Domestic adaptation multi-type water environment monitoring data collaborative management and sharing method

Abstract

This application provides a domestically adapted method for collaborative management and sharing of multi-type water environment monitoring data. The method includes: constructing a multi-level data collaborative management link for multiple types of water environment monitoring equipment; responding to the first request from multiple types of water environment monitoring equipment to access the multi-level data collaborative management link, calculating the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in a pre-built authentication library based on experimentally collected data frames and authentication base data; if the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in the authentication library exceeds the dynamic matching threshold for the corresponding equipment type, then the matching is successful, and the multiple types of water environment monitoring equipment are connected to the corresponding node of the multi-level data collaborative management link; performing multi-dimensional correction on the data collected by the equipment; and uploading the corrected data to the data collaborative management platform through a water environment management center. This application achieves collaborative and reliable sharing of multi-type water environment monitoring data under a domestically developed architecture.

Description

Technical Field

[0001] This application relates to the field of water environment monitoring and data management technology, and in particular to a domestically developed method for collaborative management and sharing of multiple types of water environment monitoring data. Background Technology

[0002] Water environment monitoring data is the core basis for watershed water environment management, water resource protection, and ecological environment decision-making. It encompasses various types of data, including hydrological, water quality, and ecological data, with wide-ranging data sources, diverse collection protocols, and massive data volumes, requiring efficient collaborative management and sharing mechanisms. With the rapid development of domestically produced information technology, the localization and adaptation of critical information infrastructure has become an inevitable requirement for national security and industry development. As a key system in the field of ecological environment, the localization and adaptation of water environment monitoring data management systems is urgently needed.

[0003] Existing methods for managing and sharing water environment monitoring data have several problems: First, the underlying hardware and software architecture relies on foreign products, posing data security risks, and there is a lack of domestically developed protocols for water environment monitoring data, making it difficult to unify multi-source acquisition protocols; second, the environmental impact of monitoring equipment is not fully considered during data acquisition, resulting in biased data and insufficient accuracy; third, data storage often uses centralized databases, which are insufficient to meet the storage needs of massive amounts of diverse water environment monitoring data; fourth, there are barriers to data collaboration and sharing among multiple departments, leading to low data utilization efficiency; and fifth, data management lacks the ability to provide early warnings for data anomalies and access violations.

[0004] Existing water environment monitoring data management systems often employ a single protocol stack (such as supporting only Modbus or only MQTT), requiring the deployment of separate access gateways for different types of devices, resulting in wasted hardware resources and data silos. Furthermore, current technologies typically separate device authentication from environmental monitoring, failing to consider the impact of communication stability degradation under extreme environments (such as heavy rain or high temperatures) on authentication accuracy.

[0005] Meanwhile, although existing technologies include methods for ecological and environmental data collection, hydrological data management, and big data collaborative access, such as dynamic monitoring systems for ecological and environmental restoration processes based on big data, hydrological data management methods, and smart grid heterogeneous high-concurrency big data collaborative access systems, none of these methods have been designed for domestic adaptation to the multiple types of water environment monitoring data, nor have they established a collaborative management mechanism for multi-dimensional hydrological, water quality, and ecological data, and have not achieved full-process management from data collection, correction, storage to sharing and supervision.

[0006] Therefore, the urgent technical problem to be solved is how to provide a domestically adapted method for collaborative management and sharing of multi-type water environment monitoring data, and how to solve the technical challenge that heterogeneous data from multiple types of water environment monitoring equipment cannot be collaboratively and reliably shared under the same domestic architecture due to heterogeneous communication protocols, differences in environmental impact, and differentiation in data security levels. Summary of the Invention

[0007] The purpose of this application is to provide a domestically developed method for collaborative management and sharing of multi-type water environment monitoring data, which solves the technical problem that heterogeneous data from various types of water environment monitoring equipment cannot be collaboratively and reliably shared under the same domestic architecture due to heterogeneous communication protocols, differences in environmental impact, and differentiation in data security levels.

[0008] To achieve the above objectives, this application provides a domestically developed method for collaborative management and sharing of multi-type water environment monitoring data. This method includes the following steps: constructing a multi-level data collaborative management link for multiple types of water environment monitoring equipment; responding to the first request from multiple types of water environment monitoring equipment to access the multi-level data collaborative management link, acquiring test acquisition data frames and authentication basic data from the multiple types of water environment monitoring equipment; calculating the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in a pre-built authentication library based on the test acquisition data frames and authentication basic data; if the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in the pre-built authentication library exceeds the dynamic matching threshold for the corresponding equipment type, the matching is successful, and the multiple types of water environment monitoring equipment are connected to the corresponding node of the multi-level data collaborative management link; otherwise, the multiple types of water environment monitoring equipment are prohibited from accessing the multi-level data collaborative management link; verifying the correlation between the data collected by the multiple types of water environment monitoring equipment and the equipment environment; combining the data identification and acquisition deviation of the equipment, performing multi-dimensional correction on the data collected by the equipment, and using it as data to be uploaded; uploading the data to be uploaded to the data collaborative management platform through the water environment management center.

