Blood relationship information processing method and device, electronic equipment and readable medium

Abstract

The embodiment of the invention discloses a blood relationship information processing method and device, electronic equipment and a readable medium. A specific embodiment of the method comprises the following steps: acquiring blood relationship information to be analyzed; performing blood relationship extraction on the to-be-analyzed blood relationship information to generate a blood relationship information set; according to a fusion mode, blood relationship fusion is carried out on the blood relationship information set to generate a local blood relationship map or a global blood relationship map, and the fusion mode comprises a fragment mode or a global mode, the fragment mode corresponds to the local blood relationship map, and the global mode corresponds to the global blood relationship map; performing map structure optimization on the local blood relationship map or the global blood relationship map to generate an optimized local blood relationship map or an optimized global blood relationship map; and generating a dynamic display page which is used for displaying the optimized local blood relationship map or the optimized global blood relationship map. According to the embodiment, the blood relationship is automatically generated and updated.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, electronic device, and readable medium for processing bloodline information. Background Art

[0002] Bloodline information is a type of information that describes the relationship between data flows. By describing bloodline relationships, functions such as data traceability and scheduling optimization can be effectively realized. However, there is currently a problem that it is difficult to effectively describe cross-system bloodlines, which makes it impossible to automatically generate and update bloodline relationships.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present disclosure propose bloodline information processing methods, devices, electronic devices and readable media to solve one or more of the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for processing bloodline information, the method comprising: obtaining bloodline information to be analyzed, wherein the bloodline information to be analyzed represents the bloodline relationship to be analyzed originating from a heterologous bloodline source; performing bloodline relationship extraction on the above-mentioned bloodline information to be analyzed to generate a bloodline relationship information set, wherein the bloodline relationship information in the above-mentioned bloodline relationship information set consists of triples recording entities and entity relationships; performing bloodline fusion on the above-mentioned bloodline relationship information set according to a fusion mode to generate a local bloodline map or a global bloodline map, wherein the fusion mode comprises: a fragment mode or a global mode, the fragment mode corresponds to the local bloodline map, and the global mode corresponds to the global bloodline map; performing graph structure optimization on the above-mentioned local bloodline map or the above-mentioned global bloodline map to generate an optimized local bloodline map or an optimized global bloodline map; generating a dynamic display page, wherein the above-mentioned dynamic display page is used to display the above-mentioned optimized local bloodline map or the optimized global bloodline map.

[0007] Optionally, the above-mentioned blood relationship extraction of the above-mentioned blood relationship information to be analyzed to generate a blood relationship information set includes: determining the language type corresponding to the above-mentioned blood relationship information to be analyzed; determining the model state of the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being a sleeping state, waking up the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being an inactivated state, activating the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being an activated state, performing blood relationship extraction on the above-mentioned blood relationship information to be analyzed according to the blood relationship extraction model corresponding to the above-mentioned language type to generate a blood relationship information set.

[0008] Optionally, the above-mentioned blood relationship extraction model includes: a blood relationship feature rough extraction model, an entity recognition model and a blood relationship recognition model; and the above-mentioned blood relationship extraction model corresponding to the above-mentioned language type performs blood relationship extraction on the above-mentioned blood relationship information to be analyzed to generate a blood relationship information set, including: determining the analysis rules corresponding to the above-mentioned blood relationship information to be analyzed; according to the above-mentioned analysis rules, performing initial blood relationship extraction on the above-mentioned blood relationship information to be analyzed to obtain an initial blood relationship information set; for each initial blood relationship information in the above-mentioned initial blood relationship information set, executing the following processing steps: using the blood relationship feature rough extraction model, performing blood relationship feature extraction on the above-mentioned initial blood relationship information to generate initial blood relationship features; determining entity pairs according to the above-mentioned initial blood relationship features and the above-mentioned entity recognition model, wherein the entity pair is a group of entities with blood relationship corresponding to the initial blood relationship information; determining the blood relationship corresponding to the above-mentioned entity pair according to the above-mentioned initial blood relationship features and the above-mentioned blood relationship recognition model; constructing the blood relationship information in the above-mentioned blood relationship information set corresponding to the above-mentioned initial blood relationship according to the above-mentioned entity pairs and the above-mentioned blood relationship.

[0009] Optionally, the above-mentioned blood relationship information set is subjected to blood fusion according to the fusion mode to generate a local blood relationship map or a global blood relationship map, including: in response to the above-mentioned fusion mode being a fragment mode, the above-mentioned blood relationship information set is subjected to blood fusion according to the storage process to generate the above-mentioned local blood relationship map; in response to the above-mentioned fusion mode being a global mode, the above-mentioned blood relationship information set is subjected to blood fusion according to the storage process to generate at least one local blood relationship map; at least one local blood relationship map is subjected to graph fusion to obtain the above-mentioned global blood relationship map.

