Intelligent Detection Method and System for Drinking Water with Multi-Process Collaboration

Abstract

This application relates to the field of intelligent detection technology, and provides an intelligent drinking water detection method and system for multi-process collaboration. The method includes: obtaining a drinking water detection task; decomposing the task according to the detection process, outputting a plurality of decomposed tasks, and constructing a detection task network; performing a correlation analysis on the detection items between the nodes in the network, and outputting an analysis result; identifying the associated detection sub-networks corresponding to each node according to the result, and outputting a plurality of sub-networks; outputting the data sharing paths of each sub-network based on the edge nodes; and sharing the node detection data of each sub-network through the data sharing paths. This application solves the technical problems of untimely data transmission and information islands caused by the lack of effective collaboration between multiple detection tasks during the drinking water detection process, and realizes the technical effect of improving the collaborative efficiency and accuracy of detection tasks through intelligent task decomposition, correlation analysis, and data sharing.

Description

Technical Field

[0001] This application relates to the field of intelligent detection technology, and specifically relates to an intelligent detection method and system for drinking water with multi-process collaboration. Background Art

[0002] With the continuous improvement of people's requirements for the quality of drinking water, the detection and guarantee of drinking water have become increasingly important. Traditional drinking water detection methods are mostly carried out by single detection equipment or processes, which have problems such as low efficiency, insufficient detection accuracy, and cumbersome data processing. Especially when facing complex water quality detection tasks, multiple detection processes often cannot work together, resulting in long detection time, narrow detection range, and inability to timely feedback results, affecting the real-time and accuracy of water quality monitoring.

[0003] In recent years, with the rapid development of Internet of Things technology, artificial intelligence, and big data analysis, intelligent and automated drinking water quality monitoring systems have gradually become an effective means to solve this problem. However, existing technologies mostly focus on the automated control and data analysis of single processes, lacking in-depth research and application of the collaboration of multiple detection processes, resulting in difficulties in achieving efficient and accurate water quality monitoring in multi-process collaboration; in addition, the traditional detection task decomposition and information sharing mechanisms are relatively simple, and each detection link often operates in isolation, unable to effectively integrate various detection data and information. With the increase in the number of detection tasks and the complexity of detection processes, how to achieve collaborative work between multiple detection tasks while ensuring detection accuracy and efficiency has become a technical problem to be solved urgently. Summary of the Invention

[0004] This application provides an intelligent detection method and system for drinking water with multi-process collaboration, aiming to solve the technical problems of untimely data transmission and information islands caused by the lack of effective collaboration between multiple detection tasks during the drinking water detection process.

[0005] In view of the above problems, this application provides an intelligent detection method and system for drinking water with multi-process collaboration.

[0006] In the first aspect disclosed in the present application, a multi-process collaborative intelligent drinking water detection method is provided. The method includes: obtaining a drinking water detection task; decomposing the drinking water detection task according to the detection process to output multiple decomposed detection tasks, and building a detection task network with the multiple decomposed detection tasks as nodes; performing a correlation analysis on the detection items between the nodes in the detection task network to output a correlation analysis result; identifying an associated detection sub-network corresponding to each node according to the correlation analysis result to output multiple associated detection sub-networks; constructing multiple edge nodes, connecting the multiple edge nodes with the multiple associated detection sub-networks, and outputting a data sharing path corresponding to each associated detection sub-network based on the multiple edge nodes; and sharing the detection data of each node in each associated detection sub-network based on the data sharing path.

[0007] In another aspect disclosed in the present application, a multi-process collaborative intelligent drinking water detection system is provided. The system includes: a task acquisition module: obtaining a drinking water detection task; a task decomposition module: decomposing the drinking water detection task according to the detection process to output multiple decomposed detection tasks, and building a detection task network with the multiple decomposed detection tasks as nodes; a correlation analysis module: performing a correlation analysis on the detection items between the nodes in the detection task network to output a correlation analysis result; a sub-network identification module: identifying an associated detection sub-network corresponding to each node according to the correlation analysis result to output multiple associated detection sub-networks; a shared path determination module: constructing multiple edge nodes, connecting the multiple edge nodes with the multiple associated detection sub-networks, and outputting a data sharing path corresponding to each associated detection sub-network based on the multiple edge nodes; and a data sharing module: sharing the detection data of each node in each associated detection sub-network based on the data sharing path.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The above-mentioned intelligent drinking water detection method for multi-process collaboration first obtains a drinking water detection task, decomposes the drinking water detection task according to the detection process, outputs multiple decomposed detection tasks, and builds a detection task network with the multiple decomposed detection tasks as nodes; subsequently, performs a correlation analysis on the detection items between each node in the detection task network, and outputs a correlation analysis result; on this basis, according to the correlation analysis result, identifies the associated detection sub-network corresponding to each node, and outputs multiple associated detection sub-networks; then, constructs multiple edge nodes, connects the multiple edge nodes with the multiple associated detection sub-networks, and outputs the data sharing path corresponding to each associated detection sub-network based on the multiple edge nodes; finally, shares the detection data of each node in each associated detection sub-network based on the data sharing path, so as to solve the technical problems of untimely data transmission and information islands caused by the lack of effective collaboration between multiple detection tasks during the drinking water detection process, and achieve the technical effect of improving the collaborative efficiency and accuracy of detection tasks through intelligent task decomposition, correlation analysis and data sharing.