[0009] The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data described above includes the following steps: uploading the data to be uploaded to the data collaborative management platform through the water environment management center. This includes using a combination of multi-level thread pools and priority task queues to concurrently access the data to be uploaded, uploading the corrected water environment monitoring data to the data collaborative management platform through the water environment management center, and storing the data according to different data types.

[0010] The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data, as described above, further includes the following steps: using a data sharing interface to achieve collaborative sharing of water environment monitoring data between the data collaborative management platform and other platform or departmental clients.

[0011] The domestically developed method for collaborative management and sharing of multiple types of water environment monitoring data, as described above, further includes the following steps: real-time monitoring of the flow and sharing of water environment monitoring data, identifying data characteristic of violations; calculating a violation warning value based on the violation characteristic data, comparing the violation warning value with a preset threshold, and issuing an alarm and modifying the sharing strategy if the violation warning value is greater than the preset threshold; otherwise, no alarm is required.

[0012] The domestically adapted method for collaborative management and sharing of multiple types of water environment monitoring data, as described above, includes a multi-level data collaborative management link consisting of three nodes: an edge gateway, a water environment management center with a domestically built server cluster, and a data collaborative management platform.

[0013] The domestically adapted method for collaborative management and sharing of multi-type water environment monitoring data, as described above, includes the following steps in response to the first request from multiple types of water environment monitoring devices to access the multi-level data collaborative management link: obtaining test acquisition data frames and authentication basic data from the multiple types of water environment monitoring devices; and calculating the matching degree between the multiple types of water environment monitoring devices and the authentication protocols in the pre-built authentication library based on the test acquisition data frames and authentication basic data. This includes: extracting test authentication feature data from the test acquisition data frames; and calculating the matching degree between the multiple types of water environment monitoring devices and the authentication protocols in the pre-built authentication library based on the test authentication feature data and authentication basic data.

[0014] The aforementioned method for collaborative management and sharing of domestically adapted multi-type water environment monitoring data involves: correlating and verifying the data collected by various types of water environment monitoring equipment with the equipment environment; identifying collection deviations based on the equipment data; and performing multi-dimensional corrections on the equipment-collected data. The data to be uploaded includes: acquiring environmental monitoring parameters from various types of water environment monitoring equipment and calculating the environmental impact index based on these parameters; calculating the deviation value based on the difference between the equipment-collected data and the standard reference value; and correcting equipment-collected data whose deviation values ​​exceed a preset deviation threshold based on the environmental impact index and the type of equipment-collected data.

[0015] The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data, as described above, includes the following steps to achieve collaborative sharing of water environment monitoring data between the data collaborative management platform and other platform or departmental clients through a data sharing interface: constructing a domestically developed multi-type data sharing interface; responding to sharing requests from third-party platform or departmental clients by verifying their permissions; allowing access to shared data if the verification passes, otherwise prohibiting access to shared data; and setting different openness and sharing strategies for data with different levels of confidentiality.

[0016] The aforementioned method for collaborative management and sharing of domestically adapted multi-type water environment monitoring data includes the following steps: Uploading data to the data collaboration management platform via the water environment management center involves: constructing a three-tiered thread pool for data acquisition, task scheduling, and uploading to form a concurrent access architecture; prioritizing tasks based on data type and importance to construct a priority task queue; determining the compatibility of domestically adapted servers; concurrently accessing the corrected water environment monitoring data via the three-tiered thread pool; prioritizing the upload of corrected water environment monitoring data to pre-determined compatible domestically adapted servers; and after the corrected water environment monitoring data is uploaded to the data collaboration management platform via compatible domestically adapted servers, it is then classified and distributed for storage according to hydrological, water quality, and water ecological types.

[0017] The method for collaborative management and sharing of domestically adapted multi-type water environment monitoring data, as described above, involves setting the edge gateway close to the multi-type water environment monitoring equipment; deploying the data collaborative management platform as the data aggregation center based on a domestically developed distributed architecture; uploading the data collected by the multi-type water environment monitoring equipment to the water environment management center via the edge gateway; and uploading the data collected by the water environment management center to the data collaborative management platform after processing.

[0018] The beneficial effects achieved by this application are as follows: (1) This application associates data collection with the environmental impact of monitoring equipment, accurately identifies collection deviations caused by the environment, and corrects different types of deviation data to improve the accuracy of water environment monitoring data.

[0019] (2) Based on the hierarchical authorization mechanism of data confidentiality level and the domestic encryption protection of the whole process, this application uses domestic algorithms such as SM2, SM3, and SM4 to realize the encryption of data access, transmission and storage. At the same time, it sets up a domestic data sharing interface to adapt to the domestic business platform of multiple departments, break down the data sharing barriers of multiple departments, and take into account the efficiency and security of data sharing.