[0010] Optionally, the above-mentioned graph structure optimization of the above-mentioned local blood relationship map or the above-mentioned global blood relationship map to generate an optimized local blood relationship map or an optimized global blood relationship map includes: determining the node out-degree value corresponding to each graph node in the above-mentioned optimized global blood relationship map; sorting the at least one graph node included in the above-mentioned optimized global blood relationship map according to the node out-degree value corresponding to the graph node to obtain a graph node sequence; for each graph node in the above-mentioned graph node sequence, executing the following optimization steps: determining at least one graph node that has a node connection relationship with the above-mentioned graph node as a candidate graph node to obtain a candidate graph node sequence, wherein the candidate graph nodes in the candidate graph node sequence are arranged in descending order according to the corresponding node out-degree values; for each candidate graph node in the above-mentioned candidate graph node sequence, updating the distance value between the candidate graph node and the above-mentioned graph node according to the node out-degree value corresponding to the candidate graph node; and determining the global blood relationship map after the distance value is updated as the above-mentioned optimized global blood relationship map.

[0011] Optionally, the above method also includes: in response to receiving a query request for a dynamic display page, verifying the authority of the client that initiates the query request; in response to passing the authority verification, displaying the above dynamic display page on the above client; in response to monitoring the bloodline query request initiated by the above client, updating the above dynamic display page according to the above bloodline query request; and sending the updated dynamic display page to the above client for display.

[0012] In a second aspect, some embodiments of the present disclosure provide a blood relationship information processing device, the device comprising: an acquisition unit, configured to acquire blood relationship information to be analyzed, wherein the blood relationship information to be analyzed represents the blood relationship to be analyzed originating from a heterogeneous blood relationship source; a blood relationship extraction unit, configured to extract blood relationship from the above-mentioned blood relationship information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the above-mentioned blood relationship information set consists of triples recording entities and entity relationships; a blood relationship fusion unit, configured to extract blood relationship from the above-mentioned blood relationship information according to a fusion mode The collection is subjected to bloodline fusion to generate a local bloodline map or a global bloodline map, wherein the fusion mode includes: fragment mode or global mode, the fragment mode corresponds to the local bloodline map, and the global mode corresponds to the global bloodline map; the map structure optimization unit is configured to optimize the map structure of the above-mentioned local bloodline map or the above-mentioned global bloodline map to generate an optimized local bloodline map or an optimized global bloodline map; the generation unit is configured to generate a dynamic display page, wherein the above-mentioned dynamic display page is used to display the above-mentioned optimized local bloodline map or the optimized global bloodline map.

[0013] Optionally, the above-mentioned blood relationship extraction unit is further configured to: determine the language type corresponding to the above-mentioned blood relationship information to be analyzed; determine the model state of the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being a sleeping state, wake up the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being an inactivated state, activate the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being an activated state, perform blood relationship extraction on the above-mentioned blood relationship information to be analyzed according to the blood relationship extraction model corresponding to the above-mentioned language type to generate a blood relationship information set.

[0014] Optionally, the above-mentioned blood relationship extraction model includes: a blood relationship feature rough extraction model, an entity recognition model and a blood relationship recognition model; and the above-mentioned blood relationship extraction unit is further configured to: determine the parsing rules corresponding to the above-mentioned blood relationship information to be parsed; according to the above-mentioned parsing rules, perform initial blood relationship extraction on the above-mentioned blood relationship information to be parsed to obtain an initial blood relationship information set; for each initial blood relationship information in the above-mentioned initial blood relationship information set, perform the following processing steps: use the blood relationship feature rough extraction model to extract blood relationship features from the above-mentioned initial blood relationship information to generate initial blood relationship features; determine entity pairs based on the above-mentioned initial blood relationship features and the above-mentioned entity recognition model, wherein the entity pair is a group of entities with blood relationship corresponding to the initial blood relationship information; determine the blood relationship corresponding to the above-mentioned entity pair based on the above-mentioned initial blood relationship features and the above-mentioned blood relationship recognition model; and construct the blood relationship information in the above-mentioned blood relationship information set that corresponds to the above-mentioned initial blood relationship based on the above-mentioned entity pairs and the above-mentioned blood relationship.

[0015] Optionally, the above-mentioned bloodline fusion unit is further configured to: in response to the above-mentioned fusion mode being a fragment mode, according to the storage process, perform bloodline fusion on the above-mentioned bloodline relationship information set to generate the above-mentioned local bloodline map; in response to the above-mentioned fusion mode being a global mode, according to the storage process, perform bloodline fusion on the above-mentioned bloodline relationship information set to generate at least one local bloodline map; perform map fusion on at least one local bloodline map to obtain the above-mentioned global bloodline map.

[0016] Optionally, the above-mentioned graph structure optimization unit is further configured to: determine the node out-degree value corresponding to each graph node in the above-mentioned optimized global blood relationship graph; according to the node out-degree value corresponding to the graph node, sort the at least one graph node included in the above-mentioned optimized global blood relationship graph to obtain a graph node sequence; for each graph node in the above-mentioned graph node sequence, perform the following optimization steps: determine at least one graph node that has a node connection relationship with the above-mentioned graph node as a candidate graph node to obtain a candidate graph node sequence, wherein the candidate graph nodes in the candidate graph node sequence are arranged in descending order according to the corresponding node out-degree values; for each candidate graph node in the above-mentioned candidate graph node sequence, update the distance value between the candidate graph node and the above-mentioned graph node according to the node out-degree value corresponding to the candidate graph node; determine the global blood relationship graph after the distance value is updated as the above-mentioned optimized global blood relationship graph.