[0010] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0012] Figure 1 It is a schematic flowchart of an intelligent drinking water detection method for multi-process collaboration in an embodiment.

[0013] Figure 2 It is an architecture diagram of an intelligent drinking water detection system for multi-process collaboration in an embodiment.

[0014] Description of the reference numerals: task acquisition module 11, task decomposition module 12, correlation analysis module 13, sub-network identification module 14, shared path determination module 15, data sharing module 16. Detailed Description of the Embodiments

[0015] Embodiments of the present application provide an intelligent drinking water detection method and system with multi-process collaboration, which solve the technical problems of untimely data transmission and information islands caused by the lack of effective collaboration among multiple detection tasks during the drinking water detection process.

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 shown, the present application provides an intelligent drinking water detection method with multi-process collaboration, and the method includes:

[0019] Obtain a drinking water detection task.

[0020] In the embodiments of the present application, the system terminal receives the drinking water detection tasks that need to be executed from the external or the user side. These tasks usually include specific detection requirements for water samples. For example, detecting the bacterial content, chemical substance concentration, physical properties, etc. in the water. By obtaining the drinking water detection tasks, it can ensure the smooth progress of all subsequent operations.

[0021] Decompose the drinking water detection task according to the detection process, output multiple decomposed detection tasks, and build a detection task network with the multiple decomposed detection tasks as nodes.

[0022] In one embodiment, after obtaining a drinking water detection task, the system terminal disassembles the entire drinking water detection task into several specific small tasks. Each small task is responsible for detecting a specific index or a certain link of the water quality. These decomposed tasks are organized into a task network through a certain logical relationship, so that each small task can be executed in a suitable order and conditions. Specifically, the system terminal divides the complex drinking water detection task into multiple subtasks according to the overall detection objectives and requirements. For example, the overall detection task may include multiple inspections of water quality, such as microbial detection, chemical composition analysis, physical property testing, etc. Each inspection task will be further refined. For example, chemical composition analysis may be decomposed into multiple subtasks such as detecting heavy metals, chlorides, pH value, etc. By outputting each subtask as an independent detection unit, these subtasks can be assigned to different detection devices or sensors for execution in an automated manner by the system. Subsequently, according to the requirements of each decomposed task, analyze the relationships between tasks. The dependencies of tasks may include sequential execution, parallel execution, or data interaction between each other. For example, some detection tasks may have to be executed after other tasks are completed (such as chemical composition analysis can only be carried out after water sample collection), while some tasks can be carried out simultaneously (such as pH value and turbidity detection). By taking each decomposed task as a node, the information of this node includes the task type of the corresponding decomposed task (such as pH value detection, bacteria count, heavy metal analysis, etc.) and task parameters, and taking the dependencies and data flow directions as edges. Based on these nodes and edges, the system terminal can organize a detection task network. Through this network structure, it is possible to efficiently schedule the execution of each task and ensure that all tasks can proceed smoothly according to the established process. This network not only considers the execution order between tasks but also includes the data flow between tasks. For example, the result of a certain task may be used as the input of another task. Therefore, the task network needs to ensure that data can be efficiently shared between each node. Through such task decomposition and network construction, a complex drinking water detection task can be divided into manageable and parallel subtasks, improving the overall detection efficiency and reducing the execution difficulty.

[0023] Perform a correlation analysis on the detection items between each node in the detection task network and output the correlation analysis result.

