Topology graph construction method and device, equipment, storage medium and program product

Abstract

The application provides a topology graph construction method and device, equipment, a storage medium and a program product, which can be applied to the technical field of data mining. The method comprises the following steps: for any device node and any target node in a plurality of nodes, determining a reference relationship associated with the device node and the target node; for any target field type in at least one field type, determining a first target field from a plurality of first candidate fields of the device node based on the target field type; determining a second target field from a plurality of second candidate fields of the target node based on the target field type, and determining a matching degree between the first target field and the second target field; determining a target relationship between the device node and the target node based on the reference relationship and at least one matching degree; establishing an edge between any two nodes with the target relationship, and constructing a topology graph based on the plurality of nodes and the plurality of edges; and further improving the accuracy of topology graph construction.

Description

Technical Field

[0001] This application relates to the field of data mining, specifically to knowledge graph construction, network topology analysis, and aerospace information correlation analysis, and more specifically to a topology graph construction method, apparatus, device, medium, and program product. Background Technology

[0002] In scenarios such as knowledge graph construction and network topology analysis, it is necessary to identify the relationships between nodes and construct a topology graph for subsequent topology analysis.

[0003] Traditional relationship identification methods lack flexibility and are prone to errors due to irrelevant field matching, resulting in low relationship identification accuracy and consequently affecting the accuracy of topology graph construction. Summary of the Invention

[0004] In view of the above problems, embodiments of this application provide a topology graph construction method, apparatus, device, medium, and program product.

[0005] According to a first aspect of this application, a method for constructing a topology graph is provided, comprising: for any device node and any target node among a plurality of nodes, determining at least one reference relationship associated with both the device node and the target node, wherein the reference relationship has at least one corresponding field type; for any target field type among the at least one field type, determining a first target field from a plurality of first candidate fields of the device node based on the target field type, wherein the first candidate field indicates the detection attribute information of the detection device represented by the device node; determining a second target field from a plurality of second candidate fields of the target node based on the target field type, and determining the degree of matching between the first target field and the second target field, wherein the second candidate field indicates the attribute information of the detection target represented by the target node; determining a target relationship between the device node and the target node based on the reference relationship and at least one degree of matching; establishing an edge between any two nodes with a target relationship, and constructing a topology graph based on the plurality of nodes and the plurality of edges.

[0006] According to an embodiment of this application, determining a target relationship between a device node and a target node based on a reference relationship and at least one matching degree includes: determining a target matching pattern from multiple candidate matching patterns based on the target field type; matching a first target field with a second target field based on the matching scale indicated by the target matching pattern to obtain a matching score characterizing the matching degree; obtaining a target matching score between the device node and the target node based on at least one matching score; and using the reference relationship as the target relationship if the target matching score is greater than a preset threshold.

[0007] According to an embodiment of this application, there are multiple target relationships, and each target relationship has a corresponding importance score. The method further includes: responding to the fact that the multiple target relationships include at least one second target relationship that conflicts with any first target relationship, performing a weighted fusion of the importance score of the first target relationship matching, the relationship identification accuracy associated with the first target relationship, and the target matching score associated with the first target relationship to obtain a first confidence score of the first target relationship; for any second target relationship among the at least one second target relationship, performing a weighted fusion of the importance score of the second target relationship matching, the relationship identification accuracy associated with the second target relationship, and the target matching score associated with the second target relationship to obtain a second confidence score of the second target relationship; and determining a target relationship from the first target relationship and the at least one second target relationship based on the first confidence score and the at least one second confidence score.

[0008] According to embodiments of this application, the reference relationship has an environment dependency identifier, which indicates whether reference detection environment information needs to be introduced; based on the reference relationship and at least one matching degree, the target relationship between the device node and the target node is determined, including: fusing the matching scores corresponding to each of the at least one matching degree to obtain a target matching score; if the target matching score is greater than a preset threshold and the environment dependency identifier indicates that reference detection environment information needs to be introduced, determining the current detection environment information associated with both the detection device and the detection target; based on the current detection environment information and the reference detection environment information, obtaining the detection state, which indicates whether the detection device's detection of the detection target is restricted; if the detection state indicates that the detection is not restricted, the reference relationship is used as the target relationship.

[0009] According to an embodiment of this application, the reference relationship has corresponding field groups, and the method further includes: in response to receiving a relationship identification task for a device node and a target node, determining a target computing node from multiple candidate computing nodes based on the task allocation characteristics of the relationship identification task, each candidate computing node having corresponding task allocation characteristics; and allocating the relationship identification task to the target computing node; wherein the task allocation characteristics include the location information of the detection device, the location information of the detection target, the first node type of the device node, the second node type of the target node, and at least one of the field groups.

[0010] According to embodiments of this application, the relationship types of the target relationship include steady-state relationships and dynamic relationships. The field types associated with steady-state relationships include at least one of device type, detection range type, and state type. The field types associated with dynamic relationships include at least one of location type, time type, and state type.

[0011] According to a second aspect of this application, a topology graph construction apparatus is provided, comprising: a first determining module, configured to determine at least one reference relationship associated with both a device node and a target node among a plurality of nodes, wherein the reference relationship has at least one corresponding field type; a second determining module, configured to determine a first target field from a plurality of first candidate fields of a device node based on any target field type among the at least one field type, wherein the first candidate field indicates detection attribute information of a detection device represented by the device node; a third determining module, configured to determine a second target field from a plurality of second candidate fields of a target node based on the target field type, and determine the degree of matching between the first target field and the second target field, wherein the second candidate field indicates attribute information of a detection target represented by the target node; a fourth determining module, configured to establish an edge between any two nodes with a target relationship, and construct a topology graph based on the plurality of nodes and the plurality of edges; and a graph construction module, configured to establish a connecting edge between any two nodes with a target relationship, thereby constructing a topology graph based on the plurality of nodes and the plurality of connecting edges.