[0017] Optionally, the above-mentioned bloodline information processing device also includes: in response to receiving a query request for a dynamic display page, verifying the authority of the client that initiates the query request; in response to passing the authority verification, displaying the above-mentioned dynamic display page on the above-mentioned client; in response to monitoring the bloodline query request initiated by the above-mentioned client, updating the above-mentioned dynamic display page according to the above-mentioned bloodline query request; and sending the updated dynamic display page to the above-mentioned client for display.

[0018] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0019] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0020] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the blood relationship information processing method of some embodiments of the present disclosure, a universal blood relationship information processing method is provided, thereby realizing the automatic generation and update of blood relationship. Specifically, the reason why blood relationship cannot be automatically generated and updated is that different business systems have different blood relationship processing logics, so it is difficult to effectively connect the blood relationship processing logics between different business systems, resulting in a large amount of manual processing. Based on this, the blood relationship information processing method of some embodiments of the present disclosure, first, obtains the blood relationship information to be analyzed, wherein the blood relationship information to be analyzed represents the blood relationship to be analyzed originating from heterogeneous blood relationship sources. In this way, the blood relationship information to be analyzed that needs to be analyzed by different business systems is automatically obtained. Secondly, the blood relationship extraction is performed on the above-mentioned blood relationship information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the above-mentioned blood relationship information set is composed of triples recording entities and entity relationships. In practice, different business systems may have different processing logics when processing blood relations, but the resolved blood relations often describe the relationship between entities in the form of triples. Therefore, the present disclosure unifies the blood relation extraction logic corresponding to the blood relation information to be resolved in different business systems, that is, uniformly generates corresponding triples for describing blood relations. Then, the above blood relation information set is subjected to blood relation fusion according to the fusion mode to generate a local blood relation map or a global blood relation map, wherein the fusion mode includes: fragment mode or global mode, the fragment mode corresponds to the local blood relation map, and the global mode corresponds to the global blood relation map. In practice, for complex scenarios, the map structure of the blood relation map is extremely complex. For such situations, if the real-time update method is adopted, it can neither meet the actual business needs nor the data update pressure is extremely high. Therefore, the present disclosure combines the fusion mode and matches the corresponding map update method to alleviate the update pressure and the actual business needs. Further, the map structure of the above local blood relation map or the above global blood relation map is optimized to generate an optimized local blood relation map or an optimized global blood relation map. In practice, the direct display of complex graph relationships may result in multiple graph nodes crossing or stacking, so the graph display effect is improved by optimizing the graph structure. Finally, a dynamic display page is generated, where the dynamic display page is used to display the optimized local blood relationship graph or the optimized global blood relationship graph. In this way, the blood relationship is automatically generated and updated. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0022] Figure 1 is a flow chart of some embodiments of the method for processing bloodline information according to the present disclosure; Figure 2 It is a schematic diagram of the process of obtaining the blood relationship information to be analyzed; Figure 3 It is a schematic diagram of the process of generating a global blood relationship map based on a local blood relationship map; Figure 4 is a schematic diagram of the structure of some embodiments of the bloodline information processing device according to the present disclosure; Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0023] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0024] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0025] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0029] Continue to refer Figure 1 , shows a process 100 of some embodiments of the method for processing bloodline information according to the present disclosure. The method for processing bloodline information comprises the following steps: Step 101, obtaining the blood relationship information to be analyzed.

[0030] In some embodiments, the execution subject (for example, a computing device) of the bloodline information processing method can obtain the bloodline information to be analyzed by wired connection or wireless connection. In practice, different business nodes can send the bloodline information to be analyzed to the above-mentioned execution subject in real time or at a fixed time. The bloodline information to be analyzed represents the bloodline relationship to be analyzed that originates from heterogeneous bloodline sources. In practice, the bloodline information to be analyzed may include metadata or stored process data for the bloodline relationship to be analyzed. Specifically, the metadata and stored process data can be stored in a specific file path to facilitate the execution subject to automatically obtain the bloodline information to be analyzed at a fixed frequency. In addition, when there is a dependency in the bloodline relationship, the metadata or stored process data can be obtained in sequence according to the dependency order to obtain the bloodline information to be analyzed.

[0031] As an example, Figure 2 The diagram shows the process of obtaining the bloodline information to be analyzed, wherein business node A corresponds to bloodline information A to be analyzed, business node B corresponds to bloodline information B to be analyzed, business node C corresponds to bloodline information C to be analyzed, and business node D corresponds to bloodline information D to be analyzed. The computing device can obtain bloodline information A to be analyzed, bloodline information B to be analyzed, bloodline information C to be analyzed, and bloodline information D to be analyzed.

[0032] In addition, there may be abnormal acquisition in practice. In this case, you can set a running log to record the operation log of the acquisition process. The running log provides an online analysis log function, especially for abnormal acquisition, allowing re-acquisition.

[0033] It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0034] It should be noted that the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0035] Step 102, extracting blood relationship from the blood relationship information to be analyzed to generate a blood relationship information set.