[0020] (3) This application identifies the characteristic data of illegal operation, calculates the illegal operation warning value based on the illegal operation characteristic data, compares the illegal operation warning value with the preset threshold, and if the illegal operation warning value is greater than the preset threshold, an alarm is triggered and the sharing strategy is modified; otherwise, no alarm is required, thereby improving the security and reliability of water environment monitoring data management and sharing.

[0021] (4) This application is adapted to the water environment monitoring and management needs of different levels such as watersheds, regions, and monitoring points. It supports the collaborative management of multiple types of data such as hydrology, water quality, and ecology. Each module adopts a modular design and can be flexibly expanded based on the domestic technology architecture. It can meet the water environment monitoring data management and sharing needs of different regions and departments and has broad application prospects. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0023] Figure 1 This is a flowchart illustrating a domestically developed method for collaborative management and sharing of multiple types of water environment monitoring data, as described in this application.

[0024] Figure 2 This is a flowchart illustrating a method for determining the compatibility of domestically produced servers according to an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0026] like Figure 1 As shown, this application provides a domestically developed method for collaborative management and sharing of multi-type water environment monitoring data, which includes the following steps: Step S1: Construct a multi-level data collaborative management link for various types of water environment monitoring equipment.

[0027] Specifically, based on a domestically developed protocol adaptation engine, a multi-level data collaborative management link is constructed for various types of water environment monitoring equipment.

[0028] Among them, the domestic protocol adaptation engine is an independently controllable software engine based on domestic technology stack. It is located between the application and the underlying infrastructure, realizing protocol conversion, data interoperability and seamless compatibility between different systems, devices and applications, and solving the language barrier problem in the domestic substitution.

[0029] The multi-level data collaborative management link includes three levels of nodes, which are connected in sequence: edge gateway, water environment management center with domestic server cluster, and data collaborative management platform.

[0030] The edge gateway is positioned close to various types of water environment monitoring equipment. The data collaboration management platform, serving as the data aggregation center, is deployed based on a domestically developed distributed architecture.

[0031] The water environment monitoring data is collected by various types of water environment monitoring equipment and then uploaded to the water environment management center, which has built a domestic server cluster, through an edge gateway. After the water environment management center completes the data correction, violation identification, concurrent scheduling and national cryptographic encryption, the data is uniformly uploaded to the data collaborative management platform for classified storage and collaborative sharing.

[0032] Among them, various types of water environment monitoring equipment include: water quality monitoring equipment (such as multi-parameter water quality analyzers and online nutrient salt monitors), hydrological monitoring equipment (such as water level gauges, flow meters, and rain gauges), and water ecological monitoring equipment (such as algae analyzers and biotoxicity monitors).

[0033] The water quality monitoring equipment is installed at water quality monitoring nodes in the water environment, the hydrological monitoring equipment is installed at hydrological monitoring nodes in the water environment, and the aquatic ecosystem monitoring equipment is installed at aquatic ecosystem monitoring nodes in the water environment. The hydrological monitoring equipment collects hydrological data such as water level, flow rate, and flow velocity; the water quality monitoring equipment collects water quality data such as pH value, COD, ammonia nitrogen, and turbidity; and the aquatic ecosystem monitoring equipment collects ecological indicator data such as aquatic organisms and vegetation coverage.

[0034] Step S2: In response to the first request from multiple types of water environment monitoring equipment to access the multi-level data collaborative management link, obtain the test acquisition data frames and authentication basic data of the multiple types of water environment monitoring equipment, and calculate the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in the pre-built authentication library based on the test acquisition data frames and authentication basic data.

[0035] The test data frames consist of multiple test data points collected by various types of water environment monitoring equipment at monitoring nodes, such as 3 or 5 data points. The authentication data consists of the identity authentication data of various types of water environment monitoring equipment, which includes, for example, equipment type, equipment code, equipment password, and equipment certificate.

[0036] Step S2 includes: Step S210: Extract test certification feature data based on the test data acquisition frame.

[0037] The test and certification feature data include: data frame length, data transmission rate, frequency of occurrence of key fields, etc.

[0038] Step S220: Based on the test certification feature data and certification basic data, calculate the matching degree between various types of water environment monitoring equipment and the certification protocols in the pre-built certification library.

[0039] The formula for calculating the matching degree between various types of water environment monitoring equipment and the certification protocols in the pre-built certification library is as follows: ; in, This indicates the degree of matching between various types of water environment monitoring equipment and the certification protocols in a pre-built certification library; This indicates the total number of types of authentication data. Indicates the first Matching factors for the basic authentication data; if the basic authentication data is within the preset authorization range, then ;otherwise, ; This indicates the total number of types of test certification feature data; Indicates the first Weights of test and certification feature data; Indicates the first The average value of the test certification characteristic data; Indicates the first Standard values ​​for the characteristic data of a certain test certification.

