An electronic information collection system based on a wireless communication network

Abstract

This invention relates to the field of information acquisition technology and discloses an electronic information acquisition system based on a wireless communication network. The system includes a task semantic deconstruction module, a dynamic instance evaluation module, a semantic pipeline negotiation module, a knowledge graph construction module, a multi-dimensional verification and repair module, and a data encapsulation and caching module. First, the task description file of the acquisition target is deconstructed to obtain the data semantic description and expected data features. Then, based on this, candidate data source instances are obtained and their health scores are given, adapted to the dynamic network environment. Next, a standardized data acquisition pipeline is negotiated based on the scores, and a knowledge graph is constructed based on the pipeline and expected features to obtain semantic intermediate data. Subsequently, multi-dimensional verification and repair are performed on this data to form qualified intermediate data. Finally, the data is encapsulated according to a preset specification to obtain the target acquisition dataset and cached in a designated location. This invention can improve the efficiency of electronic information acquisition.

Description

Technical Field

[0001] This invention relates to the field of information acquisition technology, and in particular to an electronic information acquisition system based on a wireless communication network. Background Technology

[0002] Existing electronic information acquisition technologies under wireless communication networks lack depth in semantic deconstruction of acquisition tasks, making it impossible to accurately extract data semantic descriptions and expected data features from task description files. Furthermore, they lack targeted constraints when adapting data sources in dynamic network environments, resulting in insufficient effectiveness of data source detection. Additionally, the evaluation dimensions for candidate data sources are singular, and a comprehensive health assessment standard has not been formed, making it difficult to screen out high-quality data sources that are suitable for the acquisition target. This leads to significant adaptation biases in the data source matching stage in the early stages of acquisition, directly affecting the overall progress of subsequent data acquisition.

[0003] Existing electronic information acquisition technologies have significant shortcomings in data processing and encapsulation. The intermediate data constructed during the acquisition process lacks standardized semantic processing, data verification remains at a basic level without a multi-dimensional integrity verification system, and the repair of abnormal data does not accurately complete the data in conjunction with the context. This results in numerous quality problems in the acquired intermediate data. At the same time, data encapsulation does not perform refined mapping, transformation, and nesting reorganization according to the preset architecture specifications, the structure of the acquired dataset is insufficient, and the targeted caching and storage are poor, which greatly reduces the quality of electronic information acquisition and the convenience of subsequent data use. Therefore, how to improve the efficiency of electronic information acquisition has become an urgent problem to be solved. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides an electronic information acquisition system based on a wireless communication network, characterized in that the system includes a task semantic deconstruction module, a dynamic instance evaluation module, a semantic pipeline negotiation module, a knowledge graph construction module, a multi-dimensional verification and repair module, and a data encapsulation and caching module, wherein: The task semantic deconstruction module is used to deconstruct the task description file of the acquisition target to obtain the data semantic description and expected data features of the task description file. The dynamic instance evaluation module is used to perform dynamic semantic adaptation on the dynamic network environment of the collection target based on the data semantic description, obtain candidate data source instances of the collection target, and perform fuzzy comprehensive evaluation on the candidate data source instances to obtain the health score of the candidate data source instances. The semantic pipeline negotiation module is used to perform semantic pipeline negotiation between the collection target and the target data source instance in the candidate data source instance based on the health score, so as to obtain a standardized data collection pipeline between the collection target and the candidate data source instance. The knowledge graph construction module is used to construct a knowledge graph for the candidate data source instance based on the data acquisition pipeline and the expected data features, so as to obtain the semantic intermediate data of the acquisition target. The multi-dimensional verification and repair module is used to perform multi-dimensional integrity verification on the semantic intermediate data and perform context repair on abnormal data in the verification results to obtain qualified intermediate data of the collection target. The data encapsulation and caching module is used to perform structured encapsulation of the qualified intermediate data based on a preset data architecture specification to obtain the target collection dataset of the collection target, and cache the target collection dataset to the specified storage location of the collection target.