[0012] According to a third aspect of this application, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0013] According to a fourth aspect of this application, a computer-readable storage medium is also provided, on which a computer program or instructions are stored, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0014] According to a fifth aspect of this application, a computer program product is also provided, comprising a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0015] The topology graph construction method provided in this application has at least the following advantages compared with related technologies:

[0016] A predefined correspondence between reference relationships and at least one field used to determine the reference relationship is established. Based on the field type corresponding to the reference relationship, a first target field is determined from multiple first candidate fields of the device node, and a second target field is determined from multiple second candidate fields of the target node. Then, based on the reference relationship and the matching degree of the first and second target fields, the corresponding target relationship is determined. Since the reference relationship predefines the fields that need to be matched between the device node and the target node, blind comparison of all fields of the device node and the target node is avoided, thereby reducing relationship identification errors caused by irrelevant field matching and improving the reliability of relationship identification. Then, a topology graph is constructed based on the target relationship, the device node, and the target node. Therefore, since the topology graph is constructed based on reliable target relationships, the edges in the topology graph can more realistically reflect the actual association between the device node and the target node, thereby improving the accuracy and reliability of the topology graph structure. Attached Figure Description

[0017] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 The illustrations depict application scenarios of the topology graph construction method, apparatus, device, medium, and program product according to embodiments of this application.

[0019] Figure 2 A flowchart illustrating a topology graph construction method according to an embodiment of this application is shown schematically.

[0020] Figure 3 A flowchart illustrating the determination of target relationships according to an embodiment of this application is shown schematically;

[0021] Figure 4 A flowchart illustrating the allocation relationship identification task according to an embodiment of this application is shown schematically;

[0022] Figure 5 This schematic diagram illustrates a structural block diagram of a topology graph construction apparatus according to an embodiment of the present application;

[0023] Figure 6 A block diagram schematically illustrates an electronic device suitable for implementing a topology graph construction method according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0028] The technical problems to be solved by the embodiments of this application will be described below.

[0029] In scenarios such as knowledge graph construction and network topology analysis, it is necessary to identify the relationships between nodes and construct a topology graph for subsequent topology analysis.

[0030] Traditional relationship identification methods lack flexibility and are prone to errors due to irrelevant field matching, resulting in low relationship identification accuracy and consequently affecting the accuracy of topology graph construction.

[0031] Furthermore, traditional methods can only identify fixed relationships between nodes, but not dynamic relationships, resulting in inaccurate and incomplete identification. Moreover, the relationship identification results obtained by traditional methods may not match actual physical reality.

[0032] Furthermore, traditional relationship recognition tasks mostly employ a single-machine computing model, which can easily lead to slow speeds and lag when processing a large number of relationship recognition tasks, resulting in a need to improve the computational efficiency of relationship recognition tasks.

[0033] To address at least one of the aforementioned technical problems, embodiments of this application provide a topology graph construction method. This method predefines a reference relationship and a correspondence between it and at least one field used to determine the reference relationship. Based on the field type corresponding to the reference relationship, a first target field is determined from multiple first candidate fields of a device node, and a second target field is determined from multiple second candidate fields of a target node. Then, based on the reference relationship and the matching degree of the first and second target fields, a corresponding target relationship is determined. Since the reference relationship predefines the fields that need to be matched between the device node and the target node, blind comparison of all fields of the device node and the target node is avoided, thereby reducing relationship identification errors caused by irrelevant field matching and improving the reliability of relationship identification. Then, a topology graph is constructed based on the target relationship, the device node, and the target node. Therefore, since the topology graph is constructed based on reliable target relationships, the edges in the topology graph can more realistically reflect the actual association between the device node and the target node, thereby improving the accuracy and reliability of the topology graph structure.

[0034] Figure 1 The diagram illustrates an application scenario of the topology graph construction method according to an embodiment of this application. For example... Figure 1 As shown, application scenario 100 according to an embodiment of this application may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. For example, a user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send information, etc.

[0035] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be electronic devices such as smartphones, wearable devices, personal computers, intelligent voice interaction devices, smart home appliances, intelligent vehicles, in-vehicle terminals, aircraft, unmanned vending terminals, and extended reality devices. Extended reality devices can include virtual reality devices, augmented reality devices, and mixed reality devices. A client application for the target application can be installed and run on the terminal devices. This target application can include, but is not limited to, financial transaction applications, payment applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, and social media platform software (these are just examples). Furthermore, this application embodiment does not limit the form of the target application, and it can include, but is not limited to, applications, mini-programs, etc., installed on the terminal devices, and can also be in the form of web pages.

[0036] Server 105 can be a server providing various services, such as a backend management server supporting websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services such as cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and basic cloud computing services such as big data. The server can be the backend server of the aforementioned target application, used to provide backend services to the clients of the target application.

[0037] It should be noted that the topology graph construction method provided in this application embodiment can generally be executed by server 105 and / or terminal devices 101-103. Accordingly, the topology graph construction apparatus provided in this application embodiment can generally be disposed in server 105 and / or terminal devices 101-103.

[0038] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0039] Figure 2 A flowchart illustrating a topology graph construction method according to an embodiment of this application is shown schematically. Figure 2 As shown, the topology graph construction method 200 according to the embodiments of this application may include steps S210 to S250.

[0040] In step S210, for any device node and any target node among the multiple nodes, at least one reference relationship is determined that is associated with both the device node and the target node.

[0041] According to embodiments of this application, multiple nodes may include device nodes and target nodes.

[0042] Device nodes are used to characterize detection equipment, which may include radar, photoelectric detection equipment, sonar equipment, communication base stations, etc. Target nodes are used to characterize detection targets, which may include drones, aircraft, vehicles, ships, etc.

[0043] A reference relationship has at least one corresponding field type. The field type can include at least one of the following: device type, location type, detection range type, and status type. For example, a reference relationship can be "communication reachable", "detectable", "currently detectable", "currently undetectable", "communication unreachable", etc.