[0040] Step S3: If the matching degree between the multi-type water environment monitoring equipment and the authentication protocol in the pre-built authentication library exceeds the dynamic matching threshold of the corresponding equipment type, the matching is successful, and the multi-type water environment monitoring equipment is connected to the corresponding node of the multi-level data collaborative management link; otherwise, the multi-type water environment monitoring equipment is prohibited from being connected to the multi-level data collaborative management link.

[0041] This invention sets a dynamic matching threshold to improve the success rate of connecting various types of water environment monitoring equipment.

[0042] The corresponding nodes for connecting various types of water environment monitoring equipment to the multi-level data collaborative management link include: Acquire the environmental response characteristics of various types of water environment monitoring equipment at the target monitoring node.

[0043] Based on the environmental response characteristics of the equipment, the environmental adaptability index of the equipment at the target monitoring node is predicted, and the equipment whose environmental adaptability index meets the preset threshold range is connected to the corresponding node of the multi-level data collaborative management link.

[0044] The environmental response characteristics of the equipment include: temperature adaptation characteristics, humidity adaptation characteristics, power supply adaptation characteristics, and communication adaptation characteristics.

[0045] Among these, temperature adaptability characteristics include: the response speed of the device to sudden temperature changes. Humidity adaptability characteristics include: the device's humidity resistance rating. Power supply adaptability characteristics include: low voltage sustaining time. Communication adaptability characteristics include: communication latency and packet loss rate, etc.

[0046] The formula for calculating the environmental adaptability index is as follows: ; in, Indicates the environmental adaptability index; This represents the total number of types of equipment environmental response characteristics; Indicates the first Weights of each device's environmental response characteristics; Indicates the first The actual values ​​of the environmental response characteristics of each device; Indicates the first The standard ideal value of the environmental response characteristics of each device.

[0047] Step S4 involves correlating and verifying the data collected by various types of water environment monitoring equipment with the equipment environment, identifying data collection deviations based on the equipment data, and correcting the data collected by the equipment in multiple dimensions before uploading it.

[0048] Step S4 includes: Step S410: Obtain environmental monitoring parameters from multiple types of water environment monitoring equipment, and calculate the environmental impact index based on the environmental monitoring parameters.

[0049] Among them, environmental monitoring parameters include: temperature, humidity, water flow velocity, etc. of the environment in which various types of water environment monitoring equipment are located.

[0050] Different devices have varying sensitivities to temperature, humidity, and flow rate. Considering the coupling effect between the device and the environment, the environmental impact index of different water environment monitoring devices is calculated.

[0051] The formula for calculating the environmental impact index is as follows: ; in, ; in, Indicates the environmental impact index; This indicates the total number of types of environmental monitoring parameters; Indicates the first The total number of times environmental monitoring parameters were collected; Indicates the first Weights of various environmental monitoring parameters; Indicates parameters; Indicates the first The first environmental monitoring parameter The measured values ​​collected this time; Indicates the first Minimum limits for various environmental monitoring parameters; Indicates the first Maximum limits for various environmental monitoring parameters; Indicates the first Sensitivity coefficients of various environmental monitoring parameters to the effects of multiple types of water environment monitoring equipment.

[0052] Among them, different impact sensitivity coefficients are pre-set for different types of water environment monitoring equipment. For example, for water quality equipment, humidity... ,temperature Flow rate ,illumination The higher the sensitivity coefficient, the more sensitive the equipment is to its impact.

[0053] in, Indicates the first The fluctuation range of various environmental monitoring parameters.

[0054] Among them, the A method for calculating the fluctuation range of an environmental monitoring parameter involves setting a sliding data acquisition window of a predetermined length (e.g., acquiring 10 data points) and calculating the fluctuation range of the 10 most recent environmental monitoring parameters. The fluctuation range of various environmental monitoring parameters. The formula for calculating the fluctuation range of environmental monitoring parameters is as follows: ; in, This represents the maximum value among 10 environmental monitoring parameters within the sliding data acquisition window; This represents the minimum value among 10 environmental monitoring parameters within the sliding data acquisition window.

[0055] This invention addresses the problem of unstable parameter fluctuations in complex aquatic environments (such as sudden changes in flow velocity after heavy rain or extreme temperature weather). It introduces the fluctuation amplitude of environmental monitoring parameters and the sensitivity coefficient of environmental monitoring parameters to the impact of various types of water environment monitoring equipment to calculate the environmental impact index, thereby improving the accuracy of the environmental impact index calculation.

[0056] Step S420: Calculate the deviation value based on the difference between the data collected by the device and the standard reference value.

[0057] Specifically, the deviation value is the difference between the data collected by the device and the standard reference value.

[0058] Step S430: Based on the environmental impact index and the type of data collected by the equipment, correct the data collected by the equipment whose deviation value exceeds the preset deviation threshold.

[0059] As a specific embodiment of the present invention, the data collected by different types of devices are corrected accordingly based on the type of data collected by the devices.