[0024] In one embodiment, to evaluate and understand the interrelationships and dependencies among various detection tasks in the detection task network, the system terminal acquires one or more detection items included in each task node (i.e., detection task). The quantity and type of detection items are determined according to the task objectives and experimental designs of each node. For example, if a certain task node is to detect the pH value in water, then the corresponding detection item can be the pH value; if a certain task node is to detect the dissolved oxygen in water, then the corresponding detection items can be the dissolved oxygen concentration and water temperature. Subsequently, based on these detection items, correlation analysis is performed on each node. The purpose of correlation analysis is to identify and quantify the relationships between different nodes, helping to understand which detections are related to each other and which are independent. The system terminal determines duplicate and non-duplicate detection items by overlapping judgment of the detection items of each node, and calculates a first correlation coefficient and a second correlation coefficient based on the numbers of these two types of items to avoid duplicate labor and optimize the detection process. After that, the calculated first correlation coefficient and second correlation coefficient are fused to represent the degree of correlation between two nodes. By adding the fused first correlation coefficient and second correlation coefficient to the correlation analysis result, the dependency relationships between tasks can be identified, redundant work can be reduced, and the task execution order can be optimized, which not only improves the execution efficiency of tasks but also helps the system achieve more intelligent data processing and resource management.

[0025] Further, the present application provides a method for performing correlation analysis on the detection items between various nodes in the detection task network and outputting a correlation analysis result, including:

[0026] Wherein, the detection task network includes N nodes, and a correlation analysis matrix is defined according to the N nodes; N sets of detection item sets corresponding to the N nodes are collected, and correlation identification is performed according to the correlation analysis matrix and the N sets of detection item sets, and a first correlation coefficient and a second correlation coefficient between each two nodes are output, wherein the first correlation coefficient is a correlation coefficient based on duplicate items, and the second correlation coefficient is a correlation coefficient based on non-duplicate items; mean square calculation is performed on the first correlation coefficient and the second correlation coefficient, and a correlation analysis result based on the correlation analysis matrix is output.

[0027] Preferably, when performing the correlation analysis of the detection task network, the system terminal first defines an N*N blank matrix based on the N nodes in the detection task network as the correlation analysis matrix. The rows and columns of this correlation analysis matrix represent these nodes respectively, and each element in the matrix represents the correlation between two task nodes. Subsequently, the detection items involved in each node are collected from the detection task network to construct N sets of detection item sets. Each detection item set corresponds to a node and includes one or more detection items. After that, based on the detection item sets of every two nodes, the correlation coefficient of the duplicate items between the two nodes, that is, the first correlation coefficient, is calculated through a pre-constructed first correlation coefficient expression. For example, if node 1 involves detecting the water temperature and node 2 also involves detecting the water temperature, then this water temperature can be regarded as a duplicate item. In addition, the correlation coefficient of the non-duplicate items between the two nodes, that is, the second correlation coefficient, is calculated through a pre-constructed second correlation coefficient expression. For example, if node 1 detects the dissolved oxygen concentration and node 2 detects the heavy metal content, then this dissolved oxygen concentration and heavy metal content are non-duplicate items. Then, the first correlation coefficient and the second correlation coefficient of the two nodes are fused through a mean square calculation expression. The purpose of the mean square calculation is to standardize these correlation coefficients to obtain a more representative comprehensive correlation coefficient. Finally, by mapping the fused comprehensive correlation coefficient to the positions corresponding to these two nodes in the correlation analysis matrix, the content in the correlation analysis matrix is gradually completed. When the correlation calculation between all nodes is completed, the system terminal adds the completed correlation analysis matrix to the correlation analysis result as the final output. This correlation analysis result will reflect the relationship strength between every two task nodes in the task network, and will help the system terminal judge which nodes have strong correlations and can consider parallel processing or data sharing during execution, and which nodes need to be executed in sequence to ensure the accuracy of the task, thereby improving the collaboration efficiency and data sharing ability of the task.

[0028] Furthermore, the present application provides an expression for outputting the correlation analysis result, including:

[0029] The expression for outputting the correlation analysis result includes: 。

[0030] Optionally, the expression of the correlation analysis result is used to quantify the comprehensive correlation between two nodes in the detection task network. It synthesizes the correlations between two nodes based on duplicate detection items and non-duplicate detection items, and obtains a final correlation value through mathematical calculations. This value reflects the closeness of cooperation between the two nodes in the detection task, and provides a reference for subsequent steps such as task optimization and data sharing path planning. The specific expression is as follows: ; where is node and The result of the correlation analysis between reflects the overall correlation degree of two nodes in the detection task. The value range is usually between 0 and 1. is the first correlation coefficient between node and node which reflects the consistency degree of the two nodes in the repeated detection content. is the second correlation coefficient between node and node which reflects the indirect relationship or complementarity of the two nodes in the non-repeated detection content. Through the processing of the mean square and square root, this expression obtains a unified correlation value, that is, the comprehensive correlation coefficient of the two nodes. This comprehensive correlation coefficient can not only quantify the degree of close cooperation between nodes, but also provide an important decision-making basis for task optimization and path planning, thereby improving the overall efficiency and intelligent level of the detection task network.

