Distributed pressure monitoring device integrated with cloud computing

Abstract

The invention discloses a distributed pressure monitoring device integrated with cloud computing, and belongs to the field of pressure monitoring, and the device comprises an access identification module which is used for determining an access topology task monitoring module for a distributed pressure gauge, and is used for receiving a pressure monitoring task; the resource planning module is used for performing network resource planning deployment on the cloud resource pool based on the pressure monitoring task in combination with the access topology, and determining a resource deployment topology; and the monitoring decision-making module is used for executing task processing and decision-making on the pressure monitoring task based on the resource deployment topology, generating a distributed monitoring list and performing fog distribution end storage. The technical problems that in existing distributed pressure monitoring, a cloud computing mode is low in resource utilization rate, poor in scheduling flexibility and difficult to adapt to complex and changeable pressure monitoring tasks are solved, the cloud computing mode is optimized by introducing the fog computing principle, the technical effect of improving the utilization rate of cloud resources while improving the flexibility is achieved, and the application prospect is wide. And the requirement of distributed pressure monitoring is better met.

Description

Technical Field

[0001] The present invention relates to the field of pressure monitoring, and in particular to a distributed pressure monitoring device integrated with cloud computing. Background Art

[0002] In the prior art, distributed pressure monitoring usually adopts the cloud computing model to handle complex and changeable monitoring tasks. However, there are some problems with the traditional cloud computing model. First, the resource utilization rate under the cloud computing model is low. Due to the complexity and variability of distributed pressure monitoring tasks, it is difficult for the cloud computing platform to accurately schedule and efficiently utilize resources. This has led to a large amount of idle waste of cloud resources, reducing the efficiency of the entire monitoring. Secondly, the scheduling flexibility of the cloud computing model is poor, and it is unable to respond quickly to changing monitoring needs. When there are sudden changes in monitoring tasks or changes in the monitored objects, it is difficult for the cloud computing platform to adjust resource configuration and task allocation in a timely manner, resulting in a decrease in monitoring performance and even a monitoring blind spot. Therefore, in the existing distributed pressure monitoring, the cloud computing model has low resource utilization and poor scheduling flexibility, and is difficult to adapt to the technical problems of complex and changeable pressure monitoring tasks. Summary of the invention

[0003] The present application provides a distributed pressure monitoring device integrated with cloud computing, aiming to solve the technical problems in existing distributed pressure monitoring, such as low resource utilization, poor scheduling flexibility, and difficulty in adapting to complex and changeable pressure monitoring tasks in the cloud computing model.

[0004] The distributed pressure monitoring device integrated with cloud computing disclosed in the present application includes: an access identification module, which is used to determine the access topology for the connected distributed pressure gauge, wherein the access topology identifies the local integration degree; a task monitoring module, which is used to receive the pressure monitoring task, wherein the pressure monitoring task is a distributed concurrent task of the access topology; a resource planning module, which is used to plan and deploy network resources for the cloud resource pool based on the pressure monitoring task and in combination with the access topology, and determine the resource deployment topology, wherein the resource deployment topology includes a core resource pool and a fog distribution end, the fog distribution end is located at the local edge side of the network, and the resource deployment topology is updateable; a monitoring decision module, which is used to perform task processing and decision-making on the pressure monitoring task based on the resource deployment topology, generate a distributed monitoring form and store it on the fog distribution end.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] By adopting the access identification module, the access topology is determined for the connected distributed pressure gauges, and the local integration degree is identified, so as to accurately grasp the network structure and localization degree of distributed pressure monitoring, and provide a basis for subsequent resource planning and task scheduling; through the task monitoring module, the pressure monitoring task is received and identified as a distributed concurrent task of the access topology, the monitoring task requirements are obtained in real time, and the tasks are classified and processed concurrently according to the access topology, so as to improve the task response speed and processing efficiency; the resource planning module is introduced, and the network resource planning and deployment of the cloud resource pool is carried out based on the pressure monitoring task and the access topology, and the resource deployment topology including the core resource pool and the fog distribution end is determined, and the fog computing principle is fully utilized to deploy resources at the network edge close to the monitored object, so as to realize local processing and real-time response, and at the same time, the resource distribution is dynamically updated. Deployment improves flexibility and adaptability; through the monitoring decision module, based on the resource deployment topology, the pressure monitoring task is processed and decided, a distributed monitoring list is generated and stored at the fog distribution end, and the monitoring task is sunk to the fog distribution end for local processing and storage, which reduces the computing and storage pressure of the cloud and improves the data transmission efficiency and real-time performance; at the same time, through the generation of distributed monitoring lists, the standardization and structuring of monitoring data is realized, which is convenient for subsequent analysis and application of technical solutions, and solves the technical problems of low resource utilization, poor scheduling flexibility and difficulty in adapting to complex and changeable pressure monitoring tasks in the existing distributed pressure monitoring. By introducing the principle of fog computing and optimizing the cloud computing model, the technical effect of improving the utilization of cloud resources while improving flexibility is achieved, which better meets the needs of distributed pressure monitoring.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A structural schematic diagram of a distributed pressure monitoring device integrated with cloud computing is provided for an embodiment of the present application;

[0009] Figure 2 A schematic flow chart of determining resource deployment topology in a distributed pressure monitoring device integrated with cloud computing is provided for an embodiment of the present application.