[0060] Specifically, hydrological monitoring data (such as water level and flow rate) are corrected using a time-series interpolation algorithm. The corrected hydrological monitoring data is calculated based on the spatiotemporal correlation of adjacent valid data collection points. The formula for calculating the corrected hydrological monitoring data is as follows: ; in, This indicates the revised hydrological monitoring data; Indicates the number of historical data collections. Valid hydrological monitoring data (e.g., water level or water flow) from adjacent collection points; This indicates the number of adjacent valid data collection points involved in the calculation; This represents the time decay factor, which is dynamically adjusted based on hydrological changes and environmental impact indices.

[0061] in, ; in, Indicates the base time decay coefficient; Indicates the sensitivity adjustment coefficient; the baseline time decay coefficient during the normal water level period. Flood season baseline time decay coefficient Sensitivity adjustment coefficient The value range is 0.1-0.6, preferably 0.3-0.4. This indicates the environmental impact index.

[0062] The spatial difference algorithm is used to correct the water quality deviation data. Based on the inverse distance weighted interpolation and the optimization of watershed water environment characteristics, the corrected water quality deviation data is calculated by using effective data from surrounding monitoring nodes and spatial distance weights.

[0063] in, ; in, This indicates the corrected water quality deviation data; Indicates the number of monitoring nodes around the target device; For the first Raw, accurate data collected from several effective monitoring nodes in the surrounding area. Distance attenuation coefficient (based on environmental impact index) Dynamic Adjustment: Environmental Impact Index The bigger The larger the value, the greater the weight of neighboring data; This indicates the device corresponding to the data to be corrected and the first... The spatial distance between the surrounding monitoring nodes. The monitoring data of the device to be corrected is obtained by weighting the data of the surrounding normally operating nodes according to spatial distance. The closer the node is, the greater its influence on the correction result. Moreover, the weight is dynamically adjusted according to the degree of environmental interference to improve the correction accuracy.

[0064] The water ecological index data were corrected using a combination of statistical averaging and machine learning methods. A machine learning model was trained using historical, accurate data, and then used to correct for any biased data.

[0065] Step S5: Upload the data to be uploaded to the data collaboration management platform through the Water Environment Management Center.

[0066] Uploading data to be uploaded to the data collaboration management platform through the water environment management center involves using a combination of multi-level thread pools and priority task queues to concurrently access the data to be uploaded. The corrected water environment monitoring data is then uploaded to the data collaboration management platform through the water environment management center and stored according to different data types.

[0067] Step S5 includes: Step S510: Construct a three-level thread pool for data acquisition, task scheduling, and uploading to form a concurrent access architecture.

[0068] Step S520: Prioritize tasks according to data type and importance, and construct a priority task queue.

[0069] Step S530: Determine the compatibility of the domestically produced server, and use a three-level thread pool to concurrently access the corrected water environment monitoring data. Priority scheduling is performed to the domestically produced servers that are pre-determined to be compatible to upload the corrected water environment monitoring data.

[0070] like Figure 2 As shown, the method for determining the compatibility of domestically produced servers is as follows: Step T1: Real-time monitoring of the dynamic parameters of the domestically produced server.

[0071] The dynamic parameters include: computing resource parameters, network resource parameters, energy status parameters, and task parameters. Computing resource parameters include: CPU utilization (Ycpu), memory utilization (Ymen), and accelerator card utilization (Ynpu). Network resource parameters include: current uplink bandwidth (Bsh), network latency (Lwa), and packet loss rate (Los). Energy status parameters include: power supply mode and remaining battery power (Esh). Task parameters include: number of pending tasks (Nda) and average waiting time (Twa) for tasks to be processed.

[0072] Step T2: Calculate the edge adaptation upload index of the data after it has been corrected and uploaded locally on the domestic server, based on the dynamic parameters of the domestic server.

[0073] The formula for calculating the edge adaptation upload index, which involves processing the corrected data locally on a domestically produced server, is as follows: ; in, This indicates the edge-adaptive upload index for processing and uploading corrected water environment monitoring data locally on domestically produced servers. This indicates the size of the water environment monitoring data packet to be uploaded; Indicates the current uplink bandwidth; This indicates network latency; Los indicates packet loss rate. , , These represent the impact weights of current uplink bandwidth, network latency, and packet loss rate, respectively; R(Esh) represents the power regulation parameter. Specifically, R(Esh) = 0.5 when the power supply mode is battery powered, and R(Esh) = 1 when the power supply mode is AC power powered. Indicates CPU utilization, Indicates memory usage; Indicates the utilization rate of the accelerator card; This indicates the remaining battery power of the domestically produced server. These represent the impact weights of CPU utilization, memory utilization, accelerator card utilization, and remaining power of the domestically produced server, respectively. ; .

[0074] Step T3: Compare the edge adaptation upload index with the preset adaptation threshold. If the edge adaptation upload index is greater than the preset adaptation threshold, the domestic server is an adaptable domestic server and is allowed to upload the corrected data. Otherwise, the domestic server is an unadaptable domestic server and is prohibited from uploading the corrected data.

