Intelligent drinking water detection method and system based on multi-process cooperation

Abstract

The invention relates to the technical field of intelligent detection, and provides a multi-process collaborative drinking water intelligent detection method and system. The method comprises the following steps: acquiring 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 correlation analysis on the detection items among the nodes in the network, and outputting an analysis result; identifying an association detection sub-network corresponding to each node according to the result, and outputting a plurality of sub-networks; outputting a data sharing path of each sub-network based on the edge node; and sharing the node detection data of each sub-network through the data sharing path. The technical problems that data transmission is not timely and information islands occur due to the fact that multiple detection tasks lack effective cooperation in the drinking water detection process are solved, and the technical effect that the cooperation efficiency and accuracy of the detection tasks are improved through intelligent task decomposition, correlation analysis and data sharing is achieved.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection technology, and specifically to a multi-process collaborative drinking water intelligent detection method and system. Background Art

[0002] As people's requirements for drinking water quality continue to increase, drinking water testing and assurance have become increasingly important. Traditional drinking water testing methods are mostly performed by a single testing device or process, which has problems such as low efficiency, insufficient testing accuracy, and cumbersome data processing. Especially when faced with complex water quality testing tasks, multiple testing processes often cannot work together, resulting in long testing time, narrow testing range, and inability to feedback results in a timely manner, 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 are mostly focused on the automated control and data analysis of a single process, lacking in-depth research and application of the collaborative work of multiple detection processes, resulting in difficulty in achieving efficient and accurate water quality monitoring in multi-process collaboration; in addition, the traditional detection task decomposition and information sharing mechanism is relatively simple, and each detection link is often carried out in isolation, and various types of detection data and information cannot be effectively integrated. With the increase in the number of detection tasks and the complexity of the detection process, how to achieve collaborative work between multiple detection tasks while ensuring detection accuracy and efficiency has become a technical problem that needs to be solved urgently. Summary of the invention

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

[0005] In view of the above problems, the present application provides a multi-process collaborative intelligent drinking water detection method and system.

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

[0007] Another aspect disclosed in the present application provides a multi-process collaborative intelligent drinking water detection system, the system comprising: a task acquisition module: acquiring a drinking water detection task; a task decomposition module: decomposing the drinking water detection task according to the detection process, outputting a plurality of decomposed detection tasks, and building a detection task network with the plurality of decomposed detection tasks as nodes; a correlation analysis module: performing correlation analysis on the detection items between the nodes in the detection task network, and outputting the correlation analysis results; a subnetwork identification module: identifying the associated detection subnetwork corresponding to each node according to the correlation analysis results, and outputting a plurality of associated detection subnetworks; a shared path determination module: 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; a data sharing module: sharing the detection data of each node in each associated detection subnetwork based on the data sharing path.

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

[0009] The above-mentioned multi-process collaborative drinking water intelligent detection method first obtains a drinking water detection task, and 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; then, a correlation analysis is performed on the detection items between the nodes in the detection task network, and the correlation analysis results are output; on this basis, according to the correlation analysis results, the associated detection subnetwork corresponding to each node is identified, and multiple associated detection subnetworks are output; then, multiple edge nodes are constructed, the multiple edge nodes are connected to the multiple associated detection subnetworks, and the data sharing paths corresponding to each associated detection subnetwork are output based on the multiple edge nodes; finally, based on the data sharing path, the detection data of each node in each associated detection subnetwork is shared, 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 in 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 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

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 The figure is a flow chart of a multi-process collaborative intelligent drinking water detection method in one embodiment.

[0013] Figure 2 This is an architecture diagram of a multi-process collaborative drinking water intelligent detection system in one embodiment.

[0014] Explanation of 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

[0015] The embodiments of the present application provide a multi-process collaborative drinking water intelligent detection method and system to solve the technical problems of untimely data transmission and information islands caused by the lack of effective collaboration between multiple detection tasks in the drinking water detection process.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

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

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a multi-process collaborative drinking water intelligent detection method, the method comprising:

[0019] Get the drinking water testing task.