[0044] For example, the device node and the target node have applicable pre-configured relationship definition rules, which include at least one reference relationship between the device node and the target node. At least one reference relationship can be determined from the relationship definition rules. Based on this, by pre-configuring the relationship definition rules uniformly, without needing to configure them separately for each pair of nodes, the reusability and efficiency of relationship identification are improved.

[0045] In other embodiments, multiple nodes can be obtained in advance by preprocessing the heterogeneous node dataset. A heterogeneous node dataset refers to a collection containing nodes of various different types. The heterogeneous node dataset can be obtained using a multi-source adaptation interface. A multi-source adaptation interface is an interface used to obtain heterogeneous node data from multiple different data sources and convert this data into a unified format.

[0046] For example, preprocessing may include deduplication, cleaning, and standardization operations on nodes in a heterogeneous node dataset. Deduplication refers to removing duplicate nodes. Cleaning refers to fixing missing fields and correcting formatting errors in fields. Standardization refers to standardizing node field naming and attribute values. Based on this, invalid nodes in the heterogeneous node dataset can be filtered out, outputting a set of valid nodes with uniform format and complete fields.

[0047] In step S220, for any target field type among at least one field type, a first target field is determined from a plurality of first candidate fields of the device node based on the target field type.

[0048] According to an embodiment of this application, the first candidate field indicates the detection attribute information of the detection device represented by the device node.

[0049] For example, a field that matches the target field type from multiple first candidate fields can be used as the first target field. For instance, if the target field type is device type, the field "drone" related to the device type is determined from the first candidate fields of the device node as the first target field. Here, "drone" represents the type of target supported by the detection device represented by the device node. If the target field type is detection range type, the maximum detection distance field is determined from the first candidate fields as the first target field. The field value of the first target field is the detection attribute information of the detection device under that field type.

[0050] In step S230, based on the target field type, a second target field is determined from multiple second candidate fields of the target node, and the degree of matching between the first target field and the second target field is determined.

[0051] According to embodiments of this application, a field matching the target field type from a plurality of second candidate fields can be used as the second target field. For example, if the target field type is a device type, the field "drone" corresponding to the device type in the second candidate fields can be used as the second target field; if the target field type is a detection range type, the distance between the target and the detection device represented by the device node can be determined from the second candidate fields as the second target field. The field value of the second target field is the detection attribute information of the target under that field type.

[0052] For example, the degree of matching between the first target field and the second target field can be obtained by matching the target field type using the matching pattern.

[0053] In step S240, the target relationship between the device node and the target node is determined based on the reference relationship and at least one degree of matching.

[0054] For example, if the sum of multiple matching degrees exceeds a preset threshold, the reference relationship is used as the target relationship between the device node and the target node.

[0055] In step S250, an edge is established between any two nodes that have a target relationship, and a topology graph is constructed based on multiple nodes and multiple edges.

[0056] According to an embodiment of this application, an edge is constructed by connecting the device node and the target node in the direction from the device node to the target node; then, a topology graph is constructed based on multiple nodes and multiple edges, and the relationship type and target relationship corresponding to each edge are labeled in the topology graph. Here, the topology graph can also be referred to as a directed graph.

[0057] Based on this, by labeling the relationship type and target relationship, the topology graph not only shows the connections between nodes, but also the specific semantics of the connections, which enhances the information expression capability of the topology graph and facilitates subsequent semantic query and analysis.

[0058] In other embodiments, the display scale of nodes can be dynamically adjusted based on the number of connected edges in the topology graph. Specifically, the number of connected edges of a node is positively correlated with its display scale; that is, the more connected edges a node has, the larger the node appears.

[0059] Based on this, by showing a positive correlation between the node display ratio and the number of connected edges, key nodes with dense connections can be highlighted intuitively, making it easier to quickly identify key nodes in the topology graph and improving the analysis efficiency and readability of the topology graph.

[0060] In the embodiments of this application, a reference relationship and a correspondence between at least one field used to determine the reference relationship are predefined. Based on the field type corresponding to the reference relationship, a first target field is determined from a plurality of first candidate fields of the device node and a second target field is determined from a plurality of second candidate fields of the target node. Then, based on the reference relationship and the degree of matching between the first target field and the second target field, the corresponding target relationship is determined. Since the reference relationship predefines the fields that need to be matched between the device node and the target node, blind comparison of all fields of the device node and the target node is avoided, thereby reducing relationship identification errors caused by irrelevant field matching and improving the reliability of relationship identification.

[0061] Then, a topology graph is constructed based on the target relationship, device nodes, and target nodes. Since the topology graph is constructed based on reliable target relationships, the edges in the topology graph can more realistically reflect the actual relationship between device nodes and target nodes, thereby improving the accuracy and reliability of the topology graph structure.

[0062] Furthermore, the aforementioned relation definition rule can be in the form of a rule six-tuple, where the rule six-tuple R indicates the fields to be matched between the device node and the target node and their field types, as well as the set of environment dependencies indicating whether environment dependencies need to be introduced. The rule priority P corresponding to the relation definition rule, and the preset threshold of the reference relation. Rule identifier And constraint binding set B. Based on this, the structured rule six-tuples make the configuration of relation definition rules flexible and reusable.

[0063] Among them, the environment-dependent set It may include at least one reference environment information used to assist in identifying relationships between nodes. The reference environment information may include at least one of the following: preset meteorological conditions, terrain occlusion information, hydrological conditions, and electromagnetic environment information. Environment dependency set It can be empty. (In the environment dependency set) In the case of non-empty, an environment dependency set needs to be introduced. The reference environment information is used to assist in identifying the relationships between nodes.

[0064] The rule priority P can be an integer; the larger the value, the higher the priority. Preset threshold. Used to filter reference relationships with confidence levels below this threshold. Rule identifier This is a globally unique rule identifier used to ensure that each reference relationship is uniquely referenced. Matching patterns can include exact matching, fuzzy matching, range matching, regular expression matching, and custom matching.