[0075] This invention improves the existing multi-level thread pool data upload by coupling the dynamic calculation of the edge adaptation upload index with thread pool scheduling. If the edge adaptation upload index is greater than the preset adaptation threshold, the domestic server is an adaptable domestic server, allowing the domestic server to upload the corrected data, avoiding data upload blocking, and improving the reliability and efficiency of data upload.

[0076] This invention improves upon the existing traditional polling upload strategy by using edge-adaptive dynamic scheduling to upload corrected data, thereby enhancing the stability and reliability of high-concurrency data transmission.

[0077] In a specific embodiment of the present invention, the corrected water environment monitoring data is prioritized and uploaded to servers pre-determined to be compatible with domestically produced systems. The priority of the corrected water environment monitoring data is dynamically adjusted and not fixed.

[0078] This process involves periodically collecting and revising water environment monitoring data, identifying key characteristics for upload tasks, and calculating the adjustment priority of the water environment monitoring data based on these characteristics. By dynamically adjusting the scheduling priority of water environment monitoring data, truly urgent data is uploaded, thereby improving the rational utilization of resources.

[0079] The formula for calculating the adjustment priority of water environment monitoring data is as follows: ; in, Indicates the priority of adjusting water environment monitoring data; This indicates the priority of basic services, and for alarm services... Hydrological monitoring data Water quality monitoring data Water ecological monitoring data . This indicates the edge-adaptive upload index of the water environment monitoring data to be uploaded to its local domestic server. The priority increases exponentially as the deadline approaches. Indicates the deadline for uploading the task; Indicates the current time. , , These represent the impact weights of basic business priority, edge adaptation upload index, and upload task deadline, respectively. .

[0080] Step S540: After the corrected water environment monitoring data is uploaded to the data collaborative management platform through a domestically compatible server, it is classified and distributed for storage according to hydrological, water quality, and water ecological types.

[0081] This invention isolates the acquisition, scheduling, and uploading threads at three levels, preventing single-type data from blocking the entire upload process and resolving data loss and latency issues in high-concurrency scenarios. Combining the characteristics of water environment monitoring services, this invention prioritizes alarm data and water source data, ensuring that critical data is uploaded first. This invention supports concurrent access from massive numbers of devices while maintaining controllable upload order, resolving issues of out-of-order concurrency, duplicate uploads, and traffic storms.

[0082] Step S6: Through the data sharing interface, complete the collaborative sharing of water environment monitoring data between the data collaborative management platform and other platforms or departmental clients.

[0083] As an example, differentiated data sharing services are provided to ecological and environmental regulatory departments, water conservancy management departments, and public service platforms through standardized data sharing interfaces.

[0084] As a specific embodiment of the present invention, a hierarchical authorization mechanism based on data confidentiality level and full-process domestic encryption protection are adopted. Domestic algorithms such as SM2, SM3, and SM4 are used to encrypt data access, transmission, and storage. At the same time, a domestic data sharing interface is set up to adapt to the domestic business platforms of multiple departments, break down the barriers to data sharing among multiple departments, and balance the efficiency and security of data sharing.

[0085] Step S6 includes: Step S610: Construct a domestically produced multi-type data sharing interface.

[0086] Specifically, a RESTful API interface (main interface), a message queue interface, and an offline file transfer interface are configured. The main interface uses the HTTP / HTTPS protocol and supports operations such as data query, data subscription, and data synchronization. The data transmission format is JSON (with a domestically standardized encapsulation), making it compatible with domestic business platforms of departments such as water resources, environmental protection, and housing and urban-rural development. The message queue interface supports high-concurrency data push (such as real-time water quality alarm data) and is suitable for scenarios with high real-time requirements (such as sharing hydrological data during the flood season). The offline file transfer interface is suitable for batch sharing of large-capacity historical data.

[0087] In step S620, in response to a sharing request from a third-party platform or department client, permission verification is performed on the third-party platform or department client. If the verification passes, access to shared data is allowed; otherwise, access to shared data is prohibited.

[0088] The authorization verification method includes: third-party platforms or clients need to upload verification data such as identity identifiers and interface keys. The data collaboration management platform verifies the identity and legality of the verification data through a domestically developed identifier resolution system. If the verification is successful, the verification passes; otherwise, the verification fails. The interface key is encrypted and stored using the SM3 algorithm to prevent leakage.

[0089] Step S630: Set different opening and sharing policies for data with different levels of confidentiality.

[0090] Specifically, different opening and sharing policies are set for data with different levels of confidentiality, as follows: For publicly available data, the data collaboration management platform adopts an open query and download strategy, and data transmission uses the SM4 algorithm for lightweight encryption.

[0091] For internal data, authorized collaborative sharing is achieved through a multi-department private network, identity verification and permission verification are performed based on access control lists, and operation logs are recorded throughout the data flow.