[0020] In an embodiment of the present application, the system terminal receives drinking water testing tasks to be performed from the outside or the user end. These tasks generally include specific testing requirements for water samples, such as testing the bacterial content, chemical concentration, physical properties, etc. in the water. By obtaining the drinking water testing tasks, the smooth implementation of all subsequent operations can be ensured.

[0021] The drinking water detection task is decomposed according to the detection process, a plurality of decomposed detection tasks are output, and a detection task network is built with the plurality of decomposed detection tasks as nodes.

[0022] In one embodiment, after obtaining the drinking water detection task, the system terminal breaks down the entire drinking water detection task into several specific small tasks, each of which is responsible for detecting a specific indicator or a certain link of water quality. These decomposed tasks are organized into a task network through certain logical relationships, so that each small task can be executed in a suitable order and under appropriate conditions. Specifically, the system terminal subdivides the complex drinking water detection task into multiple subtasks according to the overall detection goals and requirements. For example, the overall detection task may include multiple water quality inspections, 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 heavy metal detection, chloride, pH value, etc. By outputting each subtask as an independent detection unit, these subtasks can be assigned to different detection devices or sensors by the system in an automated manner for execution; then, according to the requirements of each decomposed task, the relationship between the tasks is analyzed. The task dependencies 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 performed after water samples are collected), while some tasks can be performed simultaneously (such as pH and turbidity detection). By taking each decomposed task as a node, the node information includes the task type (such as pH detection, bacterial counting, heavy metal analysis, etc.) and task parameters of the corresponding decomposed task, and taking the dependency and data flow as edges, the system terminal can be organized into a detection task network based on these nodes and edges. Through this network structure, the execution of each task can be efficiently scheduled to 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 task may be used as the input of another task. Therefore, the task network needs to ensure that data can be efficiently shared between nodes. 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 difficulty of execution.

[0023] Perform correlation analysis on the detection items between the nodes in the detection task network, and output the correlation analysis results.

[0024] In one embodiment, in order to evaluate and understand the interrelationships and dependencies between various detection tasks in the detection task network, the system terminal will obtain one or more detection items contained in each task node (i.e., detection task). The number and type of detection items are determined according to the task objectives and experimental design of each node. For example, if a task node is to detect the pH value in water, then the corresponding detection item may be the pH value; if a task node is to detect dissolved oxygen in water, then the corresponding detection item may be dissolved oxygen concentration and water temperature; then, correlation analysis of each node is performed based on these detection items. The purpose of correlation analysis is to identify and quantify the relationship between different nodes, and to help understand which detections are related. The system terminal determines which ones are interrelated and which ones are independent by making overlapping judgments on the detection items of each node, identifying repeated detection items and non-repeated detection items, and calculates the first correlation coefficient and the second correlation coefficient based on these two numbers of items to avoid duplication of work and optimize the detection process; then, the calculated first correlation coefficient and the second correlation coefficient are fused to indicate the degree of correlation between the two nodes, and by adding the fused first correlation coefficient and the second correlation coefficient to the correlation analysis results, the dependency between tasks can be identified, redundant work can be reduced, and the order of task execution can be optimized, which not only improves the execution efficiency of tasks, but also helps the system to achieve more intelligent data processing and resource management.

[0025] Furthermore, the present application provides a method for performing correlation analysis on detection items between nodes in the detection task network and outputting correlation analysis results, the method comprising:

[0026] Wherein, the detection task network includes N nodes, and a correlation analysis matrix is ​​defined according to the N nodes; N groups 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 groups of detection item sets, and a first correlation coefficient and a second correlation coefficient between every two nodes are output, 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; the first correlation coefficient and the second correlation coefficient are calculated by mean square, and a correlation analysis result based on the correlation analysis matrix is ​​output.