[0065] A constraint binding set B can contain one or more binding units b. Each binding unit can include a field type, a matching pattern M corresponding to the field type, and a custom constraint function F. The custom constraint function F can be activated only when the matching type is a custom match. The custom constraint function is used to execute the judgment logic for custom relation identification.

[0066] Fields corresponding to a field type can be represented in the form of field paths. Fields in a field path can be nested. Field paths can be represented using dotted path notation, for example, a field path of "device.capability.targetType" indicates access to a subfield under a device node field.

[0067] Based on this, dotted path notation can flexibly express nested fields at any depth, allowing relation definition rules to be precisely bound to specific fields at any level, thereby enhancing the flexibility and adaptability of relation definition rules.

[0068] For example, the field path can employ a two-level positioning method combining node selectors and field paths. The node selector is used to determine the nodes to which the relation definition rules apply, and the field path is used to extract the specific attributes involved in the matching from the selected nodes.

[0069] For example, the field path of the device node (source node) can be represented as S_path=(Q_s,p_s), and the field path of the target node can be represented as T_path=(Q_t,p_t). Here, Q_s and Q_t are the source node selector and target node selector, respectively, used to determine the relationship definition rules that adapt to the device node and target node; p_s and p_t are the field paths corresponding to the device node and target node, respectively.

[0070] Figure 3 A flowchart illustrating the determination of target relationships according to an embodiment of this application is shown schematically.

[0071] Combination Figure 3 In some embodiments, determining the target relationship between the device node and the target node based on the reference relationship and at least one degree of matching may include steps S310 to S340:

[0072] In step S310, the target matching pattern is determined from multiple candidate matching patterns based on the target field type.

[0073] According to embodiments of this application, multiple candidate matching patterns may include exact matching patterns, fuzzy matching patterns, range matching patterns, regular expression matching patterns, and custom matching patterns. Different field types can correspond to different matching patterns.

[0074] For example, for matching between device type fields, an exact match mode can be used; for matching between location type fields, a range match mode can be used; and for matching between name type fields, a fuzzy match mode can be used.

[0075] Based on this, by configuring different matching modes for different types of fields, it is possible to flexibly adapt to the matching needs of various fields such as exact values, text, numerical ranges, and format strings, thereby improving the accuracy and adaptability of matching.

[0076] In step S320, based on the matching scale indicated by the target matching pattern, the first target field and the second target field are matched to obtain a matching score that characterizes the degree of matching.

[0077] According to embodiments of this application, different matching modes correspond to different degrees of matching. The matching scale refers to the specific standard used to determine whether the first target field and the second target field match and the degree of matching. For example, the matching scale for the exact matching mode is whether the field values ​​are completely identical; the matching scale for the fuzzy matching mode is whether the similarity reaches a preset similarity threshold; the matching scale for the range matching mode is whether the field value falls within a preset range; the matching scale for the regular expression matching mode is whether the field value conforms to a preset regular expression; and the matching scale for the custom matching mode is whether the return value of the custom constraint function meets preset conditions.

[0078] For example, the first target field With the second target field Match score between It can be obtained in the following ways:

[0079] ;

[0080] in, This refers to the field obtained from the source node via the field path of the source node (the first target field). The field obtained from the target node through the path of the target node (second target field); This is an indicator function that returns 1 if the condition in parentheses is true, and 0 otherwise. This refers to the matching pattern. This indicates that the matching mode is an exact match mode. This indicates that the matching mode is fuzzy matching mode. This indicates that the matching mode is a range matching mode. This indicates that the matching pattern is a regular expression matching pattern. This indicates that the matching mode is a custom matching mode.

[0081] This is the similarity function, which can be edit distance, cosine similarity, etc. This is the similarity threshold; a match is considered successful if the degree of matching reaches this threshold. and This defines the range boundary of a field, used for determining the range inclusion of numeric fields. This is a regular expression defined based on the target node, used for pattern matching scenarios. Indicates to as well as Perform regular expression matching, where, This is the matching function in regular expressions.

[0082] Custom constraint functions are a set of matching logic pre-written by users or developers based on business needs. They receive source field values ​​and target field values, return a boolean value, and use the boolean value as the matching score.

[0083] Taking exact match as an example, if the first target field corresponding to the device node is "drone" and the second target field corresponding to the target node is "drone", the matching score can be calculated as 1 according to the exact match formula.

[0084] In step S330, a target matching score between the device node and the target node is obtained based on at least one matching score.

[0085] According to an embodiment of this application, when there is at least one binding unit in the relationship definition rule corresponding to the device node and the target node, that is, when at least one matching score is obtained, at least one matching score can be fused by logical AND, logical OR, or weighted mode to obtain the target matching score.

[0086] The logical AND pattern requires all bound units to match, and the matching results of each bound unit are fused through logical AND operation to obtain the target matching score, which is suitable for strict matching scenarios.

[0087] For example, the logical AND operation can be represented as:

[0088] ;

[0089] in, It is a logical AND operator, which means that the overall result is true only when all conditions are true at the same time (1). is the sequence number of the bound unit, and n is the number of bound units in the relation definition rule. It is the first The matching type corresponding to each binding unit. It is based on the first Each binding unit extracts field values ​​from the source node (device node). It is based on the first Each binding unit extracts field values ​​from the target node; This refers to the first The matching type pairs corresponding to each binding unit and Perform a match.

[0090] The logical OR operation requires any of the bound units to match, making it suitable for scenarios with loose matching.

[0091] For example, the logical OR operation can be represented as:

[0092] ;

[0093] in, It is a logical OR operator.

[0094] For example, the target match score is obtained by merging the various match scores using a weighted approach. The process can be represented as:

[0095] ;

[0096] in, For the first The weight corresponding to the matching score of each binding unit. A preset threshold associated with the bound unit.

[0097] In step S340, if the target matching score is greater than or equal to a preset threshold, the reference relationship is used as the target relationship.

[0098] According to embodiments of this application, a preset threshold is used as a criterion for determining whether a reference relationship can be used as a target relationship, and can be set and adjusted according to actual needs. For example, the preset threshold can be 0.8.