[0010] Explanation of the reference numerals: access identification module 11 , task monitoring module 12 , resource planning module 13 , monitoring decision module 14 . DETAILED DESCRIPTION

[0011] The overall idea of ​​the technical solution provided by this application is as follows:

[0012] An embodiment of the present application provides a distributed pressure monitoring device integrated with cloud computing. First, the access topology and local integration of the distributed pressure gauge are determined through the access identification module to provide a basis for subsequent resource planning and task scheduling. Then, the task monitoring module receives the pressure monitoring task in real time and identifies it as a distributed concurrent task of the access topology, thereby improving the task response speed and processing efficiency. On this basis, the resource planning module plans and deploys network resources for the cloud resource pool according to the pressure monitoring task and the access topology, introduces the fog distribution end, and realizes the localized deployment and dynamic scheduling of monitoring resources. Afterwards, the monitoring decision module performs processing and decision-making on the pressure monitoring task based on the resource deployment topology, generates a distributed monitoring form and stores it on the fog distribution end, reduces the pressure on the cloud, and improves the real-time and efficiency of monitoring.

[0013] After introducing the basic principles of the present application, the non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the drawings in the specification.

[0014] like Figure 1 As shown, an embodiment of the present application provides a distributed pressure monitoring device integrated with cloud computing, the device comprising:

[0015] The access identification module 11 is used to determine the access topology for the connected distributed pressure meter, wherein the access topology is identified by the local integration degree.

[0016] Specifically, when one or more pressure gauges are connected, the access identification module 11 first obtains the location, number and other information of these pressure gauges, and then classifies the pressure gauges based on preset rules to form an access topology. The preset rules can be flexibly set, which can be based on a threshold value of the distance between pressure gauges or an upper limit on the number of pressure gauges.

[0017] While determining the access topology, the access identification module 11 calculates the local integration of each access topology as a key attribute of the access topology. For example, the spatial distance and quantity distribution of the pressure gauges in the access topology are comprehensively considered, the integration is estimated from these two dimensions, and then the final local integration is obtained by weighting. The higher the local integration, the denser the distribution of the pressure gauges in the access topology, and the stronger the concurrency of the monitoring task may be.

[0018] By introducing local integration into the access topology, the access identification module 11 realizes the grouping and characterization of distributed pressure gauges. This provides an important basis for subsequent computing resource planning and monitoring task allocation. For example, the concurrent pressure and computing intensity of different access topologies can be estimated according to the level of local integration, thereby achieving more accurate resource matching and task scheduling.

[0019] The task monitoring module 12 is used to receive a pressure monitoring task, wherein the pressure monitoring task is a distributed concurrent task of the access topology.

[0020] Specifically, the task monitoring module 12 is responsible for receiving pressure monitoring tasks from the outside. These pressure monitoring tasks are usually distributed and concurrent, that is, the same task may involve multiple pressure gauges, and the monitoring work of different pressure gauges needs to be performed simultaneously.

[0021] When a pressure monitoring task arrives, the task monitoring module 12 first parses the content of the task and extracts key parameters such as monitoring target, monitoring time, sampling frequency, etc. Then, the task monitoring module 12 will identify the pressure gauge involved in the task, and determine the distributed characteristics of the task based on the access topology relationship of the pressure gauge. Among them, there is an inherent mapping relationship between the pressure monitoring task and the access topology. A task may only involve a local access topology, or it may span multiple access topologies. The wider the distribution range of the task, the higher the concurrency granularity. The task monitoring module 12 accurately identifies this correspondence and uses it to determine the distributed concurrency characteristics of the task. After determining the distributed concurrency characteristics of the task, the task monitoring module 12 generates a task description structure, clearly records the various attribute parameters of the task, and passes it to the subsequent resource planning module 13 to provide a basis for resource planning.

[0022] Through the task monitoring module 12, the pressure monitoring task is received and its distributed concurrent characteristics are identified, which provides key support for achieving accurate and efficient pressure monitoring and provides support for the targeted and real-time nature of pressure monitoring.