[0005] In a preferred embodiment, when the task semantic deconstruction module performs information deconstruction on the task description file of the acquisition target to obtain the data semantic description and expected data features of the task description file, it is specifically used for: Feature extraction is performed on the task description file of the acquisition target to obtain the format identifier of the task description file; Based on the format identifier, semantically guided parsing is performed on the task description file to construct the initial parse tree of the task description file; Knowledge mining is performed on the nodes of the initial parse tree to obtain the data semantic description and expected data features of the description file.

[0006] In a preferred embodiment, when the dynamic instance evaluation module performs dynamic semantic adaptation of the dynamic network environment of the target data collection based on the data semantic description to obtain candidate data source instances of the target data collection, and performs fuzzy comprehensive evaluation on the candidate data source instances to obtain a health score for the candidate data source instances, it is specifically used for: Dynamic constraints are extracted from the semantic description of the data to obtain the dynamic discovery conditions of the acquisition target; Based on the dynamic discovery conditions, semantic-driven detection is performed on the dynamic network environment of the target to obtain candidate data source instances of the target. Cognitive activity assessment is performed on the candidate data source instances to obtain activity index records for the candidate data source instances; Based on the activity index records, a multi-attribute decision is made on the responsiveness of the candidate data source instance to obtain the health score of the candidate data source instance.

[0007] In a preferred embodiment, the health score is calculated using the following formula: ; In the formula, For the candidate data source instance number A health score, For the candidate data source instance number Connection success rate, For the candidate data source instance number The integrity of each response message. For the candidate data source instance number Average response time For the candidate data source instance number One standard deviation of response time It is the minimum average response time among the candidate data source instances.

[0008] In a preferred embodiment, when the task semantic deconstruction module performs semantic pipeline negotiation based on the health score to obtain a standardized data acquisition pipeline between the acquisition target and the candidate data source instances, it is specifically used for: Based on the health score, a multi-objective selection process is performed on the candidate data source instances to obtain the target data source instances of the candidate data source instances; Deep perception is performed on the target data source instance to obtain a list of communication protocols, a list of data format types, and a transmission rate range between the acquisition target and the target data source instance; Tensor fusion is performed on the communication protocol list, the data format type list, and the transmission rate range of the target data source instance to obtain the negotiation parameter set between the acquisition target and the target data source instance; Based on the negotiation parameter set, quantum reinforcement negotiation is performed between the acquisition target and the target data source instance to obtain a standardized data acquisition pipeline between the acquisition target and the candidate data source instance.

[0009] In a preferred embodiment, when the task semantic deconstruction module performs tensor fusion on the communication protocol list, the data format type list, and the transmission rate range of the target data source instance to obtain the negotiation parameter set between the acquisition target and the target data source instance, it is specifically used for: The communication protocol list, data format type list, and transmission rate range of the target data source instance are feature-encoded to obtain the protocol feature vector, format feature vector, and rate feature vector of the acquisition target and the target data source instance; The protocol feature vector, the format feature vector, and the rate feature vector are fused by graph convolution to obtain candidate parameters for the acquisition target and the target data source instance; Based on the historical interaction records of the target data source instance, the confidence weights of the parameter combination nodes of the candidate parameters are applied to obtain the weighted parameters of the candidate parameters. Optimal path mining is performed on the weighted parameters to obtain the node with the optimal combination of the weighted parameters; Based on the optimal parameter combination node, the communication protocol list, data format type list and transmission rate range of the target data source are reconstructed using multimodal parameters to obtain the negotiation parameter set between the acquisition target and the target data source instance.