[0099] If the target matching score is less than the preset matching threshold, the relationship will be filtered out.

[0100] In the embodiments of this application, a reference relationship is determined as a target relationship only when the sum of multiple matching scores is higher than a preset threshold. Based on this, low-scoring relationships filtered by the preset threshold can reduce irrelevant relationships and improve the reliability of the target relationship and the quality of the topology graph.

[0101] Furthermore, the target relationship types include steady-state relationships and dynamic relationships. The field types associated with steady-state relationships include at least one of device type, detection range type, and state type, while the field types associated with dynamic relationships include at least one of location type, time type, and state type.

[0102] For example, the device type field can be "device category" or "device model"; the detection range type field can be "maximum detection distance" or "coverage radius"; the status type field can be "online status" or "working mode" to reflect the real-time status; the location type field can be "longitude" or "latitude"; and the time type field can be the latest time.

[0103] Based on this, the identified target relationship types include steady-state relationships and dynamic relationships. This allows us to reflect both the long-term stable inherent connections between nodes through steady-state relationships and the real-time connections between nodes as their relationships change over time and with the environment through dynamic relationships, thus more completely expressing the multidimensional relationships between nodes.

[0104] In some embodiments, the reference relationship has an environment-dependent identifier, which indicates whether reference probe environment information needs to be introduced; based on the reference relationship and at least one matching degree, determining the target relationship between the device node and the target node includes:

[0105] First, the matching scores corresponding to at least one degree of matching are combined to obtain the target matching score.

[0106] According to embodiments of this application, the environment dependency identifier corresponds to the environment dependency set in the aforementioned relationship definition rules. When the environment dependency set is not empty, the environment dependency identifier indicates that reference detection environment information needs to be introduced; when the environment dependency set is empty, the environment dependency identifier indicates that reference detection environment information does not need to be introduced. The reference detection environment information can be preset environmental constraints associated with the reference relationship, such as building shading conditions, meteorological conditions (rainfall, fog, wind speed), and terrain conditions.

[0107] For example, the target matching score can be obtained by summing at least one matching degree.

[0108] Then, if the target matching score is greater than a preset threshold and the environment dependency indicator indicates that reference detection environment information needs to be introduced, the current detection environment information associated with both the detection device and the detection target is determined.

[0109] According to an embodiment of this application, the current detection environment information is real-time acquired environmental data associated with the spatial area where the detection device and the detection target are located, such as current meteorological data, building spatial distribution data, and terrain data.

[0110] For example, the current detection environment information could be that there are obstacles obstructing the detection equipment and the detection target.

[0111] When the environment dependency identifier indicates that reference probe environment information is not required, i.e., reference probe environment information is not required to assist in relationship identification, relationship identification is based solely on the fields of the device node and the fields of the device node, and the reference relationship is directly used as the target relationship when the target matching score is greater than a preset threshold.

[0112] Based on this, the need to introduce reference probe environment information can be determined by the environment dependency identifier, so as to realize the on-demand access of reference probe environment information. This can support complex scenarios that require environmental constraints, and avoid introducing unnecessary computational overhead in simple scenarios that do not need to consider environmental information.

[0113] In other embodiments, spatial resampling, temporal alignment, and outlier repair operations can be performed on the current detection environment information to achieve standardized processing of the information. Spatial resampling refers to converting environmental data from different sources, with different spatial resolutions, or different coordinate systems to the same spatial grid, resolution, or coordinate system. Temporal alignment refers to unifying environmental data with different time frequencies, timestamps, or time zones to the same time reference. Outlier repair refers to detecting and correcting outliers (such as noisy data) in the current detection environment information. Based on this, the usability and accuracy of the current detection environment information can be improved.

[0114] Then, based on the current detection environment information and the reference detection environment information, the detection status is obtained.

[0115] According to embodiments of this application, there can be multiple reference detection environment information sets, and correspondingly, there can also be multiple detection states. The detection state indicates whether the detection device's detection of the target is limited. For example, if the reference detection environment information indicates that there are no building obstructions, but the current detection environment information shows that there are building obstructions between the device and the target, then the detection state indicates that detection is limited. If the reference detection environment information requires visibility to be greater than a visibility threshold, but the current meteorological data shows that the current visibility is less than or equal to the visibility threshold, then the detection state indicates that detection is limited.

[0116] Then, if the detection status indicates that the detection is not restricted, the reference relation is used as the target relation.

[0117] According to embodiments of this application, when any one of the multiple detection states indicates that detection is restricted, the restricted relationship corresponding to the reference relationship can be used as the target relationship. A restricted relationship refers to an association relationship that is temporarily unable to perform normal detection due to limitations in current detection conditions, and is therefore marked as restricted detection. For example, the restricted relationship corresponding to the reference relationship "currently detectable" could be "currently undetectable".

[0118] In the embodiments of this application, when it is necessary to introduce an environment-dependent identifier that references environmental information, the relationship between nodes is considered to assist in identification. Based on this, by introducing environmental information to assist in the judgment, the relationship identification result can be made more consistent with physical reality, avoiding the output of relationships that are not actually valid due to ignoring environmental constraints, thus improving the physical authenticity and reliability of relationship identification.

[0119] In some embodiments, there are multiple target relationships, and each target relationship has a corresponding importance score. The method further includes:

[0120] First, in response to the fact that multiple target relations contain at least one second target relation that conflicts with any first target relation, the importance score of the first target relation matching, the accuracy of relation identification associated with the first target relation, and the target matching score associated with the first target relation are weighted and fused to obtain the first confidence of the first target relation.

[0121] According to embodiments of this application, the type of relationship between two targets can be mutually exclusive or orthogonal. A mutually exclusive relationship refers to a conflict between the two targets. For example, for detection device A and detection target B, the steady-state relationship r1 is "A→B, communication reachable," indicating that A and B have the conditions for communication connection. The dynamic relationship r2 is "A→B, communication unreachable," indicating that A and B do not have the conditions for communication connection. Based on this, it can be seen that there is a conflict between r1 and r2.