[0036] In some embodiments, the above-mentioned execution subject can perform blood relationship extraction on the blood relationship information to be parsed to generate a blood relationship information set. The blood relationship information in the blood relationship information set is composed of triples that record entities and entity relationships. For example, the blood relationship may include: entity A, blood relationship and entity B, thereby representing the existence of a blood relationship between entity A and entity B. Specifically, the blood relationship is characterized according to the actual relationship between entity A and entity B. For example, the blood relationship between entity A and entity B can be a computational logic form of blood relationship, or a data transfer dependency form of blood relationship. In practice, tools such as SQLFlow, SQLparser, etc. can be used to realize the automatic extraction of blood relationships.

[0037] In some optional implementations of some embodiments, the execution subject extracts the blood relationship from the blood relationship information to be analyzed to generate a blood relationship information set, including: The first step is to determine the language type corresponding to the above blood relationship information to be parsed.

[0038] The language type represents the SQL language type corresponding to the bloodline information to be parsed. In practice, the language types include but are not limited to: PG, Oralce, SQLserver. Specifically, different language types correspond to different language characteristics, so the language type corresponding to the bloodline information to be parsed can be determined by, for example, parsing keywords.

[0039] The second step is to determine the model state of the kinship extraction model corresponding to the above language type.

[0040] In practice, the blood relationship extraction model may be a machine learning model for extracting blood relationships. Specifically, the execution subject may determine whether the blood relationship extraction model corresponding to the language type is in operation, thereby obtaining the model state.

[0041] The third step is to wake up the blood relationship extraction model corresponding to the above language type in response to the above model state being in the sleeping state.

[0042] In practice, blood relationship extraction is not performed in real time. When it is not in progress, the blood relationship extraction model is still kept in running state, which will occupy memory resources. Therefore, when it is not in progress, the model state of the blood relationship extraction model can be updated to the sleep state, which is manifested as suspending the corresponding process or hibernating the corresponding process.

[0043] The fourth step is to activate the blood relationship extraction model corresponding to the above language type in response to the above model state being in an inactivated state.

[0044] In practice, when it is in an inactive state, the blood relationship extraction model corresponding to the above language type can be cold started.

[0045] The fifth step is to, in response to the above model state being an activated state, extract the blood relationship of the above blood relationship information to be analyzed according to the blood relationship extraction model corresponding to the above language type to generate a blood relationship information set.

[0046] In practice, for example, a method based on fusion vocabulary features, such as the MV-RFNN model, can be used. Another example is a method based on the shortest dependency path, such as the BRCNN model. Another example is an attention mechanism-based method, such as the Bi-LSTM model, to extract the blood relationship of the above blood relationship information to be analyzed, so as to generate a blood relationship information set.

[0047] Optionally, the blood relationship extraction model includes: a blood feature rough extraction model, an entity recognition model and a blood relationship recognition model. The blood feature rough extraction model can adopt the TextRNN model to perform coarse-grained text feature extraction on the blood information to be parsed. The entity recognition model can adopt the ALBERT model. The blood relationship recognition model can use the ALBERT model. By separating entity recognition and blood relationship recognition, the two recognition tasks are performed in parallel. At the same time, the lightweight ALBERT model architecture has the characteristics of lightweight, which effectively reduces the number of parameters and computational costs of the model.

[0048] In some optional implementations of some embodiments, the execution subject extracts the blood relationship of the blood relationship information to be parsed according to the blood relationship extraction model corresponding to the language type to generate a blood relationship information set, including: The first step is to determine the parsing rules corresponding to the above blood relationship information to be parsed.

[0049] In practice, the parsing rules may be pre-configured parsing rules for different language types, for example, the parsing rules may be constructed by configuring regular expressions or parsing templates.

[0050] The second step is to perform initial blood relationship extraction on the blood relationship information to be analyzed according to the above analysis rules to obtain an initial blood relationship information set.

[0051] In practice, by combining parsing rules, multiple blood relationship information contained in the blood relationship information to be parsed can be extracted in batches.

[0052] Step 3: For each initial blood relationship information in the above initial blood relationship information set, perform the following processing steps: In the first sub-step, a blood relationship feature rough extraction model is used to extract blood relationship features from the initial blood relationship information to generate initial blood relationship features. The second sub-step is to determine entity pairs based on the above initial blood relationship features and the above entity recognition model.

[0053] Among them, an entity pair is a group of entities with blood relationship corresponding to the initial blood relationship information.

[0054] The third sub-step is to determine the blood relationship corresponding to the entity pair according to the initial blood relationship characteristics and the blood relationship recognition model; The fourth sub-step is to construct the blood relationship information in the blood relationship information set corresponding to the initial blood relationship based on the entity pair and the blood relationship.

[0055] Step 103, performing blood relationship fusion on the blood relationship information set according to the fusion mode to generate a local blood relationship map or a global blood relationship map.

[0056] In some embodiments, the above-mentioned execution subject can perform bloodline fusion on the bloodline relationship information set according to the fusion mode to generate a local bloodline map or a global bloodline map. Among them, the fusion mode represents the fusion method for bloodline relationships. Fusion modes include: fragment mode or global mode. The fragment mode corresponds to the local bloodline map. The global mode corresponds to the global bloodline map. The fragment mode represents the construction of the bloodline map for a single stored process. The global mode represents the construction of the bloodline map for multiple stored processes. Specifically, after the fragment mode is executed, the global mode can also be further executed.