[0031] Furthermore, the present application provides expressions for outputting the first correlation coefficient and the second correlation coefficient between every two nodes, including:

[0032] The expressions for outputting the first correlation coefficient and the second correlation coefficient between every two nodes include: .

[0033] Optionally, the expression of the first correlation coefficient is used to calculate the first correlation coefficient between two detection task nodes to reflect the degree of repetition of the detection items between the two nodes. The specific expression of the first correlation coefficient is as follows: ; where is the set of detection items of node representing the tasks required to be detected by this node, is the set of detection items of node representing the tasks of another node, is the intersection of the set of detection items of node and node representing the repeated detection items of the two nodes, is the number of items in the intersection, representing the number of detection items shared by the two nodes, is the minimum value of the set of detection items of node and node ; Through the expression of the first correlation coefficient, the first correlation coefficient of the two nodes can be calculated to measure the similarity degree of the detection tasks of the two nodes. The larger the first correlation coefficient, the more overlapping the detection items of the two nodes; The expression of the second correlation coefficient is used to calculate the second correlation coefficient between two detection task nodes to reflect the difference of the detection items of the two nodes. The specific expression of the second correlation coefficient is as follows: ; where Node and the number of items in the union of the sets of detection items of the node , that is, the total number of the sets of detection items of the two nodes is the number of items in the set of detection items of node that do not belong to node . is the number of items in the set of detection items of node that do not belong to node , representing the number of detection items unique to each of the two nodes; the second correlation coefficient between the two nodes can be calculated through the expression of the second correlation coefficient, which is used to measure the degree of difference in the content of the detection tasks of the two nodes. The smaller the second correlation coefficient, the more similar the detection items of the two nodes are.

[0034] According to the correlation analysis result, identify the associated detection sub-network corresponding to each node, and output multiple associated detection sub-networks.

[0035] In one embodiment, the correlation analysis result quantifies the degree of association between every two nodes in the detection task network. For example, through the comprehensive correlation index in the correlation analysis matrix of the correlation analysis result, it represents the strength of the association between node and . Based on these results, the system terminal combines the preset correlation index to distinguish whether the association between nodes is high enough. When the comprehensive correlation index is greater than or equal to this preset value, it can be determined that nodes and have a strong enough association, so they can be grouped into the same sub-network; repeat the above process, by traversing all nodes and checking the association values of all other nodes related to them, multiple associated detection sub-networks are constructed. Each associated detection sub-network contains a central node and all nodes associated with it, forming a tightly structured set of sub-tasks. The nodes in these sub-networks have strong associations, so the detection process can be optimized by sharing data and collaborating to execute tasks, reducing repetitive work, and improving the overall detection efficiency. In addition, this method can dynamically adjust the association threshold and sub-network division according to task changes, further improving the intelligence level of the detection system, ensuring that the detection tasks can be allocated and executed in the optimal way. The associated detection sub-networks generated in this way can not only reflect the node associations in the detection task network, but also serve as the basis for optimizing task scheduling, reducing resource waste, and improving system performance.

[0036] Furthermore, the present application provides a method for identifying the associated detection sub-network corresponding to each node according to the correlation analysis result, including:

[0037] Obtain a preset correlation metric; according to the correlation analysis result, identify the nodes in the detection task network that are greater than or equal to the preset correlation metric, and construct an associated detection sub-network corresponding to each node, where the correlation metric of any node in the associated detection sub-network corresponding to each node is greater than or equal to the preset correlation metric.