[0027] Preferably, when performing a correlation analysis of the detection task network, the system terminal first defines an N*N blank matrix as a correlation analysis matrix based on the N nodes in the detection task network, wherein the rows and columns of the correlation analysis matrix represent these nodes respectively, and each element in the matrix represents the correlation between two task nodes; then, the detection items involved in each node are collected from the detection task network to construct N groups of detection item sets, each detection item set corresponds to a node and includes one or more detection items; then, based on the detection item set of every two nodes, the correlation coefficient of the repeated items of the two nodes is calculated through a pre-constructed first correlation coefficient expression, i.e., the first correlation coefficient. For example, node 1 involves detecting water temperature, and node 2 also involves detecting water temperature, then this water temperature can be regarded as a repeated item. In addition, the correlation coefficient of the non-repeated items of the two nodes is calculated through a pre-constructed second correlation coefficient expression, i.e., the second correlation coefficient. For example, node 1 detects dissolved oxygen concentration, Node 2 detects the heavy metal content, so the dissolved oxygen concentration and heavy metal content are non-repeated items; then, the first correlation coefficient and the second correlation coefficient of the two nodes are fused through the 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 the 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 strength of the relationship between every two task nodes in the task network, and will help the system terminal determine which nodes have a strong correlation. It can be considered to process or share data in parallel during execution, and which nodes need to be executed in sequence to ensure the accuracy of the task, thereby improving the task collaboration efficiency and data sharing capabilities.

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

[0029] The expressions for outputting correlation analysis results include: .

[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 combines the correlation between the two nodes based on repeated detection items and non-repeated detection items, and obtains a final correlation value through mathematical calculation. This value reflects the degree of collaboration between the two nodes on 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: ;in, For Node and The correlation analysis results between them reflect the overall correlation between the two nodes in the detection task, and the value range is usually between 0 and 1. For Node and nodes The first correlation coefficient between them reflects the consistency of the two nodes in the repeated detection content. For Node and nodes The second correlation coefficient between them reflects the indirect relationship or complementarity between the two nodes in non-repetitive detection content. This expression is processed by mean square and square root to obtain 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 collaboration between the nodes, but also provide important decision-making basis for task optimization and path planning, thereby improving the overall efficiency and intelligence level of the detection task network.

[0031] Furthermore, the present application provides an expression for outputting a first correlation coefficient and a 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 detection items between the two nodes. The expression of the first correlation coefficient is specifically as follows: ;in, For Node A set of detection items, indicating the tasks that the node needs to detect. For Node A set of detection items, representing the task of another node, 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, indicating the number of detection items shared by the two nodes. For Node and nodes The minimum value of the set of detection items; the first correlation coefficient expression can be used to calculate the first correlation coefficient of the two nodes, which is used to measure the similarity of the detection tasks of the two nodes. The larger the first correlation coefficient, the more overlaps the detection items of the two nodes; the expression of the second correlation coefficient is used to calculate the second correlation coefficient between the two detection task nodes to reflect the difference of the detection items of the two nodes. The expression of the second correlation coefficient is as follows: ;in, node and nodes The number of items in the union of the detection item sets of , that is, the total number of detection item sets of the two nodes, 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 represents the number of unique detection items of the two nodes. The second correlation coefficient of 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 items detected by the two nodes are.

[0034] According to the correlation analysis result, the associated detection subnetwork corresponding to each node is identified, and multiple associated detection subnetworks are output.

[0035] In one embodiment, the correlation analysis result quantifies the degree of association between each two nodes in the detection task network, for example, through the comprehensive correlation index of the correlation analysis matrix in the correlation analysis result: Representation Node and Based on these results, the system terminal combines the preset correlation index to distinguish whether the correlation between nodes is high enough. When the comprehensive correlation index is greater than or equal to the preset value, the node can be judged and The nodes have a strong enough correlation so that they can be classified into the same sub-network; repeat the above process, traverse all nodes, and check the correlation values ​​of all other nodes related to them, so as to construct multiple correlation detection sub-networks. Each correlation detection sub-network contains a central node and all nodes associated with it, forming a tightly structured sub-task set. The nodes in these sub-networks have a strong correlation, so the detection process can be optimized by sharing data and collaboratively executing tasks, reducing repetitive work and improving overall detection efficiency. In addition, this method can dynamically adjust the correlation threshold and sub-network division according to task changes, further improve the intelligence level of the detection system, and ensure that the detection tasks can be allocated and executed in the best way. The correlation detection sub-network generated in this way can not only reflect the node association 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 subnetwork corresponding to each node according to the correlation analysis result, the method comprising:

[0037] Obtain a preset correlation index; based on 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 subnetwork corresponding to each node, 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.

[0038] Preferably, before dividing the associated detection sub-network, the system terminal first obtains a preset correlation index, which is a threshold used to determine the correlation strength between nodes in the detection task network. The setting of this index is usually determined according to the specific requirements of the task; after obtaining the preset correlation index, the system terminal begins to identify which nodes in the detection task network have correlations that meet the threshold condition based on the correlation analysis matrix in the previous correlation analysis results. For each node in the detection task network , by consulting the correlation analysis matrix, determine all other nodes related to this node Is there a correlation value? Nodes that are greater than or equal to the preset correlation index, if is greater than or equal to the preset correlation index, then the node is considered to be related to the node Then, a correlation detection subnetwork is constructed based on the nodes that meet the preset correlation index. The specific operation is that for each node , taking it as the central node, and linking all nodes whose correlation values ​​meet the preset correlation index Into the sub-network corresponding to the node, thus forming a node It is important to note that in each subnetwork, the correlation value between any two nodes must satisfy Greater than or equal to the preset correlation index, that is, the correlation strength between the nodes in the sub-network must also meet the preset index; this process will be repeated for each node in the detection task network, thereby outputting multiple associated detection sub-networks, each of which is composed of a core node and its highly correlated nodes; ultimately, the construction of these associated detection sub-networks can provide a basis for optimizing task allocation for system terminals, ensuring the structural optimization of the detection task network, while improving the overall detection efficiency and accuracy of the system.

[0039] 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.

[0040] In one embodiment, in the detection task network, each associated detection sub-network is usually composed of multiple nodes, and there is a certain execution process and data transmission relationship between these nodes. In order to ensure that the data within the sub-network can flow efficiently and accurately, it is necessary to build a clear data transmission framework through edge nodes. Specifically, the system terminal first builds edge nodes. These edge nodes are used to coordinate the data flow within the sub-network. The edge nodes can be regarded as a virtual control center or connection point. They are responsible for organizing the data flow in the sub-network and ensuring that all data is transmitted between nodes according to the established process sequence; then, the system terminal clarifies the dependencies between nodes according to the process sequence of the nodes in each associated detection sub-network, and Serialization and sorting are performed, and the corresponding data sharing path is generated based on the sorting result. The data sharing path refers to the data flow route within the associated detection subnetwork, which indicates how the data between nodes flows from the previous node to the next node. The data sharing path defines the starting point of the data within the subnetwork, how the data is gradually transmitted to each node within the subnetwork, and the final collection of the data when it reaches the end of the subnetwork (usually the last node). By integrating these paths into the edge nodes, the system terminal can use the edge nodes as the hub to dynamically manage the data sharing process within each subnetwork, ensuring that data can flow efficiently along the established path, laying the foundation for the scheduling and optimization of subsequent tasks, thereby improving the overall intelligence level and execution efficiency of the detection tasks.

[0041] Further, the present application provides a data sharing path corresponding to each associated detection subnetwork outputted by the multiple edge nodes, the method comprising:

[0042] The nodes of each associated detection sub-network are sorted according to their process sequence, and serialized nodes are output; and according to the serialized nodes, the data sharing path corresponding to each associated detection sub-network is output.