[0023] The resource planning module 13 is used to plan and deploy network resources for the cloud resource pool based on the pressure monitoring task and in combination with the access topology, and determine the resource deployment topology, wherein the resource deployment topology includes a core resource pool and a fog distribution end, and the fog distribution end is located at the local edge of the network, and the resource deployment topology can be updated.

[0024] Specifically, when the resource planning module 13 receives the task description from the task monitoring module 12, it first extracts the key attributes of the task, such as distribution range, concurrency granularity, real-time requirements, etc. Then, the resource planning module 13 comprehensively considers the task attributes and the access topology of the pressure gauge, divides and schedules the cloud resource pool in a networked manner, and determines the resource deployment topology. Among them, the resource deployment topology includes the core resource pool and the fog distribution end; the core resource pool is located at the central node of the network, has powerful computing and storage capabilities, and is mainly responsible for global task coordination and data aggregation; the fog distribution end is deployed at the edge of the network, close to the pressure gauge access point, and is mainly responsible for localized data collection and preliminary processing. The resource planning module 13 finds the best balance between the division of labor between the two, giving full play to the centralized advantages of the cloud and taking advantage of the proximity efficiency of the fog end. For example, the resource planning module 13 first plans and deploys a certain number of fog nodes on the edge of the network according to the access topology of the pressure gauge, and each fog node is responsible for a local access topology; then, according to the concurrent granularity of the task, the load pressure of each fog node is estimated, and the computing and storage resources are dispatched from the core resource pool accordingly to support and expand the fog nodes; at the same time, a dynamic coordination mechanism is established between the core resource pool and the fog nodes to achieve global and local benign interaction. Since the pressure monitoring task and the access topology may change dynamically, the resource deployment topology also needs to be able to be adjusted dynamically. The resource planning module 13 continuously monitors the changes in tasks and topologies, and optimizes the resource deployment topology in real time according to the preset adjustment strategy to ensure that resource configuration is always in line with demand.

[0025] Through the resource planning module 13, the cloud resource pool is planned and deployed in a networked manner, and a resource deployment topology integrating cloud and fog is constructed. According to the characteristics of the pressure monitoring task, the computing power can be reasonably allocated globally and locally, thereby improving resource utilization while ensuring monitoring performance.

[0026] The monitoring decision module 14 is used to perform task processing and decision-making on the pressure monitoring task based on the resource deployment topology, generate a distributed monitoring list and perform fog distribution end storage.

[0027] Specifically, after the resource planning module 13 completes the network deployment of cloud resources, the monitoring decision module 14 takes over the actual execution process of the pressure monitoring task. First, the monitoring decision module 14 splits the entire monitoring task into multiple subtasks according to the distributed concurrent features in the task description, and distributes the subtasks to the corresponding fog nodes. On each fog node, the monitoring decision module 14 schedules the computing and storage resources of the node to collect and process real-time data for the pressure gauges in the responsible area. This local processing method can greatly reduce the delay and bandwidth pressure of data transmission, and improve the real-time and reliability of monitoring. During the processing, the monitoring decision module 14 analyzes and judges the collected pressure data in real time according to the preset decision rules, and identifies the abnormal patterns and key events therein. Among them, the decision rules can be comparisons triggered by thresholds, or intelligent analysis based on machine learning. When the local data processing and decision are completed, the monitoring decision module 14 generates a standardized monitoring list, records the monitoring results and key data of the node, and obtains a distributed monitoring list. Each monitoring list will be immediately stored in the local storage of the fog node as a distributed data backup to prevent data loss. At the same time, the monitoring list will also be asynchronously uploaded to the core resource pool for global summary analysis and decision-making reference.

[0028] Through the monitoring decision module 14, distributed execution and real-time decision-making of pressure monitoring tasks are realized, which not only takes advantage of edge computing but also ensures the accuracy of global decision-making. The distributed monitoring list generated by the monitoring decision module 14 is not only the direct result of pressure monitoring, but also an important data source for further analysis and mining, providing a strong guarantee for the automation and intelligence of distributed pressure monitoring.

[0029] Furthermore, the embodiment of the present application also includes:

[0030] Based on a preset neighborhood interval, the access topology is divided to determine N local topologies; the N local topologies are traversed to determine a first integration degree based on the distribution quantity, and a second integration degree based on the neighborhood distance mean; based on the first integration degree and the second integration degree, a weighted calculation is performed to obtain N local integration degrees.

[0031] In a feasible implementation, in order to calculate the local integration of the access topology, first, the access topology is divided based on a preset neighborhood interval to form N local topologies. The so-called neighborhood interval refers to the maximum distance threshold between two adjacent pressure gauges when dividing the local topology. Only when the distance between the two pressure gauges is less than this threshold will they be divided into the same local topology. Among them, the size of the neighborhood interval can be flexibly set according to the actual application scenario and requirements.