[0038] Preferably, before partitioning the associated detection sub-network, the system terminal first obtains a preset correlation metric, which is a threshold for judging the association strength between nodes in the detection task network, and the setting of this metric is usually determined according to the specific requirements of the task; after obtaining the preset correlation metric, the system terminal starts to identify which nodes in the detection task network have a correlation that meets the threshold condition according to the correlation analysis matrix in the previous correlation analysis result. For each node in the detection task network , by referring to the correlation analysis matrix, judge all other nodes related to this node whether they have a correlation value greater than or equal to the preset correlation metric. If it is greater than or equal to the preset correlation metric, then the node is considered to be associated with the node ; subsequently, construct an associated detection sub-network according to the nodes that meet the preset correlation metric. The specific operation is that for each node , taking it as the central node, include all nodes whose correlation value with it meets the preset correlation metric into the sub-network corresponding to this node. In this way, an associated detection sub-network centered on the node is formed. It should be noted that in each sub-network, the correlation value between any two nodes must meet greater than or equal to the preset correlation metric, that is, the correlation strength between the nodes within the sub-network also needs to meet the preset metric; this process will be repeated for each node in the detection task network, so as to output multiple associated detection sub-networks, and these sub-networks are all composed of a core node and its highly correlated nodes; finally, the construction of these associated detection sub-networks can provide a basis for the system terminal to optimize task allocation, ensure the structural optimization of the detection task network, and at the same time improve the overall detection efficiency and accuracy of the system.

[0039] Construct multiple edge nodes, connect the multiple edge nodes with the multiple associated detection sub-networks, and output the data sharing path corresponding to each associated detection sub-network based on the multiple edge nodes.

[0040] In one embodiment, in the detection task network, each associated detection sub-network generally consists of multiple nodes, and there is a certain execution process and data transfer relationship among these nodes. To ensure the efficient and accurate flow of data within the sub-network, it is necessary to build a clear data transmission framework through edge nodes. Specifically, the system terminal first constructs edge nodes, which are used to uniformly coordinate the data flow within the sub-network. The edge nodes can be regarded as a virtual control center or connection point, and they are responsible for organizing the data flow in the sub-network to ensure that all data is transferred between nodes in accordance with the established process sequence. Subsequently, the system terminal determines the dependency relationships between nodes according to the process sequence of the nodes in each associated detection sub-network, serializes and sorts the nodes, and generates corresponding data sharing paths based on the sorting results. The data sharing path refers to the data flow route within the associated detection sub-network, indicating how data flows from the previous node to the next node between nodes. The data sharing path defines the starting point of the data within the sub-network, how the data is gradually transferred to each node within the sub-network, and the final aggregation of the data when it reaches the end point of the sub-network (usually the last node). By integrating these paths into the edge nodes, the system terminal can use the edge nodes as the center to dynamically manage the data sharing process within each sub-network, ensuring that the data can flow efficiently according to the established paths, laying a foundation for the scheduling and optimization of subsequent tasks, thereby improving the overall intelligent level and execution efficiency of the detection tasks.

[0041] Furthermore, the present application provides a method for outputting the data sharing path corresponding to each associated detection sub-network based on the multiple edge nodes, the method comprising:

[0042] Sorting according to the node process sequence of each associated detection sub-network to output serialized nodes; and outputting the data sharing path corresponding to each associated detection sub-network according to the serialized nodes.

[0043] Preferably, in the association detection sub-network, there is a certain execution process and data transfer relationship between nodes. Therefore, it is necessary to sort according to the node process order of each sub-network and generate the corresponding data sharing path based on the sorting result. The system terminal first clarifies the dependency relationship between nodes within the sub-network, which is the basis for generating the process order. The dependency relationship reflects the logical sequence between nodes. For example, the output of a certain node may be the input of another node, or the execution of some nodes must wait for the completion of other nodes. By analyzing the task attributes and dependencies of each node in the sub-network, a directed acyclic graph (DAG) can be constructed. Each node in the graph represents a specific detection task, and the edges in the graph represent the data transfer relationship between tasks. Based on the directed acyclic graph, the topological sorting method can be used to sort the nodes and generate a serialized node list. This list is arranged according to the execution order of the nodes, clarifying the sequence of tasks in the sub-network. For example, if the task dependency relationship in a certain sub-network is that the output of node is passed to node , and then the output of node is further passed to node , then the sorted serialized nodes are , , . This arrangement of serialized nodes ensures the smoothness of data transfer and the logic of task execution. After obtaining the serialized node list, the system terminal generates the corresponding data sharing path for the sub-network based on these serialized nodes. The data sharing path defines the data flow mode within the sub-network, clarifying the data source and data destination of each node. According to the order of the serialized nodes, a data transfer connection is established between every two adjacent nodes. For example, if the serialized node list is , , , then the data sharing path can be expressed as , indicating that the data starts from node and is sequentially passed to node and node . In this way, the data sharing path not only clarifies the task execution order within the sub-network but also standardizes the data flow direction, ensuring that the data of each node can be passed to the next node at the appropriate time, thereby achieving efficient cooperation and precise execution within the sub-network. The construction of this path provides a clear basis for task scheduling and data management, and at the same time optimizes the data processing efficiency and logical consistency within the sub-network, ensuring the orderliness and efficiency of the detection tasks within the sub-network.