[0092] For classified data, an offline encrypted transmission combined with physical isolation is used for sharing, which is only open to designated personnel with access to classified information, and multiple authentication methods are used for data access and use.

[0093] As a specific embodiment of the present invention, hydrological data (public level), water quality data (internal level), and aquatic ecological data (confidential level) are subject to hierarchical dynamic management and control.

[0094] Step S7: Monitor the flow and sharing of water environment monitoring data in real time, and identify data showing characteristics of violations.

[0095] The data identifying violations includes: abnormal access frequency and abrupt changes in access behavior. Abnormal access frequency refers to situations where the client's request frequency per unit of time far exceeds normal business operations. Abrupt changes in access behavior refer to situations where a client's historical access habits suddenly change, such as a long-term query for water quality monitoring data followed by a sudden access to classified data.

[0096] Abrupt access behavior characteristics include: mismatched permission levels, operation scope exceeding limits, and excessive data downloads.

[0097] As a specific embodiment of the present invention, when high-frequency client access is detected, the service path accessed by the client is searched. Based on the searched service path, it is determined whether the service path is a reasonable path. If it is a reasonable path, no alarm is issued; otherwise, an alarm is issued. Reasonable paths for client access are pre-constructed. A reasonable path is, for example, a known authorized IP address of the water resources bureau - upstream site - access to water level data and flow data. The searched service path is compared with the pre-constructed reasonable paths. If the currently searched service path belongs to the pre-constructed reasonable paths, then the currently searched service path is a reasonable path; otherwise, the currently searched service path is an unreasonable path. An unreasonable path is, for example, evening - unknown IP address - a certain site - access to confidential data.

[0098] Step S8: Calculate the violation warning value based on the violation operation characteristic data, compare the violation warning value with the preset threshold. If the violation warning value is greater than the preset threshold, an alarm is triggered and the sharing policy is modified; otherwise, no alarm is triggered.

[0099] The formula for calculating the violation warning value is as follows: ; in, Indicates the warning value for violations. This represents the reasonable path factor; if the client accesses the site using a reasonable path, then... ;otherwise, ; Indicates the factors influencing the rationality of the path; This indicates the influencing factor of the data on the characteristics of illegal operations. ; This indicates the total number of client types that exhibited violations. Indicates the first The total number of types of violation feature data identified in each client that violated the rules; Indicates the first The first client identified for violating the rules. Threat value of data related to the characteristics of such violations; Indicates the first The first client identified for violating the rules. Quantitative values ​​of various violation operation characteristic data (such as number of unauthorized accesses, number of high-frequency requests, number of data downloads exceeding the limit, etc.); Indicates the first The first client identified for violating the rules. The maximum standard threshold for characteristic data of various violations; Indicates the first The maximum data security level coefficient for each client that violates the rules when accessing data; where classified data corresponds to... Internal data corresponding to ; Public level corresponding . .

[0100] This application also provides a computer storage medium storing computer instructions, which, when invoked, execute the address mapping method for the large-capacity solid-state drive. The computer storage medium includes one or more program instructions, which are executed by a processor to provide a domestically developed, multi-type water environment monitoring data collaborative management and sharing method.

[0101] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer performs the aforementioned method for collaborative management and sharing of domestically produced, multi-type water environment monitoring data.

[0102] This invention provides a processor for processing the aforementioned domestically developed method for collaborative management and sharing of multi-type water environment monitoring data.

[0103] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0104] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0105] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0106] The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EEPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0107] The beneficial effects achieved by this application are as follows: (1) This application associates data collection with the environmental impact of monitoring equipment, accurately identifies collection deviations caused by the environment, and corrects different types of deviation data to improve the accuracy of water environment monitoring data.

[0108] (2) Based on the hierarchical authorization mechanism of data confidentiality level and the domestic encryption protection of the whole process, this application uses domestic algorithms such as SM2, SM3, and SM4 to realize the encryption of data access, transmission and storage. At the same time, it sets up a domestic data sharing interface to adapt to the domestic business platform of multiple departments, break down the data sharing barriers of multiple departments, and take into account the efficiency and security of data sharing.

[0109] (3) This application identifies the characteristic data of illegal operation, calculates the illegal operation warning value based on the illegal operation characteristic data, compares the illegal operation warning value with the preset threshold, and if the illegal operation warning value is greater than the preset threshold, an alarm is triggered and the sharing strategy is modified; otherwise, no alarm is required, thereby improving the security and reliability of water environment monitoring data management and sharing.

[0110] (4) This application is adapted to the water environment monitoring and management needs of different levels such as watersheds, regions, and monitoring points. It supports the collaborative management of multiple types of data such as hydrology, water quality, and ecology. Each module adopts a modular design and can be flexibly expanded based on the domestic technology architecture. It can meet the water environment monitoring data management and sharing needs of different regions and departments and has broad application prospects.