[0032] After dividing N local topologies, we then traverse each local topology and calculate its local integration. The local integration consists of two parts, the first integration and the second integration. The first integration reflects the number distribution of pressure gauges in the local topology. The calculation method is to count the total number of pressure gauges in the local topology. The more the number, the higher the first integration. The second integration reflects the distance distribution of pressure gauges in the local topology. The calculation method is to count the distances between all pressure gauges in the local topology and take the average. The smaller the distance, the higher the second integration.

[0033] After obtaining the first integration degree and the second integration degree, based on a predetermined weight ratio, the first integration degree and the second integration degree are weighted and summed to obtain the local integration degree of the local topology. The weight ratio distribution is adjusted according to actual needs, for example, whether to pay more attention to the quantity factor or the distance factor. Through weighted calculation, the local integration degree of each of the N local topologies is obtained, thereby achieving a fine characterization of the access topology.

[0034] The local integration not only helps to more accurately describe the characteristics of the access topology, but also provides an important basis for subsequent resource planning and task scheduling. For example, when dividing cloud resources, a fog computing node is assigned to each local topology, so as to achieve the nearby deployment and rapid response of computing resources. The topology with higher local integration can be allocated more computing resources. Therefore, by introducing the local integration, a hierarchical description and characterization of the access topology is realized, which not only retains the global topological structure, but also highlights the local aggregation characteristics, laying the foundation for more accurate and efficient pressure monitoring.

[0035] Furthermore, the cloud resource pool includes a storage resource pool and a computing resource pool, and the storage resource pool and the computing resource pool are relatively independent and collaborative.

[0036] In a feasible implementation, the cloud resource pool includes a storage resource pool and a computing resource pool. Among them, the storage resource pool includes various types of data storage devices and services, such as distributed file systems, object storage, block storage, databases, etc., to provide persistent storage and efficient access to massive data for pressure monitoring tasks. For example, the storage resource pool can be used to store the monitoring data reported in real time by the pressure gauge, the intermediate results generated by the task, and the final analysis report. By adopting a distributed storage architecture, the storage resource pool can provide highly scalable, highly available, and high-performance data storage services. The computing resource pool includes various types of computing devices and services, such as virtual machines, containers, serverless functions, etc., to provide powerful computing power and flexible computing modes for pressure monitoring tasks. For example, the computing resource pool can be used to run data processing algorithms, machine learning models, statistical analysis programs, etc., to achieve real-time or offline analysis of monitoring data. By adopting an elastically scalable computing architecture, the computing resource pool can dynamically adjust the configuration of computing resources according to changes in task load, thereby improving resource utilization while reducing usage costs.

[0037] The storage resource pool and the computing resource pool are relatively independent in function, but they are highly coordinated in the execution of the stress monitoring task. Data analysis is inseparable from data storage, and data storage also requires the support of computing power. Therefore, when performing resource planning and deployment, the resource planning module 13 will fully consider the collaborative optimization of storage and computing resources, so that the storage resource pool and the computing resource pool cooperate to complete the stress monitoring task together. For example, for I / O intensive tasks, the resource planning module 13 will give priority to allocating high-throughput storage resources and supporting computing resources; for computing-intensive tasks, it will give priority to allocating high-performance computing resources and matching storage resources. Through collaborative optimization, the overall efficiency of the cloud resource pool can be maximized.

[0038] By subdividing the cloud resource pool into relatively independent and collaborative storage resource pools and computing resource pools, fine management and dynamic optimization of cloud resources are achieved, so that limited resources can more efficiently and flexibly meet the diverse needs of pressure monitoring, providing a foundation for large-scale, high-performance pressure monitoring.

[0039] Further, such as Figure 2 As shown, the embodiment of the present application also includes:

[0040] Traverse the pressure monitoring tasks, build a fog distribution end based on the local topology of the tasks; identify the concurrent task volume of the local topology and determine the resource pool ratio; determine the resource deployment topology based on the fog distribution end and the resource pool ratio.

[0041] In a preferred embodiment, first, the resource planning module 13 traverses all pressure monitoring tasks, analyzes the distribution of pressure gauges involved in each task, and then determines the local topology to which the task belongs. Based on the local topology belonging to different tasks, the resource planning module 13 constructs a corresponding fog distribution terminal on the edge of the network. Each fog distribution terminal is responsible for carrying one or more local topology pressure monitoring tasks to achieve local computing and local storage. The number and location of the fog distribution terminals are dynamically adjusted according to the distribution of the local topology and the density of the tasks.