[0122] Orthogonal type refers to the coexistence of two target relationships. For example, the steady-state relationship between detection device A and detection target B is "A has the ability to detect targets of type B," and the dynamic relationship between them is "B can currently be detected by A." It can be seen that the two relationships describe the inherent capabilities of the device and its current real-time state, respectively, and semantically, they can both hold true simultaneously. Therefore, the type of the two relationships is orthogonal and can be retained simultaneously. In other words, the relationship between detection device A and detection target B includes both the relationship "A has the ability to detect targets of type B" and the relationship "B can currently be detected by A."

[0123] According to embodiments of this application, the relationship identification accuracy associated with the first target relationship, i.e., the accuracy of the relationship definition rule corresponding to the first target relationship in historical relationship identification tasks, is used to characterize the reliability of the relationship output by the relationship definition rule. The importance score corresponding to the first target relationship refers to the value obtained after normalization of the priority of the target relationship definition rule associated with the first target relationship.

[0124] Then, for any second target relation in at least one second target relation, the importance score of the second target relation matching, the accuracy of relation identification associated with the second target relation, and the target matching score associated with the second target relation are weighted and fused to obtain the second confidence of the second target relation.

[0125] According to embodiments of this application, the relationship identification accuracy associated with the second target relationship is the accuracy of the relationship definition rule corresponding to the second target relationship in historical relationship identification tasks. The importance score corresponding to the second target relationship refers to the value obtained after normalizing the priority of the relationship definition rule associated with the second target relationship. Based on this, by normalizing the priority, the calculation result of the confidence score is avoided from being dominated by an excessively large range of priority values, thereby affecting the accuracy and reliability of the confidence score calculation.

[0126] The confidence level of a target relationship can be obtained by weighting and summing the importance score, target matching score, and relationship recognition accuracy of the target relationship.

[0127] For example, target relationship The confidence level (first confidence level or second confidence level). It can be represented in the following ways:

[0128] ;

[0129] in, , as well as These are the weighting coefficients. For target relationship The corresponding importance score, The target matching score is the target relation r. For target relationship The corresponding relationship recognition accuracy.

[0130] In other embodiments, where the environment dependency identifier associated with the device node and the target node indicates that reference probe environment information needs to be introduced, the confidence level can be further calculated by combining the environment confidence factor related to the reference probe environment information to improve the reliability of the confidence level calculation.

[0131] The importance score, target matching score, and relationship recognition accuracy of the target relationship can be weighted and summed. Then, the weighted sum is multiplied by the environmental confidence factor to obtain the confidence level of the target relationship.

[0132] For example, target relationship confidence level It can be represented in the following ways:

[0133] ;

[0134] in, This refers to the environmental confidence factor. The environmental confidence factor can be based on the target relationship. The environmental confidence factor is determined by factors such as the degree of matching, data completeness, timeliness, and reliability of the environmental data declared in the corresponding relationship definition rules. It reflects the credibility of the environmental data itself used in this judgment. The environmental confidence factor can range from 0 to 1. The more complete, timely, and supportive the environmental data is for the current relationship identification, the closer the environmental confidence factor is to 1; conversely, it decreases accordingly.

[0135] Then, based on the first confidence level and at least one second confidence level, the target relationship is determined from the first target relationship and at least one second target relationship.

[0136] According to an embodiment of this application, for a first confidence level and a second confidence level, if the difference between the first confidence level and the second confidence level is greater than a preset difference, and the first confidence level is greater than the second confidence level, then the first target relationship is taken as the target relationship. Here, the preset difference can be 0.1.

[0137] When the difference between the first confidence level and the second confidence level is less than or equal to a preset difference, the priorities of the relationship definition rules corresponding to the first and second target relationships are compared, and the target relationship corresponding to the rule with the higher priority is determined as the target relationship. Based on this, when the difference in confidence levels is insufficient to distinguish between them, adjudicating based on the priority of the relationship definition rules can avoid misjudgments caused by small numerical fluctuations and enhance the robustness of conflict arbitration.

[0138] In the embodiments of this application, when two target relationships are mutually exclusive, the decision is made based on the confidence level, which can objectively quantify the credibility of the two mutually exclusive relationships, thereby improving the reliability of conflict resolution and reducing the risk of misjudgment of relationships.

[0139] Figure 4 A flowchart illustrating the allocation relationship identification task according to an embodiment of this application is shown.

[0140] Combination Figure 4In some embodiments, the reference relationship has corresponding field groupings, and the method further includes steps S410 and S420:

[0141] In step S410, in response to receiving a relationship identification task for device nodes and target nodes, a target computing node is determined from multiple candidate computing nodes based on the task allocation characteristics of the relationship identification task, and each candidate computing node has a corresponding task allocation characteristic.

[0142] According to embodiments of this application, the task allocation features include location information of the detection device, location information of the detection target, a first node type of the device node, a second node type of the target node, and at least one of the field groups.

[0143] According to an embodiment of this application, a composite sharding key corresponding to a relationship recognition task can be calculated based on at least one task allocation feature, and then a target computing node corresponding to the relationship recognition task can be allocated based on the composite sharding key.

[0144] A composite sharding key can be obtained by hashing the task allocation features and then taking the modulo of the number of partitions. This composite sharding key is used to split multiple relation recognition tasks into data shards.

[0145] For example, the composite sharding key SHARD_KEY can be obtained in the following way:

[0146]

[0147] in, For the rule identifiers corresponding to the device node and the target node; For device nodes Type identifier, For the target node Type identifier; This is the root field name of the field path corresponding to the device node, which is also the field group and is used to indicate the top-level attribute category to which the field path belongs. For example, if the field path corresponding to the device node is capability.maxDetectRange, then the field group corresponding to this device node is capability. This refers to the remainder operation.