[0044] Furthermore, the method provided by this application includes:

[0045] Determine whether the serialized nodes include non-sequential synchronization nodes, where the non-sequential synchronization nodes are nodes that can be synchronously detected; if the serialized nodes include non-sequential synchronization nodes, establish a synchronous shared path; update the data shared path according to the synchronous shared path.

[0046] Optionally, in the association detection sub-network, the serialization of nodes is usually carried out according to the logical execution order of tasks. However, some nodes may not require a strict sequential relationship and can be detected synchronously. These nodes are defined as non-sequential synchronization nodes. To ensure that the data shared path can correctly reflect the execution characteristics of these synchronous nodes, further analysis and processing of the serialized nodes are required. The system terminal first judges the generated list of serialized nodes to determine whether it contains non-sequential synchronization nodes. Non-sequential synchronization nodes refer to those nodes that have no data dependencies logically and can be detected simultaneously. For example, node and node If they neither depend on each other's outputs nor affect the execution order of subsequent nodes, they can be marked as non-sequential synchronization nodes. To judge these nodes, the system terminal will analyze the dependency relationships of the serialized nodes and check whether there are direct or indirect dependencies between the nodes. If there is no dependency between two nodes and their dependency relationships with other nodes are also independent, they can be determined as non-sequential synchronization nodes; after confirming that there are non-sequential synchronization nodes in the serialized nodes, the system terminal establishes a synchronous shared path. The synchronous shared path is a data transfer path specifically designed for these non-sequential synchronization nodes, aiming to enable these nodes to start the detection tasks simultaneously without being restricted by the traditional serialization order. For example, if node and node are determined to be non-sequential synchronization nodes, they can receive the data of the previous node simultaneously through the synchronous shared path and pass the results to the subsequent nodes respectively after the detection is completed. The establishment of the synchronous shared path usually requires inserting a synchronous control point in the data shared path, and this control point is responsible for coordinating the data reception and result output of the synchronous nodes; finally, the system terminal will update the original data shared path according to the synchronous shared path. The purpose of the update is to integrate the synchronous execution characteristics of the synchronous nodes into the overall data shared path so that the data flow path can accurately reflect the actual execution logic between the nodes. Specifically, the original data shared path is gradually connected according to the order of the serialized nodes, and the updated path will establish parallel transfer channels between the synchronous nodes. For example, the original path may be , but after identifying that and can be executed synchronously, the path will be updated to , where indicates that these two nodes are synchronized. Through the above process, the data sharing path can not only reflect the logical sequence of tasks, but also efficiently support the parallel execution of synchronized nodes, thereby improving the overall efficiency of the detection task. This path update mechanism enables the system terminal to maximize the utilization of the parallelism advantage of synchronizable nodes while ensuring the accuracy of data transmission, accelerating the task completion speed, and optimizing the resource utilization rate of the system.

[0047] Share the detection data of each node in each associated detection sub-network based on the data sharing path.

[0048] In one embodiment, in an associated detection sub-network, each node generates specific detection data when performing a detection task, and this data usually needs to flow or be shared among other nodes in the sub-network to support the task execution of subsequent nodes. Based on the data sharing path, the system terminal can effectively organize and manage the transmission of this data, enabling the data of each node to be transmitted to the nodes that need to use this data according to the established path, ensuring the smoothness and collaborative efficiency of the detection task. Specifically, the data sharing path has clearly defined the data transmission relationship and the data flow direction among the nodes in the sub-network. The system terminal controls the data sharing process through this path, collects and uploads the detection data generated by each node to the edge node. The edge node will analyze which subsequent nodes the detection data of the current node needs to be transmitted to according to the definition of the data sharing path, and then transmit this data to the corresponding target nodes according to the path. During the data transmission process, the system terminal will mark each data stream to ensure that the data can be correctly routed to the target nodes. For example, for a node (water sample collection), (pH value detection), (heavy metal content detection), if the data sharing path is defined as , then the system terminal will transmit the generated water sample collection data to for its pH value detection. At the same time, after completes the pH value detection task, it will then transmit the detected data to for subsequent heavy metal content detection. This data transmission method ensures the sequential execution of the drinking water detection task and the seamless flow of data, thereby further optimizing the overall performance of the detection system.