[0042] While building the fog distribution end, the resource planning module 13 identifies the concurrent task volume of each local topology, that is, the number of pressure monitoring tasks executed simultaneously in the local topology. The concurrent task volume reflects the computing pressure and resource requirements of the local topology. Based on the concurrent task volume, the resource planning module 13 determines the ratio of storage resources and computing resources in the cloud resource pool, that is, how much storage resources and computing resources are allocated to different local topologies. The principle of resource allocation is that the local topology with a larger concurrent task volume has a higher proportion of allocated resources.

[0043] Afterwards, the resource planning module 13 comprehensively considers the constructed fog distribution terminals and the determined resource pool ratio to form the final resource deployment topology. In this topology, each fog distribution terminal obtains the corresponding proportion of storage and computing resources from the cloud resource pool according to the resource ratio of the local topology it is responsible for. At the same time, a high-speed and stable network connection needs to be established between the fog distribution terminal and the cloud resource pool to ensure data transmission and task collaboration between the two parties.

[0044] Through the above adaptive resource planning and deployment, the distribution characteristics and concurrency characteristics of the pressure monitoring task are fully considered. The fog distribution end can be dynamically constructed according to actual needs, and the cloud resource pool can be reasonably configured to form a matching resource deployment topology. It can not only meet the real-time and reliability requirements of pressure monitoring, but also improve resource utilization and reduce operation and maintenance costs.

[0045] Furthermore, the embodiment of the present application also includes:

[0046] The pressure monitoring task is taken as the main task, and an extended task is generated, wherein the extended task is a task extension of the main task; the main task and the extended task are integrated to perform resource planning and deployment compensation, wherein the priority of the main task is higher than that of the extended task.

[0047] In a preferred embodiment, the resource planning module 13 introduces the concepts of main tasks and extended tasks when determining the resource deployment topology to cope with dynamic changes and additional requirements during the pressure monitoring process.

[0048] Specifically, after receiving the pressure monitoring task, the resource planning module 13 regards it as the main task, that is, the core task that needs to be completed at present. At the same time, the resource planning module 13 dynamically generates some extended tasks according to the characteristics and actual needs of the main task. The extended task is an extension and supplement to the main task, which is used to deal with special situations that may occur during the pressure monitoring process. For example, when a pressure gauge monitors abnormal data, it may be necessary to monitor the pressure gauge at a high frequency for a period of time in order to more accurately judge the abnormal trend. This requires the generation of an extended task to expand the monitoring task of the pressure gauge in time and frequency. For example, when a pressure gauge fails and needs to be debugged, it may be necessary to temporarily increase the monitoring intensity of the pressure gauge in order to find and locate the problem in time. This also requires the generation of an extended task to strengthen the monitoring task of the pressure gauge in a targeted manner. For example, when a pressure gauge on a sensing line detects an abnormality, it may be necessary to check all the pressure gauges on the entire line to check whether there are any related problems. This also requires the generation of a series of extended tasks to expand the monitoring range of the relevant pressure gauges.

[0049] After the extended task is generated, the resource planning module 13 integrates it with the main task to form a complete task set. When performing resource planning and deployment, this task set is comprehensively considered to match it with appropriate computing and storage resources. This process is resource planning and deployment compensation, that is, based on the original resource deployment topology, dynamic adjustments and supplements are made according to the newly added extended tasks. When performing resource planning and deployment compensation, the main task and the extended task are not equal. As a core task, the main task always has a higher priority than the extended task. This means that when resource competition occurs, the resource requirements of the main task will be met first, and the extended task can only be taken into account as much as possible when resources permit. This priority strategy can ensure that the main goal of pressure monitoring is not affected, while also taking into account additional monitoring needs when conditions permit.

[0050] By introducing main tasks and extended tasks, and adopting a priority-differentiated resource planning and deployment compensation strategy, dynamic expansion and flexible processing of pressure monitoring tasks are achieved. This enables the pressure monitoring system to more flexibly respond to complex and changing monitoring needs in the actual environment, and improve adaptability and robustness.

[0051] Furthermore, the embodiment of the present application also includes:

[0052] Determine a collaboration mode between a core resource pool and a fog distribution end, wherein the collaboration mode includes a computing dimension and a communication dimension; and establish communication interaction between the core resource pool and the fog distribution end based on the collaboration mode.

[0053] In a preferred implementation, the resource planning module 13 also needs to consider the coordination between the core resource pool and the fog distribution end when determining the resource deployment topology.