[0148] The root field name of the target node's field path, i.e., the field group, is used to indicate the top-level attribute category to which the field path belongs. Optional spatial grid encoding, derived from the geographic coordinates of nodes, is used to aggregate spatially adjacent node pairs into the same partition. The number of partitions can be dynamically configured based on the number of compute nodes in the compute cluster. The number of partitions is usually an integer multiple of the number of compute nodes and is used to control the number of tasks that can be executed in parallel and the load balancing among the compute nodes. This indicates a hash calculation.

[0149] In step S420, the relationship identification task is assigned to the target computing node.

[0150] According to embodiments of this application, each composite fragment key has a corresponding computing node.

[0151] For example, the data shards corresponding to each relationship identification task can be allocated to the corresponding candidate computing nodes for parallel execution.

[0152] In the embodiments of this application, the relationship identification task is assigned to the corresponding target computing node based on the task allocation characteristics of the relationship identification task, which can improve the computational efficiency of large-scale node relationship identification. At the same time, by combining location information, node type and field grouping, matching tasks with the same or similar characteristics are assigned to the same computing node, which can ensure that the field data required to execute the task is already local on the node, realizing computation localization and thus significantly reducing cross-node data transmission.

[0153] Based on the above-described topology graph construction method, embodiments of this application also provide a topology graph construction apparatus. The following will be combined with... Figure 5 The device is described in detail.

[0154] Figure 5 A schematic block diagram of a topology graph construction apparatus according to an embodiment of this application is shown.

[0155] like Figure 5 As shown, the topology graph construction device 500 of this embodiment includes a first determining module 510, a second determining module 520, a third determining module 530, a fourth determining module 540, and a graph construction module 550.

[0156] The first determining module 510 is used to determine at least one reference relationship associated with both the device node and the target node among a plurality of nodes, wherein the reference relationship has at least one corresponding field type. In one embodiment, the first determining module 510 may be used to perform step S210 described above, which will not be repeated here.

[0157] The second determining module 520 is used to determine a first target field from a plurality of first candidate fields of a device node based on any target field type among at least one field type. The first candidate field indicates the detection attribute information of the detection device represented by the device node. In one embodiment, the second determining module 520 may be used to perform step S220 described above, which will not be repeated here.

[0158] The third determining module 530 is used to determine a second target field from multiple second candidate fields of the target node based on the target field type, and to determine the degree of matching between the first target field and the second target field. The second candidate field indicates the attribute information of the probe target represented by the target node. In one embodiment, the third determining module 530 can be used to perform step S230 described above, which will not be repeated here.

[0159] The fourth determining module 540 is used to establish an edge between any two nodes with a target relationship, and to construct a topology graph based on multiple nodes and multiple edges. In one embodiment, the fourth determining module 540 can be used to perform step S240 described above, which will not be repeated here.

[0160] Graph construction module 550 is used to establish connecting edges between any two nodes with a target relationship, so as to construct a topology graph based on multiple nodes and multiple connecting edges. In one embodiment, graph construction module 550 can be used to perform step S250 described above, which will not be repeated here.

[0161] According to an embodiment of this application, the fourth determining module 540 includes a first determining submodule, which is used to determine a target matching pattern from multiple candidate matching patterns based on the target field type; match a first target field and a second target field based on the matching scale indicated by the target matching pattern to obtain a matching score that characterizes the degree of matching; obtain a target matching score between a device node and a target node based on at least one matching score; and take a reference relationship as the target relationship when the target matching score is greater than or equal to a preset threshold.

[0162] According to an embodiment of this application, there are multiple target relationships, and each target relationship has a corresponding importance score. The topology graph construction device 500 further includes a fifth determining module. The fifth determining module is used to respond to the fact that the multiple target relationships include at least one second target relationship that conflicts with any first target relationship, to perform a weighted fusion of the importance score of the first target relationship matching, the relationship identification accuracy associated with the first target relationship, and the target matching score associated with the first target relationship to obtain a first confidence level of the first target relationship; for any second target relationship in the at least one second target relationship, to perform a weighted fusion of the importance score of the second target relationship matching, the relationship identification accuracy associated with the second target relationship, and the target matching score associated with the second target relationship to obtain a second confidence level of the second target relationship; and to determine a target relationship from the first target relationship and the at least one second target relationship based on the first confidence level and the at least one second confidence level.

[0163] According to an embodiment of this application, the reference relationship has an environment-dependent identifier, which is used to indicate whether reference detection environment information needs to be introduced. The fourth determining module further includes a second determining submodule, which is used to fuse the matching scores corresponding to at least one matching degree to obtain a target matching score. When the target matching score is greater than a preset threshold and the environment-dependent identifier indicates that reference detection environment information needs to be introduced, the current detection environment information associated with both the detection device and the detection target is determined. Based on the current detection environment information and the reference detection environment information, a detection state is obtained, which indicates whether the detection device's detection of the detection target is restricted. When the detection state indicates that the detection is not restricted, the reference relationship is used as the target relationship.

[0164] According to an embodiment of this application, the topology graph construction apparatus 500 further includes an allocation module, which is configured to, in response to receiving a relationship identification task for a device node and a target node, determine a target computing node from multiple candidate computing nodes based on the task allocation characteristics of the relationship identification task, each candidate computing node having a corresponding task allocation characteristic; and allocate the relationship identification task to the target computing node; wherein the task allocation characteristics include at least one of the following: location information of the probe device, location information of the probe target, a first node type of the device node, a second node type of the target node, and field grouping.

[0165] According to embodiments of this application, the relationship types of the target relationship include steady-state relationships and dynamic relationships. The field types associated with steady-state relationships include at least one of device type, detection range type, and state type. The field types associated with dynamic relationships include at least one of location type, time type, and state type.

[0166] According to embodiments of this application, any plurality of modules among the first determining module 510, the second determining module 520, the third determining module 530, the fourth determining module 540, and the graph construction module 550 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this application, at least one of the first determining module 510, the second determining module 520, the third determining module 530, the fourth determining module 540, and the graph construction module 550 can be at least partially implemented as hardware circuitry, such as field-programmable gate arrays, programmable logic arrays, systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits, or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in any one of software, hardware, and firmware methods, or in a suitable combination of any of these methods. Alternatively, at least one of the first determining module 510, the second determining module 520, the third determining module 530, the fourth determining module 540, and the graph construction module 550 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0167] Figure 6 A block diagram schematically illustrates an electronic device suitable for implementing a topology graph construction method according to an embodiment of this application.