[0049] In summary, the embodiments of the present application have at least the following technical effects:

[0050] In the embodiment of the present application, a drinking water detection task is first obtained, and the drinking water detection task is decomposed according to the detection process, and a plurality of decomposed detection tasks are output. A detection task network is built with the plurality of decomposed detection tasks as nodes; subsequently, a correlation analysis is performed on the detection items between the nodes in the detection task network, and a correlation analysis result is output; on this basis, according to the correlation analysis result, the associated detection sub-network corresponding to each node is identified, and a plurality of associated detection sub-networks are output; thereafter, a plurality of edge nodes are constructed, the plurality of edge nodes are connected to the plurality of associated detection sub-networks, and a data sharing path corresponding to each associated detection sub-network is output based on the plurality of edge nodes; finally, the detection data of each node in each associated detection sub-network is shared based on the data sharing path. These technical effects together solve the technical problems of untimely data transmission and information islands caused by the lack of effective cooperation between multiple detection tasks in the process of drinking water detection, and achieve the technical effects of improving the collaborative efficiency and accuracy of detection tasks through intelligent task decomposition, correlation analysis, and data sharing.

[0051] Embodiment 2, based on the same inventive concept as the multi-process collaborative drinking water intelligent detection method in the foregoing embodiment, as Figure 2 shown, the present application provides a multi-process collaborative drinking water intelligent detection system, and the system includes:

[0052] A task acquisition module 11: to acquire a drinking water detection task; a task decomposition module 12: to decompose the drinking water detection task according to the detection process, output a plurality of decomposed detection tasks, and build a detection task network with the plurality of decomposed detection tasks as nodes; a correlation analysis module 13: to perform a correlation analysis on the detection items between the nodes in the detection task network, and output a correlation analysis result; a sub-network identification module 14: to identify the associated detection sub-network corresponding to each node according to the correlation analysis result, and output a plurality of associated detection sub-networks; a sharing path determination module 15: to construct a plurality of edge nodes, connect the plurality of edge nodes to the plurality of associated detection sub-networks, and output a data sharing path corresponding to each associated detection sub-network based on the plurality of edge nodes; a data sharing module 16: to share the detection data of each node in each associated detection sub-network based on the data sharing path.

[0053] Further, the correlation analysis module 13 is further used to execute the following method:

[0054] Among them, the detection task network includes N nodes, and a correlation analysis matrix is defined according to the N nodes; N sets of detection item sets corresponding to the N nodes are collected; correlation recognition is performed according to the correlation analysis matrix and the N sets of detection item sets, and a first correlation coefficient and a second correlation coefficient between every two nodes are output, where the first correlation coefficient is a correlation coefficient based on duplicate items, and the second correlation coefficient is a correlation coefficient based on non-duplicate items; mean square calculation is performed on the first correlation coefficient and the second correlation coefficient, and a correlation analysis result based on the correlation analysis matrix is output.

[0055] Further, the correlation analysis module 13 is further configured to execute the following method:

[0056] The expression for outputting the correlation analysis result includes: ; where is the correlation analysis result between node and , is the first correlation coefficient between node and node , is the second correlation coefficient between node and node .

[0057] Further, the correlation analysis module 13 is further configured to execute the following method:

[0058] The expressions for outputting the first correlation coefficient and the second correlation coefficient between every two nodes include: ; where is the detection item set of node , is the detection item set of node , is the intersection of the detection item sets of node and node , representing the duplicate detection items of the two nodes, is the number of items in the intersection, is the minimum value of the detection item sets of node and node , is the number of items in the union of the detection item sets of node and node , is the number of items in the detection item set of node that do not belong to node , is the number of items in the detection item set of node that do not belong to node .

[0059] Furthermore, the sub-network identification module 14 is further configured to execute the following method:

[0060] Obtain a preset correlation index; according to the correlation analysis result, identify the nodes in the detection task network that are greater than or equal to the preset correlation index, and construct an associated detection sub-network corresponding to each node, where the correlation index of any node in the associated detection sub-network corresponding to each node is greater than or equal to the preset correlation index.

[0061] Furthermore, the shared path determination module 15 is further configured to execute the following method:

[0062] Sort according to the node process order of each associated detection sub-network, and output serialized nodes; according to the serialized nodes, output the data sharing path corresponding to each associated detection sub-network.

[0063] Furthermore, the shared path determination module 15 is further configured to execute the following method:

[0064] Determine whether the serialized nodes include non-sequential synchronization nodes, where the non-sequential synchronization nodes are nodes that can be synchronously detected; if the serialized nodes include non-sequential synchronization nodes, establish a synchronous shared path; update the data sharing path according to the synchronous shared path.