[0054] Specifically, the core resource pool and the fog distribution end represent the two computing modes of cloud computing and fog computing respectively. Cloud computing focuses on centralized large-scale computing and storage, while fog computing focuses on distributed real-time computing and local storage. In order to give full play to their respective advantages, it is necessary to establish an efficient coordination mechanism between the core resource pool and the fog distribution end. First, the resource planning module 13 determines the coordination mode between the core resource pool and the fog distribution end. The coordination mode mainly includes two aspects: computing dimension and communication dimension. In terms of computing dimension, the division of labor between the core resource pool and the fog distribution end in the pressure monitoring task is clarified. Generally speaking, the core resource pool is more suitable for performing global task scheduling, data aggregation and deep analysis and other computing-intensive tasks, while the fog distribution end is more suitable for performing localized data collection, real-time processing and rapid response and other delay-sensitive tasks. By reasonably dividing the computing tasks, the conflict of responsibilities and waste of resources between the core resource pool and the fog distribution end can be avoided. In terms of communication dimension, the data interaction mode between the core resource pool and the fog distribution end is clarified. Since the fog distribution end is close to the pressure gauge, the monitoring data collected by it will first be processed and stored locally. Then, according to the task requirements and network conditions, the fog distribution end will selectively upload the data to the core resource pool for further analysis and decision-making. At the same time, the core resource pool also sends certain computing tasks or control instructions to the fog distribution end according to the needs of global scheduling to achieve collaborative execution of tasks. In order to support this two-way data interaction, a high-speed and reliable communication link is established between the core resource pool and the fog distribution end, and a data transmission protocol and synchronization mechanism are designed.

[0055] After determining the collaborative mode, the resource planning module 13 further establishes a communication and interaction mechanism between the core resource pool and the fog distribution end. The first is to define standardized data exchange formats and interface specifications. The core resource pool and the fog distribution end may use different data representation methods and processing logics, so it is necessary to define a unified data exchange format to achieve seamless docking between the two parties. At the same time, it is also necessary to define a set of standardized API interfaces to specify the service calls and message transmission methods between the core resource pool and the fog distribution end to achieve functional interoperability. The second is to build an efficient data transmission channel and scheduling mechanism. There is a complex network topology and dynamic link quality between the core resource pool and the fog distribution end, so it is necessary to build an intelligent data transmission channel that can dynamically select the optimal transmission path and strategy according to the network conditions and data characteristics to ensure the efficiency and reliability of data transmission.

[0056] Through the above methods, close coordination between the core resource pool and the fog distribution end is achieved. While giving full play to the respective advantages of the two computing modes, the deep integration of the two sides in computing, storage, communication, etc. is also achieved to form a unified resource view and scheduling framework. This collaborative mechanism enables pressure monitoring to respond to various complex scenarios more flexibly and efficiently, while improving monitoring performance, it also reduces complexity and operation and maintenance costs.

[0057] Furthermore, the embodiment of the present application also includes:

[0058] The access update data of the interactive pressure gauge updates the access topology, wherein the segmentation of the local topology is synchronously updated with the access distribution of the pressure gauge; if the concurrent distribution characteristics of the pressure monitoring task are updated, or the local segmentation of the access topology is updated, the resource deployment topology is updated, wherein the update of the resource deployment topology includes the expansion of the fog distribution end and the peak and valley coordination of the end resources.

[0059] In a preferred embodiment, first, continuous data interaction is performed with the pressure gauge to obtain access update data of the pressure gauge. These data may include access information of newly added pressure gauges, status changes of existing pressure gauges, and removal or replacement of pressure gauges. The access identification module 11 processes these update data in real time and updates the access topology structure maintained in the system accordingly. When a new pressure gauge is connected or an existing pressure gauge is removed, the boundary of the local topology will change. At this time, the access identification module 11 re-divides the local topology and adjusts the range of each local topology. This process needs to be synchronized with the access distribution changes of the pressure gauge, that is, the redivision of the local topology is triggered when the pressure gauge is connected or removed to ensure that the access topology is consistent with the actual deployment status of the pressure gauge.

[0060] The update of the access topology will further cause the concurrent distribution characteristics of the pressure monitoring task to change. For example, when a large number of pressure gauges are added to a local topology, the concurrency of the monitoring task corresponding to the local topology may increase significantly. Conversely, when a large number of pressure gauges are removed from a local topology, the concurrency of the monitoring task corresponding to the local topology may decrease significantly. The task monitoring module 12 promptly identifies these changes and updates the concurrent distribution feature description of the task accordingly. When the concurrent distribution characteristics of the pressure monitoring task are updated or the local division of the access topology is adjusted, the existing resource deployment topology may be affected. At this time, the resource planning module 13 re-plans and deploys resources and updates the resource deployment topology to adapt to the new task characteristics and topological structure. Among them, the update of the resource deployment topology includes the expansion of the fog distribution end and the peak and valley coordination of the end resources. The expansion of the fog distribution end refers to adding fog computing nodes on the edge of the network according to the increase in local topology and task concurrency to share computing pressure and reduce transmission delays. Peak-valley coordination of end resources refers to dynamically adjusting the configuration of computing and storage resources according to the load changes of different fog nodes, increasing resource supply during peak periods, and recovering idle resources during valley periods to improve resource utilization and reduce operation and maintenance costs.

