of the IoT node represents the spatial representation capability of the collected data of the IoT node for the water affairs scenario, the cluster score is positively correlated with the data fluctuation degree of the nodes in the cluster set, and the cluster sparsity factor is negatively correlated with the node density in the cluster set.

2. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 1, characterized in that, During the pre-disconnection verification process, the verification unit determines the priority of data re-entry, including: A priority score is generated based on the accuracy of the supplementary data, the degree of deviation, and the spatial representative value of the IoT node. Based on priority scores and real-time available bandwidth, the first priority supplementary dataset is determined and uploaded immediately after determination. The first priority supplementary dataset also includes supplementary data that matches the fault database. The remaining supplementary data is divided into several levels of upload groups according to priority scores, and each upload group is uploaded in sequence according to priority scores; The priority score is positively correlated with the accuracy, the degree of deviation, and the spatial representative value of the IoT node.

3. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 1, characterized in that, The verification unit is configured to execute the following process to determine the accuracy of the supplementary data: After receiving the water data segment of the time slice before the disconnection uploaded by the mobile terminal, the verification unit retrieves the historical water data segment uploaded by the corresponding IoT node through the IoT before the disconnection from the temporary storage, and performs verification based on the data consistency of the two. In response to the consistency verification, the degree of trend matching is judged by comparing the consistency of the direction, magnitude and rate of change of the supplementary data with the historical water data. The verification unit generates a corresponding accuracy grading result based on the data consistency and the degree of trend matching, wherein accuracy is positively correlated with both data consistency and the degree of trend matching.

4. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 3, characterized in that, The analysis unit is configured with the following upload mode determination process: In response to an accuracy greater than or equal to a threshold, the reserved upload mode is selected; If the accuracy is less than the threshold, the simplified upload mode is selected.

5. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 1, characterized in that, In the simplified upload mode, the analysis unit selects water data segments that continuously exceed the standard range of water data as the partial representative data in the simplified upload mode for a single supplementary data entry.

6. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 1, characterized in that, The temporary storage of the water gateway uses a circular queue structure to store water data.

7. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 1, characterized in that, The upload unit establishes a long-term connection channel with the cloud platform via the MQTT protocol, and transmits some representative data using binary compression encoding in the simplified upload mode.

8. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 6, characterized in that, The verification unit adds an HMAC-SHA256 signature verification to the water data fragment before the disconnection input by the mobile terminal, and triggers the mobile terminal re-authentication and data input process when the data consistency verification is inconsistent or the signature authentication is inconsistent.

9. The multi-channel information collaborative access system based on a high-concurrency water affairs information service gateway according to claim 1, characterized in that, The fault database of the water gateway uses a key-value pair structure to store the water data range corresponding to sudden faults.

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