[0065] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0066] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0067] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. 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 equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A multi-process collaborative drinking water intelligent detection method, characterized in that: The method comprises: Get drinking water testing tasks; Decomposing the drinking water testing task according to the testing process, outputting a plurality of decomposed testing tasks, and building a testing task network with the plurality of decomposed testing tasks as nodes; Performing correlation analysis on detection items between various nodes in the detection task network, and outputting correlation analysis results; According to the correlation analysis result, identifying the associated detection subnetwork corresponding to each node, and outputting multiple associated detection subnetworks; Constructing a plurality of edge nodes, connecting the plurality of edge nodes with the plurality of associated detection subnetworks, and outputting a data sharing path corresponding to each associated detection subnetwork based on the plurality of edge nodes; Sharing the detection data of each node in each associated detection subnetwork based on the data sharing path; Performing correlation analysis on detection items between nodes in the detection task network and outputting correlation analysis results, the method includes: Wherein, the detection task network includes N nodes, and a correlation analysis matrix is ​​defined according to the N nodes; Collecting N sets of detection items corresponding to the N nodes; Perform correlation identification according to the correlation analysis matrix and the N sets of detection items, and output a first correlation coefficient and a second correlation coefficient between every two nodes, wherein the first correlation coefficient is a correlation coefficient based on repeated items, and the second correlation coefficient is a correlation coefficient based on non-repeated items; Perform mean square calculation on the first correlation coefficient and the second correlation coefficient, and output a correlation analysis result based on the correlation analysis matrix.

2. The method according to claim 1, characterized in that The expressions for outputting correlation analysis results include: ； in, For Node and The results of correlation analysis between For Node and nodes The first correlation coefficient between For Node and nodes The second correlation coefficient between .

3. The method according to claim 2, characterized in that The expressions for outputting the first correlation coefficient and the second correlation coefficient between every two nodes include: ； in, For Node The set of detection items, For Node The set of detection items, For Node and nodes The intersection of the detection item sets of , represents the repeated detection items of the two nodes, is the number of items in the intersection, For Node and nodes The minimum value of the set of detection items, node and nodes The number of items in the union of the detection item sets, For Node The set of detection items does not belong to the node The number of items, For Node The set of detection items does not belong to the node The number of items.

4. The method according to claim 1, characterized in that According to the correlation analysis result, identifying the associated detection subnetwork corresponding to each node, the method includes: Get preset correlation indicators; According to the correlation analysis result, nodes in the detection task network whose correlation index is greater than or equal to a preset correlation index are identified, and an associated detection subnetwork corresponding to each node is constructed, wherein the correlation index of any node in the associated detection subnetwork corresponding to each node is greater than or equal to the preset correlation index.

5. The method according to claim 1, characterized in that Based on the multiple edge nodes outputting the data sharing path corresponding to each associated detection subnetwork, the method includes: Sort the nodes of each associated detection sub-network according to their process order and output the serialized nodes; According to the serialized nodes, the data sharing path corresponding to each associated detection sub-network is output.

6. The method according to claim 5, characterized in that The method comprises: Determining whether the serialized nodes include non-sequential synchronization nodes, wherein the non-sequential synchronization nodes are nodes that can be detected synchronously; If the serialized nodes include non-serialized synchronization nodes, establishing a synchronous shared path; The data sharing path is updated according to the synchronization sharing path.

7. The multi-process collaborative drinking water intelligent detection system is characterized by: The steps for implementing the multi-process collaborative drinking water intelligent detection method according to any one of claims 1 to 6 include: Task acquisition module: obtain drinking water testing tasks; Task decomposition module: decompose the drinking water detection task according to the detection process, output a plurality of decomposed detection tasks, and build a detection task network with the plurality of decomposed detection tasks as nodes; Correlation analysis module: performs correlation analysis on the detection items between the nodes in the detection task network and outputs the correlation analysis results; Subnetwork identification module: identifying the associated detection subnetwork corresponding to each node according to the correlation analysis result, and outputting multiple associated detection subnetworks; A shared path determination module: constructs a plurality of edge nodes, connects the plurality of edge nodes with the plurality of associated detection subnetworks, and outputs a data sharing path corresponding to each associated detection subnetwork based on the plurality of edge nodes; Data sharing module: sharing the detection data of each node in each associated detection sub-network based on the data sharing path.

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