[0111] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0112] In the description of this application, the word "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0113] The above description is merely an embodiment of the present invention and is 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 principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A domestically developed method for collaborative management and sharing of multi-type water environment monitoring data, characterized in that, The method includes the following steps: Construct a multi-level data collaborative management link for various types of water environment monitoring equipment; In response to the first request from multiple types of water environment monitoring equipment to access the multi-level data collaborative management link, the system obtains the test acquisition data frames and authentication basic data of the multiple types of water environment monitoring equipment, and calculates the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in the pre-built authentication library based on the test acquisition data frames and authentication basic data. If the matching degree between multiple types of water environment monitoring equipment and the authentication protocol in the pre-built authentication library exceeds the dynamic matching threshold of the corresponding equipment type, the matching is successful, and the multiple types of water environment monitoring equipment will be connected to the corresponding node of the multi-level data collaborative management link; otherwise, the multiple types of water environment monitoring equipment will be prohibited from being connected to the multi-level data collaborative management link. The data collected by various types of water environment monitoring equipment are correlated and verified with the equipment environment. Combined with the data of the equipment to identify the collection deviation, the data collected by the equipment is corrected in multiple dimensions and used as the data to be uploaded. The data to be uploaded will be uploaded to the data collaboration management platform through the Water Environment Management Center.

2. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 1, characterized in that, Uploading the data to be uploaded to the data collaboration management platform through the Water Environment Management Center includes: A multi-level thread pool combined with a priority task queue is used to concurrently access data to be uploaded. The corrected water environment monitoring data is uploaded to the data collaboration management platform through the water environment management center and stored according to different data types.

3. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 1, characterized in that, The method also includes the following steps: Through the data sharing interface, the data collaborative management platform can achieve collaborative sharing of water environment monitoring data with other platforms or departmental clients.

4. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 3, characterized in that, The method also includes the following steps: Real-time monitoring of water environment monitoring data flow and shared usage data; identification of data characteristics of illegal operations. Based on the characteristics of the violation, a violation warning value is calculated. The violation warning value is compared with a preset threshold. If the violation warning value is greater than the preset threshold, an alarm is triggered and the sharing policy is modified. Otherwise, no alarm is triggered.

5. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 1, characterized in that, The multi-level data collaborative management link consists of three levels of nodes: edge gateway, water environment management center with domestic server cluster, and data collaborative management platform.

6. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 1, characterized in that, In response to the first request from multiple types of water environment monitoring equipment to access the multi-level data collaborative management link, the system acquires test data frames and authentication basic data from these devices. Based on the test data frames and authentication basic data, it calculates the matching degree between the multiple types of water environment monitoring equipment and the authentication protocols in the pre-built authentication library, including: Extract test certification feature data from the test data frames; Based on the test certification feature data and certification basic data, the matching degree between various types of water environment monitoring equipment and certification protocols in the pre-built certification library is calculated.

7. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 1, characterized in that, Data collected by various types of water environment monitoring equipment is correlated and verified with the equipment environment. Based on the data identification and collection deviations from the equipment, the collected data is corrected in multiple dimensions. The data to be uploaded includes: Obtain environmental monitoring parameters from various types of water environment monitoring equipment, and calculate the environmental impact index based on these parameters; The deviation value is calculated based on the difference between the data collected by the equipment and the standard reference value; Based on the environmental impact index and the type of data collected by the equipment, the data collected by the equipment whose deviation value exceeds the preset deviation threshold is corrected accordingly.

8. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 3, characterized in that, Through the data sharing interface, the collaborative sharing of water environment monitoring data between the data collaborative management platform and other platform or departmental clients includes: Build domestically produced multi-type data sharing interfaces; In response to sharing requests from third-party platforms or departmental clients, the system performs permission verification on the third-party platforms or departmental clients. If the verification passes, access to shared data is allowed; otherwise, access to shared data is prohibited. Different openness and sharing policies are set for data with different levels of confidentiality.

9. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 1, characterized in that, Uploading the data to be uploaded to the data collaboration management platform through the Water Environment Management Center includes: A three-tiered thread pool is constructed for data acquisition, task scheduling, and uploading to form a concurrent access architecture; Prioritize tasks based on data type and importance, and construct a priority task queue; The compatibility of domestically produced servers is determined, and the corrected water environment monitoring data is accessed concurrently through a three-level thread pool. The data is then scheduled to be uploaded to domestically produced servers that are pre-determined to be compatible, based on priority. The revised water environment monitoring data is uploaded to the data collaborative management platform via a domestically compatible server and then stored in a distributed manner according to hydrological, water quality, and water ecological types.

10. The domestically developed method for collaborative management and sharing of multi-type water environment monitoring data according to claim 5, characterized in that, The edge gateway is positioned close to various types of water environment monitoring equipment; The data collaboration management platform, serving as a data aggregation center, is deployed based on a domestically developed distributed architecture. The data collected by the various types of water environment monitoring equipment is uploaded to the water environment management center through the edge gateway. The water environment management center processes the data collected by the various types of water environment monitoring equipment and then uploads it to the data collaborative management platform.

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