[0057] As an example, when the fusion mode is the fragment mode, the execution subject can perform bloodline fusion on the bloodline information involved in a single storage process. In practice, since the bloodline information stores triples constructed with entities and bloodline relationships, the bloodline information can be combined to construct a local bloodline graph with entities as graph nodes and bloodline relationships as edges between graph nodes.

[0058] As another example, when the fusion mode is global, the execution subject can perform bloodline fusion on the bloodline information involved in multiple stored procedures. In practice, since the bloodline information stores triples constructed with entities and bloodline relationships, the bloodline information can be combined to construct a complete bloodline graph with entities as graph nodes and bloodline relationships as edges between graph nodes. In addition, considering that the global mode is equivalent to the repeated execution of the fragment mode for different stored procedures, the local bloodline graphs corresponding to different stored procedures can also be spliced ​​to obtain a global bloodline graph. See Figure 3 The schematic diagram of the process of generating a global bloodline map based on local bloodline maps is shown, wherein the above-mentioned execution subject can splice local bloodline map A, local bloodline map B, and local bloodline map C to obtain a global bloodline map A.

[0059] In some optional implementations of some embodiments, the execution subject performs bloodline fusion on the bloodline relationship information set according to a fusion mode to generate a local bloodline map or a global bloodline map, including: The first step is to perform bloodline fusion on the bloodline relationship information set in response to the above fusion mode being the fragment mode according to the storage process to generate the above local bloodline map.

[0060] In practice, since the fragment mode processes the lineage relationship of a single stored procedure, the triples contained in the lineage information in the lineage information set can be lineage fused in the order of the stored procedures to construct a local lineage graph for a single stored procedure.

[0061] In the second step, in response to the above fusion mode being a global mode, the above blood relationship information set is subjected to blood relationship fusion according to the storage process to generate at least one local blood relationship map.

[0062] In practice, since the global mode processes the blood relationship of multiple stored procedures, it can be understood that when there are multiple blood relationships, there may be multiple corresponding stored procedures. Therefore, a local blood relationship map can be constructed based on the blood relationship corresponding to a single stored procedure.

[0063] The third step is to fuse at least one local blood relationship map to obtain the above-mentioned global blood relationship map.

[0064] In practice, at least one local bloodline map is fused to obtain a global bloodline map. Compared with directly generating a global bloodline map, when constructing a local bloodline map for a single stored procedure, it can be done in parallel with multiple threads, which can effectively improve the efficiency of constructing the bloodline map.

[0065] Step 104, optimizing the graph structure of the local blood relationship graph or the global blood relationship graph to generate an optimized local blood relationship graph or an optimized global blood relationship graph.

[0066] In some embodiments, the execution subject may optimize the graph structure of the local bloodline graph or the global bloodline graph to generate an optimized local bloodline graph or an optimized global bloodline graph. In practice, the execution subject may optimize the spacing and density between graph nodes in the local bloodline graph or the global bloodline graph to obtain an optimized local bloodline graph or an optimized global bloodline graph.

[0067] In some optional implementations of some embodiments, the execution subject optimizes the graph structure of the local blood relationship graph or the global blood relationship graph to generate an optimized local blood relationship graph or an optimized global blood relationship graph, including: The first step is to determine the node out-degree value corresponding to each graph node in the global bloodline graph after the above optimization.

[0068] In the second step, at least one graph node included in the above-mentioned optimized global blood relationship graph is sorted according to the node out-degree value corresponding to the graph node to obtain a graph node sequence.

[0069] Among them, the above-mentioned execution entity can sort the at least one graph node included in the above-mentioned optimized global blood relationship graph in descending order to obtain a graph node sequence.

[0070] In the third step, for each graph node in the above graph node sequence, perform the following optimization steps: In the first sub-step, at least one graph node having a node connection relationship with the above graph node is determined as a candidate graph node, and a candidate graph node sequence is obtained.

[0071] Among them, the candidate graph nodes in the candidate graph node sequence are arranged in descending order according to the corresponding node out-degree values.

[0072] The second sub-step is to update, for each candidate graph node in the candidate graph node sequence, the distance value between the candidate graph node and the graph node according to the node out-degree value corresponding to the candidate graph node.

[0073] In practice, for each candidate graph node in the candidate graph node sequence corresponding to the graph node, the distance value between the graph node and the candidate graph node is the same at the beginning, but when the number of candidate graph nodes is large, the edges between the graph nodes may overlap or be blocked, etc. Therefore, for the candidate graph node corresponding to the larger node out-degree value, the distance value between the corresponding candidate graph node and the above graph node is extended. In addition, the transitional extension of the distance value will also increase the visual reading effect of the graph. Therefore, when updating the distance value, the distance value will be standardized so that the updated distance values ​​are all within the same distance value range.

[0074] The fourth step is to determine the global blood relationship map after the distance value is updated as the above-mentioned optimized global blood relationship map.

[0075] Step 105: Generate a dynamic display page.

[0076] In some embodiments, the execution subject may generate a dynamic display page. The dynamic display page is used to display the optimized local blood relationship map or the optimized global blood relationship map. The dynamic display page may combine CSS, HTML and JS technologies to generate a corresponding interactive web page as a dynamic display page.