[0010] In a preferred embodiment, when the knowledge graph construction module performs knowledge graph construction on the candidate data source instances based on the data acquisition pipeline and the expected data features to obtain semantic intermediate data of the acquisition target, it is specifically used for: Based on the standardized data acquisition pipeline, causal awareness reading is performed on the target data source instance to obtain the data fragment sequence of the acquisition target; A feature graph is constructed from the expected data features to obtain an initial knowledge graph framework for the expected data features; The data fragment sequence is aligned with the initial knowledge graph framework using neural symbols to obtain the entity relationship annotation instance set of the acquisition target; Attention fusion is performed on the entity relationship annotation instance set to obtain semantic intermediate data of the collection target.

[0011] In a preferred embodiment, when the knowledge graph construction module performs neural symbol alignment between the data fragment sequence and the initial knowledge graph framework to obtain the entity relation annotation instance set of the acquisition target, it is specifically used for: Entity boundary detection is performed on the data segments of the data segment sequence to obtain a candidate entity set of the target to be acquired; The entity mentions in the candidate entity set are linked with the entity type tags in the initial knowledge graph framework to obtain the entity annotation set of the collection target. Based on the entity annotation set, multi-hop relational reasoning is performed on the contextual features of the data fragment sequence and the relational type description of the initial knowledge graph framework to obtain the relation annotation set of the collection target; The entity annotation set and the relation annotation set are semantically jointly constructed to obtain the entity relation annotation instance set of the collection target.

[0012] In a preferred embodiment, when the multi-dimensional verification and repair module performs multi-dimensional integrity verification on the semantic intermediate data and performs context repair on abnormal data in the verification results to obtain qualified intermediate data of the acquisition target, it is specifically used for: The semantic intermediate data is subjected to consistency verification to obtain preliminary verification anomaly records of the acquisition target; Based on the preliminary verification of the abnormal records, deep anomaly localization is performed on the semantic intermediate data to obtain the set of data fragments to be repaired for the collected target. Based on the neighboring entity attribute values ​​and the association relationships between neighboring entities in the data fragment set to be repaired, neighborhood aggregation is performed on the data fragment set to be repaired to obtain the complete data value of the collection target. Based on the completed data values, knowledge injection is performed on the missing attribute fields of the semantic intermediate data to obtain qualified intermediate data of the collection target.

[0013] In a preferred embodiment, when the data encapsulation and caching module performs structured encapsulation of the qualified intermediate data based on a preset data architecture specification to obtain the target acquisition dataset of the acquisition target, and caches the target acquisition dataset to a designated storage location of the acquisition target, it is specifically used for: Meta-learning extraction is performed on the preset data architecture specifications to obtain the field mapping relationship table, data type conversion rules and data nesting hierarchy definition of the collection target; Based on the field mapping relationship table and data type conversion rules, the qualified intermediate data is mapped and converted to obtain the flattened data record set of the collection target; According to the data nesting hierarchy definition, the data records of the flattened data record set are nested and reorganized to obtain the target collection dataset of the collection target, and the target collection dataset is cached in the specified storage location of the collection target.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This technology achieves refined control over the entire electronic information collection process through multi-module collaboration. First, it performs in-depth information deconstruction on the collection task description file, accurately extracts the semantic description of the data and expected data features, and then completes semantic adaptation of the dynamic network environment based on these features. Through multi-dimensional evaluation, it obtains the health score of candidate data source instances, and completes the negotiation and construction of standardized data collection pipelines based on this. This makes the data source matching and collection link construction in the early stage of collection more targeted, effectively improves the connection and accuracy of the early stage of the collection process, and ensures the orderly progress of the data collection process.

[0015] 2. This technology performs standardized semantic processing and multi-dimensional integrity verification on intermediate data during the acquisition process, and accurately repairs abnormal data in combination with context, greatly improving the quality of intermediate data. Then, according to the preset data architecture specifications, the qualified intermediate data is structured and encapsulated and cached in a targeted manner, so that the final target acquisition dataset has regular structural features. At the same time, it realizes accurate storage of the dataset, improving the convenience of subsequent data access and use. The progressive processing method of each module improves both the overall efficiency of electronic information acquisition and the usability of the acquired data. Attached Figure Description

[0016] Figure 1 A system architecture diagram of an electronic information acquisition system based on a wireless communication network provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0019] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0020] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0021] In practice, the server-side equipment deployed in an electronic information acquisition system based on a wireless communication network may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing electronic information acquisition services to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage these user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices used to provide electronic information acquisition services to various user terminals.