[0168] like Figure 6 As shown, an electronic device 600 according to an embodiment of this application includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory 602 or a program loaded from a storage portion 608 into a random access memory 603. The processor 601 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a dedicated microprocessor. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for executing different steps of the method flow according to an embodiment of this application.

[0169] Random access memory 603 stores various programs and data required for the operation of electronic device 600. Processor 601, read-only memory 602, and random access memory 603 are interconnected via bus 604. Processor 601 executes various steps of the method flow according to embodiments of this application by executing programs in read-only memory 602 and / or random access memory 603. It should be noted that the programs may also be stored in one or more memories other than read-only memory 602 and random access memory 603. Processor 601 may also execute various steps of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0170] According to embodiments of this application, the electronic device 600 may further include an input / output interface 605, which is also connected to a bus 604. The electronic device 600 may also include one or more of the following components connected to the input / output interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube, liquid crystal display, etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card, such as a local area network card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0171] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0172] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include the read-only memory 602 described above, and / or random access memory 603, and / or one or more memories other than read-only memory 602 and random access memory 603.

[0173] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0174] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0175] In embodiments of this application, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by processor 601, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0176] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0178] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A method for constructing a topology graph, characterized in that, The method includes: For any device node and any target node among multiple nodes, determine at least one reference relationship associated with both the device node and the target node, wherein the reference relationship has at least one corresponding field type; For any target field type among the at least one field type, a first target field is determined from a plurality of first candidate fields of the device node based on the target field type, wherein the first candidate field indicates the detection attribute information of the detection device represented by the device node; Based on the target field type, a second target field is determined from multiple second candidate fields of the target node, and the degree of matching between the first target field and the second target field is determined. The second candidate field indicates the attribute information of the probe target represented by the target node. Based on the reference relationship and at least one of the matching degrees, a target relationship between the device node and the target node is determined; An edge is established between any two nodes that have a target relationship, and a topology graph is constructed based on the plurality of nodes and the plurality of edges.

2. The method according to claim 1, characterized in that, Determining the target relationship between the device node and the target node based on the reference relationship and at least one of the matching degrees includes: Based on the target field type, a target matching pattern is determined from multiple candidate matching patterns; Based on the matching scale indicated by the target matching pattern, the first target field and the second target field are matched to obtain a matching score that characterizes the degree of matching. Based on at least one of the matching scores, a target matching score is obtained between the device node and the target node; If the target matching score is greater than or equal to a preset threshold, the reference relationship is used as the target relationship.

3. The method according to claim 1, characterized in that, The target relationships are multiple, and each target relationship has a corresponding importance score. The method further includes: In response to the fact that multiple target relations include at least one second target relation that conflicts with any first target relation, the importance score of the first target relation matching, the relation identification accuracy associated with the first target relation, and the target matching score associated with the first target relation are weighted and fused to obtain the first confidence of the first target relation; For any second target relation in at least one second target relation, the importance score of the second target relation matching, the accuracy of relation identification associated with the second target relation, and the target matching score associated with the second target relation are weighted and fused to obtain the second confidence of the second target relation; Based on the first confidence level and at least one second confidence level, a target relationship is determined from the first target relationship and at least one second target relationship.

4. The method according to claim 1, characterized in that, The reference relationship has an environment-dependent identifier, which is used to indicate whether reference probe environment information needs to be introduced. Determining the target relationship between the device node and the target node based on the reference relationship and at least one of the matching degrees includes: By merging the matching scores corresponding to at least one of the aforementioned matching degrees, a target matching score is obtained; If the target matching score is greater than a preset threshold and the environment dependency identifier indicates that the reference detection environment information needs to be introduced, determine the current detection environment information that is associated with both the detection device and the detection target; Based on the current detection environment information and the reference detection environment information, a detection state is obtained, which indicates whether the detection device is restricted in detecting the target. When the detection state indicates that detection is not restricted, the reference relationship is taken as the target relationship.

5. The method according to claim 1, characterized in that, The reference relationship has corresponding field groupings, and the method further includes: In response to receiving a relationship identification task for the device node and the target node, the target computing node is determined from multiple candidate computing nodes based on the task allocation characteristics of the relationship identification task, and each candidate computing node has a corresponding task allocation characteristic; The relationship identification task is assigned to the target computing node; The task allocation features include the location information of the detection device, the location information of the detection target, the first node type of the device node, the second node type of the target node, and at least one of the field groups.

6. The method according to claim 1, characterized in that, The target relationship includes steady-state relationships and dynamic relationships. The field types associated with the steady-state relationship include at least one of device type, detection range type, and state type. The field types associated with the dynamic relationship include at least one of location type, time type, and state type.

7. A topology graph construction apparatus, characterized in that, The device includes: The first determining module is used to determine at least one reference relationship associated with both the device node and the target node for any device node and any target node among a plurality of nodes, wherein the reference relationship has at least one corresponding field type; The second determining module is used to determine a first target field from a plurality of first candidate fields of the device node based on the target field type for any target field type among the at least one field type, wherein the first candidate field indicates the detection attribute information of the detection device represented by the device node; The third determining module is used to determine a second target field from a plurality of second candidate fields of the target node based on the target field type, and to determine the degree of matching between the first target field and the second target field, wherein the second candidate field indicates the attribute information of the probe target represented by the target node; The fourth determining module is used to establish an edge between any two nodes that have a target relationship, and to construct a topology graph based on the plurality of nodes and the plurality of edges; The graph construction module is used to establish a connection edge between any two nodes that have the target relationship, so as to construct a topology graph based on multiple nodes and multiple connection edges.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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