[0061] Through the dynamic update mechanism, real-time synchronization between the pressure monitoring system and the actual monitoring environment is achieved, which can timely perceive and respond to external changes, dynamically optimize the internal structure and resource allocation, and always maintain the best working state.

[0062] Furthermore, the embodiment of the present application also includes:

[0063] Establish a corresponding connection between the terminal device and the pressure gauge; based on the terminal device, upload the pressure monitoring task and query the distributed monitoring list, wherein the corresponding connection relationship is used as a query constraint; identify the distributed monitoring list, locate abnormal monitoring data, and issue a corresponding connection terminal alarm for the pressure gauge mapped by the abnormal monitoring data.

[0064] In a preferred embodiment, a corresponding connection relationship is established between each terminal device and the pressure gauge. A terminal device can establish a connection with one or more pressure gauges to form a one-to-one or one-to-many mapping relationship. This mapping relationship can be determined based on a variety of factors such as the geographical location of the pressure gauge, the device to which it belongs, and the type of monitoring task, and stored in a mapping table. The purpose of establishing a corresponding connection is to achieve accurate control and query of the pressure gauge by the terminal device, and to avoid misoperation caused by address confusion or authority mismatch.

[0065] After establishing the corresponding connection, the terminal device can interact with the pressure monitoring device. On the one hand, the user can upload the pressure monitoring task to the pressure monitoring device through the terminal device. These tasks can be scheduled, periodic, or immediate and one-time. The user fills in the various parameters of the task on the terminal device, such as the monitoring object, sampling frequency, trigger conditions, etc., and then uploads the task to the pressure monitoring device. The task monitoring module 12 will receive these tasks in real time and process them in the manner described above. On the other hand, the user can also query the execution results of the distributed monitoring list through the terminal device. The distributed monitoring list records the execution of the pressure monitoring task on different pressure gauges, including sampling data, abnormal events, processing results, etc. These monitoring lists may be scattered and stored in different fog nodes or clouds, so it is necessary to provide a unified query interface. The user enters query conditions on the terminal device, such as task number, time range, pressure gauge address, etc., and the pressure monitoring device searches for matching monitoring lists based on these conditions and returns the results to the user.

[0066] In addition to supporting active user inquiries, the pressure monitoring device also provides an abnormal alarm mechanism. The pressure monitoring device identifies abnormal monitoring data in distributed monitoring sheets in real time, such as pressure readings that exceed the threshold, sudden data loss, etc. Once abnormal data is found, the pressure gauge corresponding to the abnormal data is immediately located, and the terminal device connected to the pressure gauge is found through the mapping relationship. Then, the pressure monitoring device automatically sends alarm information to these terminal devices, prompting users to pay attention to abnormal situations and take corresponding measures. This active alarm mechanism can greatly improve real-time performance and reliability, helping users to promptly discover and deal with potential problems.

[0067] By introducing terminal devices and corresponding connection mechanisms, seamless interaction between users and pressure monitoring devices is achieved, allowing users to conveniently, safely and efficiently manage and monitor the working status of the pressure gauge. In particular, for concurrent tasks of multiple terminal devices, the parallel processing capabilities of cloud computing can be used to simultaneously process massive user requests and monitoring data, providing fast and stable service responses. This human-computer interaction capability has greatly improved the intelligence level of the pressure monitoring device, enabling it to better adapt to the needs of actual application scenarios.

[0068] In summary, the distributed pressure monitoring device integrated with cloud computing provided in the embodiments of the present application has the following technical effects:

[0069] The access identification module is used to determine the access topology for the connected distributed pressure gauges, where the access topology identifies the local integration level. Through access identification, the network structure and localization level of distributed pressure monitoring can be accurately grasped, providing an important basis for subsequent resource planning and task scheduling. The task monitoring module is used to receive pressure monitoring tasks, where the pressure monitoring tasks are distributed concurrent tasks of the access topology. By obtaining the monitoring task requirements in real time and classifying and concurrently processing the tasks according to the access topology, the task response speed and processing efficiency are improved. The resource planning module is used to plan and deploy network resources for the cloud resource pool based on the pressure monitoring tasks and the access topology, and determine the resource deployment topology. The resource deployment topology includes the core resource pool and the fog distribution end, which is located at the local edge of the network. The resource deployment topology can be updated. By introducing the fog computing principle, resources are deployed at the edge of the network close to the monitored object, realizing localized processing and real-time response. At the same time, by dynamically updating the resource deployment topology, flexibility and adaptability are improved. The monitoring decision module is used to perform task processing and decision-making on pressure monitoring tasks based on resource deployment topology, generate distributed monitoring orders and store them on the fog distribution end, and sink monitoring tasks to the fog distribution end for local processing and storage, which reduces the computing and storage pressure on the cloud and improves data transmission efficiency and real-time performance. At the same time, through the generation of distributed monitoring orders, the standardization and structuring of monitoring data is achieved, which is convenient for subsequent analysis and application.

[0070] Any steps of the above-described device may be stored as computer instructions or programs in an unlimited computer memory, and may be called and recognized by an unlimited computer processor to implement any device in the embodiments of the present application, without any unnecessary restrictions.

[0071] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these changes and variations.

Claims

1. A distributed pressure monitoring device integrated with cloud computing, characterized in that: The device comprises: An access identification module, the access identification module is used to determine an access topology for an accessed distributed pressure gauge, wherein the access topology is identified by a local integration degree; A task monitoring module, wherein the task monitoring module is used to receive a pressure monitoring task, wherein the pressure monitoring task is a distributed concurrent task of the access topology; A resource planning module, wherein the resource planning module is used to plan and deploy network resources for the cloud resource pool based on the pressure monitoring task and in combination with the access topology, and determine a resource deployment topology, wherein the resource deployment topology includes a core resource pool and a fog distribution end, the fog distribution end is located at the edge side of the network local area, and the resource deployment topology is updateable; A monitoring decision module is used to perform task processing and decision-making on the pressure monitoring task based on the resource deployment topology, generate a distributed monitoring list and perform fog distribution end storage.

2. The distributed pressure monitoring device integrated with cloud computing according to claim 1, characterized in that: The access topology identifier has a local integration level, including: Based on a preset neighborhood interval, the access topology is segmented to determine N local area topologies; Traversing the N local topologies, determining a first integration level based on the distribution quantity, and determining a second integration level based on the neighborhood distance mean; Based on the first integration level and the second integration level, weighted calculation is performed to obtain N local integration levels.

3. The distributed pressure monitoring device integrated with cloud computing according to claim 1, characterized in that: The cloud resource pool includes a storage resource pool and a computing resource pool, and the storage resource pool and the computing resource pool are relatively independent and collaborative.

4. The distributed pressure monitoring device integrated with cloud computing according to claim 1, characterized in that: The determining of the resource deployment topology includes: Traversing the pressure monitoring tasks, and building a fog distribution terminal based on the local topology of the tasks; Identify the concurrent tasks of the local topology and determine the resource pool ratio; The resource deployment topology is determined based on the ratio between the fog distribution end and the resource pool.

5. The distributed pressure monitoring device integrated with cloud computing as claimed in claim 4, characterized in that: The determining the resource deployment topology includes: Taking the pressure monitoring task as the main task and generating an extended task, wherein the extended task is a task extension of the main task; The main task and the extended task are integrated to perform resource planning and deployment compensation, wherein the priority of the main task is higher than that of the extended task.

6. The distributed pressure monitoring device integrated with cloud computing as claimed in claim 1, characterized in that: Determining the resource deployment topology includes: Determine the coordination mode between the core resource pool and the fog distribution end, wherein the coordination mode includes a computing dimension and a communication dimension; Based on the collaborative mode, a communication interaction between the core resource pool and the fog distribution end is established.

7. The distributed pressure monitoring device integrated with cloud computing as claimed in claim 2, characterized in that: include: Access update data of the interactive pressure gauge is updated to update the access topology, wherein the segmentation of the local topology is synchronously updated with the pressure gauge access distribution; If the concurrent distribution characteristics of the pressure monitoring task are updated, or the local segmentation of the access topology is updated, the resource deployment topology is updated, wherein the update of the resource deployment topology includes the expansion of the fog distribution end and the peak-valley coordination of the end resources.

8. The distributed pressure monitoring device integrated with cloud computing as claimed in claim 1, characterized in that: Also includes: Establish corresponding connections between terminal equipment and pressure gauge; Based on the terminal device, uploading the pressure monitoring task and querying the distributed monitoring list are performed, wherein the corresponding connection relationship is used as a query constraint; The distributed monitoring list is identified, abnormal monitoring data is located, and a corresponding connection terminal alarm is issued to the pressure gauge mapped by the abnormal monitoring data.

Images