[0077] Optionally, the above method further includes: In the first step, in response to receiving a query request for a dynamically displayed page, the client that initiates the query request is authenticated.

[0078] In practice, blood maps may correspond to different data sources, so different clients may correspond to different access permissions. Therefore, it is necessary to perform corresponding permission verification on the client that initiates the query request to determine whether the client has the corresponding query and / or editing permissions for the dynamic display page.

[0079] In the second step, in response to passing the authority verification, the dynamic display page is displayed on the client.

[0080] In practice, the above execution entity can feed back the dynamic display page to the client and render it on the client.

[0081] The third step is to respond to monitoring the blood relationship query request initiated by the client and update the dynamic display page according to the blood relationship query request.

[0082] In practice, when the blood relationship is complex, the dynamic display page will contain complex blood relationships. Therefore, users may initiate, through the client, including but not limited to: status query for a single blood node (graph node), status query for a single blood path, etc. Therefore, it is necessary to adjust the dynamic display page according to the user's actual blood query needs.

[0083] The fourth step is to send the updated dynamic display page to the above-mentioned client for display.

[0084] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the blood relationship information processing method of some embodiments of the present disclosure, a universal blood relationship information processing method is provided, thereby realizing the automatic generation and update of blood relationship. Specifically, the reason why blood relationship cannot be automatically generated and updated is that different business systems have different blood relationship processing logics, so it is difficult to effectively connect the blood relationship processing logics between different business systems, resulting in a large amount of manual processing. Based on this, the blood relationship information processing method of some embodiments of the present disclosure, first, obtains the blood relationship information to be analyzed, wherein the blood relationship information to be analyzed represents the blood relationship to be analyzed originating from heterogeneous blood relationship sources. In this way, the blood relationship information to be analyzed that needs to be analyzed by different business systems is automatically obtained. Secondly, the blood relationship extraction is performed on the above-mentioned blood relationship information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the above-mentioned blood relationship information set is composed of triples recording entities and entity relationships. In practice, different business systems may have different processing logics when processing blood relations, but the resolved blood relations often describe the relationship between entities in the form of triples. Therefore, the present disclosure unifies the blood relation extraction logic corresponding to the blood relation information to be resolved in different business systems, that is, uniformly generates corresponding triples for describing blood relations. Then, the above blood relation information set is subjected to blood relation fusion according to the fusion mode to generate a local blood relation map or a global blood relation map, wherein the fusion mode includes: fragment mode or global mode, the fragment mode corresponds to the local blood relation map, and the global mode corresponds to the global blood relation map. In practice, for complex scenarios, the map structure of the blood relation map is extremely complex. For such situations, if the real-time update method is adopted, it can neither meet the actual business needs nor the data update pressure is extremely high. Therefore, the present disclosure combines the fusion mode and matches the corresponding map update method to alleviate the update pressure and the actual business needs. Further, the map structure of the above local blood relation map or the above global blood relation map is optimized to generate an optimized local blood relation map or an optimized global blood relation map. In practice, the direct display of complex graph relationships may result in multiple graph nodes crossing or stacking, so the graph display effect is improved by optimizing the graph structure. Finally, a dynamic display page is generated, where the dynamic display page is used to display the optimized local blood relationship graph or the optimized global blood relationship graph. In this way, the blood relationship is automatically generated and updated.

[0085] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a bloodline information processing device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the bloodline information processing device can be specifically applied to various electronic devices.

[0086] like Figure 4 As shown, the blood relationship information processing device 400 of some embodiments includes: an acquisition unit 401, a blood relationship extraction unit 402, a blood relationship fusion unit 403, a graph structure optimization unit 404 and a generation unit 405. The acquisition unit 401 is configured to acquire the blood relationship information to be analyzed, wherein the blood relationship information to be analyzed represents the blood relationship to be analyzed originating from a heterogeneous blood relationship source; the blood relationship extraction unit 402 is configured to perform blood relationship extraction on the blood relationship information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the blood relationship information set is composed of triples recording entities and entity relationships; the blood relationship fusion unit 403 is configured to perform blood relationship fusion on the blood relationship information set according to a fusion mode to generate a set. A local bloodline map or a global bloodline map, wherein the fusion mode includes: a fragment mode or a global mode, the fragment mode corresponds to the local bloodline map, and the global mode corresponds to the global bloodline map; a graph structure optimization unit 404 is configured to optimize the graph structure of the above-mentioned local bloodline map or the above-mentioned global bloodline map to generate an optimized local bloodline map or an optimized global bloodline map; a generation unit 405 is configured to generate a dynamic display page, wherein the above-mentioned dynamic display page is used to display the above-mentioned optimized local bloodline map or the optimized global bloodline map.

[0087] In some optional implementations of some embodiments, the above-mentioned blood relationship extraction unit 402 is further configured to: determine the language type corresponding to the above-mentioned blood relationship information to be parsed; determine the model state of the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being a sleeping state, wake up the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being an inactivated state, activate the blood relationship extraction model corresponding to the above-mentioned language type; in response to the above-mentioned model state being an activated state, perform blood relationship extraction on the above-mentioned blood relationship information to be parsed according to the blood relationship extraction model corresponding to the above-mentioned language type to generate a blood relationship information set.