[0022] In terms of implementation, the electronic information collection system based on wireless communication networks and the user terminal are mutually compatible. That is, if the electronic information collection system based on wireless communication networks is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the electronic information collection system based on wireless communication networks is implemented as a website, then the user terminal is implemented as a webpage; or if the electronic information collection system based on wireless communication networks is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0023] like Figure 1 The figure shown is a system architecture diagram of an electronic information acquisition system based on a wireless communication network provided in an embodiment of the present invention.

[0024] The electronic information acquisition system 100 based on a wireless communication network described in this invention can be located on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the implemented functions, the electronic information acquisition system 100 based on a wireless communication network may include a task semantic deconstruction module 101, a dynamic instance evaluation module 102, a semantic pipeline negotiation module 103, a knowledge graph construction module 104, a multi-dimensional verification and repair module 105, and a data encapsulation and caching module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0025] In this embodiment of the invention, in the electronic information acquisition system based on a wireless communication network, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the electronic information acquisition system based on a wireless communication network provided by this embodiment of the invention, the applicability of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the electronic information acquisition system based on a wireless communication network. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0026] The following describes, with reference to specific embodiments, each component of the electronic information acquisition system based on a wireless communication network and its specific workflow: The task semantic deconstruction module 101 is used to deconstruct the task description file of the acquisition target to obtain the data semantic description and expected data features of the task description file. In this embodiment of the invention, when the task semantic deconstruction module performs information deconstruction on the task description file of the acquisition target to obtain the data semantic description and expected data features of the task description file, it is specifically used for: Feature extraction is performed on the task description file of the acquisition target to obtain the format identifier of the task description file; Based on the format identifier, semantically guided parsing is performed on the task description file to construct the initial parse tree of the task description file; Knowledge mining is performed on the nodes of the initial parse tree to obtain the data semantic description and expected data features of the description file.

[0027] A full-content feature extraction operation is performed on the task description file of the target data collection. The character encoding format, content hierarchy and layout format, and data field identification format of the file are scanned and identified segment by segment to extract format feature information with unique identification attributes. The extracted format feature information is integrated and processed according to the preset 16-bit fixed character length format encoding rule. The integrated feature information set is the format identifier of the task description file. The length of the scanned and identified character segment is determined to be no less than 2 characters to ensure that the extracted feature information has effective identification.

[0028] The format identifier of the acquired task description file is precisely matched with the preset parsing rule library. The semantic parsing guidance rule that corresponds exactly to the format identifier is retrieved. According to the rule, the content logical unit of the task description file is used as the basic splitting unit. The overall content of the file is semantically split into layers. Each split content logical unit is used as the basic node of the parse tree. Based on the judgment criterion that there is a direct semantic subordinate relationship between the content logical units, all basic nodes are connected in an orderly manner according to the hierarchical structure of parent nodes and child nodes. The hierarchical node structure after connection is the initial parse tree of the task description file. The number of levels of the parse tree is consistent with the logical level of the file content.

[0029] A full node traversal operation is performed on the initial parse tree of the task description file. The traversal criteria are to extract all information from the root node, intermediate nodes, and leaf nodes of the parse tree without omission. Core semantic information is extracted from the content logic unit corresponding to each node. The extracted content includes three types of information: core data definition, data association requirements, and data collection direction within the logic unit. The semantic information of all extracted nodes is systematically integrated and sorted according to the hierarchical structure of the initial parse tree. The integrated and sorted information set is the data semantic description of the task description file. At the same time, three types of information directly related to the collection target, namely data attribute requirements, data scope definition, and data quality standards, are selected from the integrated semantic information. The selected information is formalized according to the preset feature organization specifications. The processed information set is the expected data features of the task description file.