[0043] Preferably, in the associated detection subnetwork, there are certain execution processes and data transfer relationships between nodes, so it is necessary to sort the nodes in each subnetwork according to the node process order, and generate the corresponding data sharing path based on the sorting result; the system terminal first clarifies the dependency relationship of the nodes in the subnetwork, which is the basis for generating the process order. The dependency relationship reflects the logical sequence between the nodes. For example, the output of a 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 subnetwork, 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 nodes can be sorted using a topological sorting method to generate a serialized node list. This list is arranged in the order of node execution, which clarifies the sequence of tasks in the subnetwork. For example, if the task dependency relationship in a subnetwork is node The output is passed to the node , then the node The output is further passed to the node , then the sorted serialized nodes are [ , , ], this arrangement of serialized nodes ensures the smoothness of data transmission and the logic of task execution; after obtaining the serialized node list, the system terminal generates the data sharing path corresponding to the sub-network based on these serialized nodes. The data sharing path defines the data flow mode within the sub-network, clarifies the data source and destination of each node, and establishes a data transmission connection between every two adjacent nodes in the order of the serialized nodes. For example, if the serialized node list is [ , , ], then the data sharing path can be expressed as , indicating that the data is from the node Start, pass to the nodes in turn and nodes In this way, the data sharing path not only clarifies the order of task execution within the sub-network, but also standardizes the direction of data flow, ensuring that the data of each node can be transmitted to the next node at the right time, thereby achieving efficient collaboration and precise execution within the sub-network. The construction of this path provides a clear basis for task scheduling and data management, while also optimizing the data processing efficiency and logical consistency within the sub-network, ensuring the orderliness and efficiency of detection tasks within the sub-network.

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

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

[0046] Optionally, in the associated detection subnetwork, the serialization of nodes is usually performed in the logical execution order of the task. However, some nodes may not require a strict sequential relationship. They can be detected synchronously. These nodes are defined as non-sequential synchronous nodes. To ensure that the data sharing path can correctly reflect the execution characteristics of these synchronous nodes, the serialized nodes need to be further analyzed and processed. The system terminal first judges the generated serialized node list to determine whether it contains non-sequential synchronous nodes. Non-sequential synchronous nodes refer to those nodes that have no logical data dependency and can be detected simultaneously. For example, nodes and nodes If they do not depend on each other's output and do not affect the execution order of subsequent nodes, they can be marked as non-sequential synchronization nodes. In order to judge these nodes, the system terminal will analyze the dependencies of the serialized nodes and check whether there are direct or indirect dependencies between the nodes. If there is no dependency between the two nodes and their dependencies with other nodes are also independent, they can be determined to be non-sequential synchronization nodes. After confirming the existence of non-sequential synchronization nodes in the serialized nodes, the system terminal establishes a synchronous sharing path. The synchronous sharing path is a data transmission path specially designed for these non-sequential synchronization nodes. The purpose is to enable these nodes to start detection tasks at the same time without being restricted by the traditional serialization order. For example, if the node and nodes If they are judged as non-sequential synchronization nodes, they can simultaneously receive the previous node through the synchronous shared path. After the detection is completed, the results are passed to the subsequent nodes respectively. ,The establishment of a synchronous sharing path usually requires to insert a synchronization control point in the data sharing path. This control point is responsible for coordinating the data reception and result output of the synchronization nodes. Finally, the system terminal will update the original data sharing path according to the synchronous sharing path. The purpose of the update is to integrate the synchronous execution characteristics of the synchronization nodes into the overall data sharing path, so that the data flow path can accurately reflect the actual execution logic between the nodes. Specifically, the original data sharing path is gradually connected in the order of the serialized nodes, while the updated path will establish a parallel transmission channel between the synchronization nodes. For example, the original path may be , but in identifying and After synchronization is possible, the path will be updated to ,in, Indicates that the two nodes are synchronized. Through the above process, the data sharing path can not only reflect the logical order of the task, but also efficiently support the parallel execution of the synchronization nodes, thereby improving the overall efficiency of the detection task. This path update mechanism enables the system terminal to maximize the parallel advantages of the synchronized nodes while ensuring the accuracy of data transmission, speeding up the completion of the task and optimizing the resource utilization of the system.