[0088] In some optional implementations of some embodiments, the above-mentioned blood relationship extraction model includes: a blood relationship feature rough extraction model, an entity recognition model and a blood relationship recognition model; and the above-mentioned blood relationship extraction unit 402 is further configured to: determine the parsing rules corresponding to the above-mentioned blood relationship information to be parsed; according to the above-mentioned parsing rules, perform initial blood relationship extraction on the above-mentioned blood relationship information to be parsed to obtain an initial blood relationship information set; for each initial blood relationship information in the above-mentioned initial blood relationship information set, perform the following processing steps: extract blood relationship features from the above-mentioned initial blood relationship information through the blood relationship feature rough extraction model to generate initial blood relationship features; determine entity pairs according to the above-mentioned initial blood relationship features and the above-mentioned entity recognition model, wherein the entity pair is a group of entities with blood relationship corresponding to the initial blood relationship information; determine the blood relationship corresponding to the above-mentioned entity pair according to the above-mentioned initial blood relationship features and the above-mentioned blood relationship recognition model; and construct the blood relationship information in the above-mentioned blood relationship information set corresponding to the above-mentioned initial blood relationship according to the above-mentioned entity pairs and the above-mentioned blood relationship.

[0089] In some optional implementations of some embodiments, the above-mentioned bloodline fusion unit 403 is further configured to: in response to the above-mentioned fusion mode being a fragment mode, according to the storage process, perform bloodline fusion on the above-mentioned bloodline relationship information set to generate the above-mentioned local bloodline map; in response to the above-mentioned fusion mode being a global mode, according to the storage process, perform bloodline fusion on the above-mentioned bloodline relationship information set to generate at least one local bloodline map; perform map fusion on at least one local bloodline map to obtain the above-mentioned global bloodline map.

[0090] In some optional implementations of some embodiments, the above-mentioned graph structure optimization unit 404 is further configured to: determine the node out-degree value corresponding to each graph node in the above-mentioned optimized global bloodline graph; according to the node out-degree value corresponding to the graph node, sort the at least one graph node included in the above-mentioned optimized global bloodline graph to obtain a graph node sequence; for each graph node in the above-mentioned graph node sequence, perform the following optimization steps: determine at least one graph node that has a node connection relationship with the above-mentioned graph node as a candidate graph node to obtain a candidate graph node sequence, wherein the candidate graph nodes in the candidate graph node sequence are arranged in descending order according to the corresponding node out-degree values; for each candidate graph node in the above-mentioned candidate graph node sequence, update the distance value between the candidate graph node and the above-mentioned graph node according to the node out-degree value corresponding to the candidate graph node; determine the global bloodline graph after the distance value is updated as the above-mentioned optimized global bloodline graph.

[0091] In some optional implementations of some embodiments, the above-mentioned bloodline information processing device 400 also includes: in response to receiving a query request for a dynamic display page, verifying the authority of the client that initiates the query request; in response to passing the authority verification, displaying the above-mentioned dynamic display page on the above-mentioned client; in response to monitoring the bloodline query request initiated by the above-mentioned client, updating the above-mentioned dynamic display page according to the above-mentioned bloodline query request; and sending the updated dynamic display page to the above-mentioned client for display.

[0092] It is understandable that the units recorded in the bloodline information processing device 400 and the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the bloodline information processing device 400 and the units contained therein, and will not be described in detail here.

[0093] Reference below Figure 5 , which shows a structural schematic diagram of an electronic device (eg, a computing device) 500 suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0094] like Figure 5 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory 502 or a program loaded from a storage device 508 to a random access memory 503. Various programs and data required for the operation of the electronic device 500 are also stored in the random access memory 503. The processing device 501, the read-only memory 502, and the random access memory 503 are connected to each other via a bus 504. An input / output interface 505 is also connected to the bus 504.

[0095] Typically, the following devices may be connected to the input / output interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0096] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the read-only memory 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0097] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0098] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0099] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the blood information to be analyzed, wherein the blood information to be analyzed represents the blood relationship to be analyzed originating from a heterologous blood source; performs blood relationship extraction on the blood information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the blood relationship information set is composed of triples recording entities and entity relationships; performs blood relationship fusion on the blood relationship information set according to a fusion mode to generate a local blood relationship map or a global blood relationship map, wherein the fusion mode includes: a fragment mode or a global mode, the fragment mode corresponds to the local blood relationship map, and the global mode corresponds to the global blood relationship map; performs graph structure optimization on the local blood relationship map or the global blood relationship map to generate an optimized local blood relationship map or an optimized global blood relationship map; generates a dynamic display page, wherein the dynamic display page is used to display the optimized local blood relationship map or the optimized global blood relationship map.

[0100] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor including an acquisition unit, a blood relationship extraction unit, a blood relationship fusion unit, a graph structure optimization unit and a generation unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the graph structure optimization unit may also be described as "a unit for performing graph structure optimization on the above-mentioned local blood relationship map or the above-mentioned global blood relationship map to generate an optimized local blood relationship map or an optimized global blood relationship map".