[0030] The beneficial effects are as follows: This implementation process achieves in-depth information deconstruction of the target task description file through standardized operating steps. The feature extraction stage relies on fixed scanning and encoding rules to form a unique format identifier, ensuring the accuracy and directionality of subsequent semantic parsing. The semantic-guided parsing stage constructs an initial parse tree based on the format identifier and matching exclusive rules, making the structure of the parse tree highly compatible with the file's format and logic. The node knowledge mining stage achieves the completeness of the data semantic description and the relevance of the expected data features through full traversal and targeted filtering. Each stage has set clear operation judgment benchmarks and fixed processing rules, ensuring the consistency and reproducibility of the information deconstruction results, and providing accurate and standardized basic information support for subsequent technical stages such as dynamic semantic adaptation.

[0031] The dynamic instance evaluation module 102 is used to perform dynamic semantic adaptation on the dynamic network environment of the collection target based on the data semantic description, obtain candidate data source instances of the collection target, and perform fuzzy comprehensive evaluation on the candidate data source instances to obtain the health score of the candidate data source instances. In this embodiment of the invention, when the dynamic instance evaluation module performs dynamic semantic adaptation on the dynamic network environment of the target data collection based on the data semantic description to obtain candidate data source instances of the target data collection, and performs fuzzy comprehensive evaluation on the candidate data source instances to obtain a health score for the candidate data source instances, it is specifically used for: Dynamic constraints are extracted from the semantic description of the data to obtain the dynamic discovery conditions of the acquisition target; Based on the dynamic discovery conditions, semantic-driven detection is performed on the dynamic network environment of the target to obtain candidate data source instances of the target. Cognitive activity assessment is performed on the candidate data source instances to obtain activity index records for the candidate data source instances; Based on the activity index records, a multi-attribute decision is made on the responsiveness of the candidate data source instance to obtain the health score of the candidate data source instance.

[0032] The formula for calculating the health score is as follows: ; In the formula, For the candidate data source instance number A health score, For the candidate data source instance number Connection success rate, For the candidate data source instance number The integrity of each response message. For the candidate data source instance number Average response time For the candidate data source instance number One standard deviation of response time It is the minimum average response time among the candidate data source instances.

[0033] A full-scale analysis of the data semantic description is performed to extract four core constraints: data source type, communication protocol adaptation, data output format, and network access range. Each constraint is calibrated against a pre-defined list of industry-standard criteria. The data source type calibration benchmark is directly related to the data requirements of the target data collection. Communication protocol adaptation is limited to pre-defined protocol categories under wireless communication networks. The data output format must meet the parsing compatibility requirements of the target data collection. The network access range is defined as the network address range to which the target data collection belongs. The four core constraints extracted and calibrated are systematically integrated according to a four-dimensional structure of "type-protocol-format-range". The integrated constraint set document is the dynamic discovery condition of the target data collection.

[0034] The constraints of the four-dimensional structure in the dynamic discovery conditions are transformed into executable network probing instructions. A full-domain scan of the dynamic network environment to which the target belongs is carried out at a fixed probing frequency of 1 second / time. The scanned network address range is the Class C network segment to which the target's IP address belongs. Constraint matching verification is performed on each network node identified during the scan. The criterion for passing the verification is that the actual attributes of the network node completely match the calibration requirements of the corresponding constraint in the dynamic discovery conditions. All verified network nodes are standardized and registered in a unified format according to node number, network physical address, and core attribute information. The set of network nodes after registration is the candidate data source instance of the target.

[0035] For each network node in the candidate data source instance, a continuous connection request and data interaction probe is initiated. The duration of the probe is fixed at 60 seconds, and the data packet used to initiate the probe is a standard test packet of fixed 1024 bytes. Three core activity indicators are extracted for each network node during the probe process: connection response status, data transmission status, and node online status. Each indicator is recorded in real time according to the binary judgment standard of "normal-abnormal", with a recording time granularity of 1 second. The time-series records of the three core activity indicators of each network node are associated and integrated by node number. The integrated indicator record document is the activity indicator record of the candidate data source instance.