[0047] The detection data of each node in each associated detection subnetwork is shared based on the data sharing path.

[0048] In one embodiment, in an associated detection subnetwork, each node generates specific detection data when performing a detection task, and this data usually needs to flow or be shared between other nodes in the subnetwork 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, so that the data of each node is transmitted to the nodes that need to use the data according to the established path, ensuring the smoothness and collaborative efficiency of the detection task. Specifically, the data sharing path has clarified the data transmission relationship between the nodes in the subnetwork and the direction of data flow. The system terminal controls the data sharing process through the path, collects the detection data generated by each node and uploads it 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 transmits the data to the corresponding target node 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 node. For example, for a node in a detection task network (Water sampling), (pH value detection), (Heavy metal content detection), if the data sharing path is defined as , then the system terminal will The generated water sampling data is transmitted to , for pH value detection, and at the same time After completing the pH value detection task, the data generated by the detection is passed to , used for subsequent heavy metal content detection, this data transmission method ensures the sequential execution of drinking water testing tasks 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] The embodiment of the present application first obtains a drinking water detection task, and 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; then, the detection items between the nodes in the detection task network are subjected to correlation analysis, and the correlation analysis results are output; on this basis, according to the correlation analysis results, the associated detection subnetwork corresponding to each node is identified, and multiple associated detection subnetworks are output; then, multiple edge nodes are constructed, the multiple edge nodes are connected to the multiple associated detection subnetworks, and the data sharing paths corresponding to each associated detection subnetwork are output based on the multiple edge nodes; finally, the detection data of each node in each associated detection subnetwork is shared based on the data sharing path. These technical effects jointly solve the technical problems of untimely data transmission and information islands caused by the lack of effective collaboration between multiple detection tasks in the drinking water detection process, and realize the technical effect 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 previous embodiment, Figure 2 As shown, the present application provides a multi-process collaborative drinking water intelligent detection system, the system comprising:

[0052] Task acquisition module 11: acquire drinking water detection task; task decomposition module 12: 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; correlation analysis module 13: perform correlation analysis on the detection items between the nodes in the detection task network, and output the correlation analysis results; sub-network identification module 14: identify the associated detection sub-network corresponding to each node according to the correlation analysis results, and output multiple associated detection sub-networks; shared path determination module 15: 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; data sharing module 16: share the detection data of each node in each associated detection sub-network based on the data sharing path.

[0053] Furthermore, the correlation analysis module 13 is also used to perform 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 groups of detection item sets corresponding to the N nodes are collected; correlation identification is performed according to the correlation analysis matrix and the N groups of detection item sets, and a first correlation coefficient and a second correlation coefficient are output 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; the first correlation coefficient and the second correlation coefficient are calculated by mean square, and a correlation analysis result based on the correlation analysis matrix is ​​output.

[0055] Furthermore, the correlation analysis module 13 is also used to perform the following method:

[0056] 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 .

[0057] Furthermore, the correlation analysis module 13 is also used to perform the following method:

[0058] 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.

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

[0060] Obtain a preset correlation index; based on 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 subnetwork corresponding to each node, 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.

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

[0062] The nodes of each associated detection sub-network are sorted according to their process sequence, and serialized nodes are output; and according to the serialized nodes, the data sharing path corresponding to each associated detection sub-network is output.

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

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

[0065] It should be noted that the above-mentioned sequence 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-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0067] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations 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 modifications and variations.

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; The detection data of each node in each associated detection subnetwork is shared based on the data sharing path.

2. The method according to claim 1, characterized in that Perform correlation analysis on the detection items between each node in the detection task network, and output the correlation analysis results. include: 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.

3. The method according to claim 2, 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 .

4. The method according to claim 3, 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.

5. 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.

6. 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.

7. The method according to claim 6, 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.

8. 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 7 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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