[0103] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0104] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for processing blood relationship information, characterized in that: include: Obtaining blood relationship information to be resolved, wherein the blood relationship information to be resolved represents the blood relationship to be resolved originating from a heterologous blood relationship source; Extracting blood relationship from the blood relationship information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the blood relationship information set is composed of triples recording entities and entity relationships; Performing blood relationship fusion on the blood relationship information set according to a fusion mode to generate a local blood relationship map or a global blood relationship map, wherein the fusion mode includes: a fragment mode or a global mode, the fragment mode corresponds to the local blood relationship map, and the global mode corresponds to the global blood relationship map; Optimizing the structure of the local blood relationship map or the global blood relationship map to generate an optimized local blood relationship map or an optimized global blood relationship map; Generate a dynamic display page, wherein the dynamic display page is used to display the optimized local blood relationship map or the optimized global blood relationship map.

2. The method according to claim 1, characterized in that The extracting of blood relationship from the blood relationship information to be analyzed to generate a blood relationship information set includes: Determining the language type corresponding to the blood relationship information to be parsed; Determining a model state of a kinship extraction model corresponding to the language type; In response to the model being in a sleeping state, waking up a blood relationship extraction model corresponding to the language type; In response to the model state being an inactive state, activating a blood relationship extraction model corresponding to the language type; In response to the model state being an activated state, the blood relationship extraction is performed on the blood relationship information to be parsed according to the blood relationship extraction model corresponding to the language type to generate a blood relationship information set.

3. The method according to claim 2, characterized in that The blood relationship extraction model includes: a blood relationship feature rough extraction model, an entity recognition model and a blood relationship recognition model; and The extracting the blood relationship of the blood relationship information to be parsed according to the blood relationship extraction model corresponding to the language type to generate a blood relationship information set includes: Determining a parsing rule corresponding to the blood relationship information to be parsed; According to the analysis rule, initial blood relationship extraction is performed on the blood relationship information to be analyzed to obtain an initial blood relationship information set; For each initial blood relationship information in the initial blood relationship information set, the following processing steps are performed: Extracting bloodline characteristics from the initial bloodline information using a bloodline characteristics rough extraction model to generate initial bloodline characteristics; Determine an entity pair according to the initial blood relationship feature and the entity recognition model, wherein the entity pair is a group of entities with blood relationship corresponding to the initial blood relationship information; Determining the blood relationship corresponding to the entity pair according to the initial blood relationship feature and the blood relationship recognition model; According to the entity pair and the blood relationship, blood relationship information corresponding to the initial blood relationship in the blood relationship information set is constructed.

4. The method according to claim 3, characterized in that The step of fusing the blood relationship information set according to the fusion mode to generate a local blood relationship map or a global blood relationship map includes: In response to the fusion mode being the fragment mode, performing blood relationship fusion on the blood relationship information set according to the storage process to generate the local blood relationship map; In response to the fusion mode being a global mode, performing bloodline fusion on the bloodline relationship information set according to a storage process to generate at least one local bloodline map; At least one local blood relationship map is fused to obtain the global blood relationship map.

5. The method according to claim 4, characterized in that The optimizing the structure of the local blood relationship map or the global blood relationship map to generate an optimized local blood relationship map or an optimized global blood relationship map includes: Determine the node out-degree value corresponding to each graph node in the optimized global kinship graph; According to the node out-degree values ​​corresponding to the graph nodes, at least one graph node included in the optimized global blood relationship graph is sorted to obtain a graph node sequence; For each graph node in the graph node sequence, the following optimization steps are performed: Determine at least one graph node that has a node connection relationship with the graph node as a candidate graph node, and obtain a candidate graph node sequence, wherein the candidate graph nodes in the candidate graph node sequence are arranged in descending order according to the corresponding node out-degree values; For each candidate graph node in the candidate graph node sequence, updating the distance value between the candidate graph node and the graph node according to the node out-degree value corresponding to the candidate graph node; The global blood relationship map after the distance value is updated is determined as the optimized global blood relationship map.

6. The method according to claim 5, characterized in that The method further comprises: In response to receiving a query request for a dynamically displayed page, verifying the authority of the client that initiated the query request; In response to passing the authority verification, displaying the dynamic display page on the client; In response to monitoring a blood relationship query request initiated by the client, updating the dynamic display page according to the blood relationship query request; The updated dynamic display page is sent to the client for display.

7. A blood relationship information processing device, characterized in that: include: An acquisition unit is configured to acquire blood relationship information to be analyzed, wherein the blood relationship information to be analyzed represents the blood relationship to be analyzed originating from a heterologous blood relationship source; A blood relationship extraction unit is configured to extract blood relationship from the blood relationship information to be analyzed to generate a blood relationship information set, wherein the blood relationship information in the blood relationship information set is composed of triples recording entities and entity relationships; A blood relationship fusion unit is configured to perform blood relationship fusion on the blood relationship information set according to a fusion mode to generate a local blood relationship map or a global blood relationship map, wherein the fusion mode includes: a fragment mode or a global mode, the fragment mode corresponds to the local blood relationship map, and the global mode corresponds to the global blood relationship map; A graph structure optimization unit is configured to optimize the graph structure of the local blood relationship graph or the global blood relationship graph to generate an optimized local blood relationship graph or an optimized global blood relationship graph; The generating unit is configured to generate a dynamic display page, wherein the dynamic display page is used to display the optimized local blood relationship map or the optimized global blood relationship map.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable medium, characterized in that A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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