[0036] A full statistical analysis was conducted on the three core activity indicators recorded in the activity indicator records. The duration of the normal state for each indicator within a 60-second trial period was calculated. The proportion of the duration of the normal state to the total trial period was used as the quantitative basis for each indicator. The quantitative results of the three types of indicators were comprehensively judged according to a preset fixed weight. The judgment result was presented as an integer score from 0 to 100. The score assignment benchmark was that the percentage value of the comprehensive quantitative result directly corresponded to the corresponding integer score. The network node number of each candidate data source instance was bound one by one with the corresponding comprehensive judgment score. The set of scores after binding was the health score of the candidate data source instance. This health score was quantified by a dedicated calculation formula. All parameters in the formula were extracted from the products of the above implementation process or obtained through derivation calculation.

[0037] For candidate data source instance number The connection success rate is extracted from the activity index records of candidate data source instances. 100 consecutive standard connection requests conforming to the wireless communication network transmission specifications are sent to the candidate data source instances. The time interval between the request sending is 1 second. The number of successful connection requests is counted. The ratio of the number of successful requests to the total number of requests is used as the value of this parameter. The statistical process only records the results of valid connection requests. For candidate data source instance number The integrity of each response message is extracted from the activity index record of the candidate data source instance. A full-byte check is performed on the response message returned by the candidate data source instance after each successful connection. The actual number of received message bytes is compared with the 1024-byte standard response message byte count set according to the wireless communication network message transmission specification. The ratio of the actual number of bytes to the standard number of bytes is used as the value of this parameter. For candidate data source instance number The average response time is extracted from the activity index records of the candidate data source instance. It records the time interval from the issuance of each connection request to the receipt of a complete response message by the candidate data source instance in milliseconds. Only valid connection data with successful connection and complete message are counted. The time intervals of 100 consecutive valid connections are summed, and the quotient of the summation result and the number of valid connections is used as the value of this parameter. For candidate data source instance number Each standard deviation of response time, based on The calculations show that the time interval between each valid connection is first calculated. The difference is calculated by squaring all differences and summing them. The sum is then divided by the quotient of the effective connection count, and the square root is taken. The square root result, stored to the millisecond level, is the value of this parameter. The calculation only uses the... The same valid connection data; The minimum average response time among all candidate data source instances is determined by evaluating the cognitive activity of all candidate data source instances. The values ​​were obtained by performing a full comparison, and all of them were... The values ​​are sorted in ascending order, and the first value in the sorted result is selected as the value of the parameter. No candidate data source instance data is missed during the comparison process. For candidate data source instance number A health score, based on , , , , Obtained through fixed arithmetic logic calculations, with and The product is the numerator of the operation, with and The product of the numerator and denominator is used as the denominator of the operation. The quotient of the numerator and denominator is used as the value of the parameter. During the operation, all parameters use the corresponding values ​​of the same candidate data source instance.

[0038] This calculation formula provides a quantitative basis for the dynamic instance evaluation module's multi-attribute decision-making process regarding the responsiveness of candidate data source instances. It transforms three core attributes of candidate data source instances—connection status, message transmission status, and response time characteristics—into quantifiable numerical indicators, enabling accurate determination of the health status of candidate data source instances. As the core basis for the semantic pipeline negotiation module to select target data source instances, it provides a clear numerical reference standard for the selection of target data source instances. Its operation logic is highly compatible with the cognitive activity evaluation results of the dynamic instance evaluation module. The numerator comprehensively quantifies the effective connection and message transmission capabilities of candidate data source instances, while the denominator comprehensively quantifies the response efficiency and response stability of candidate data source instances. Through the operation relationship between the numerator and denominator, it allows... The numerical value directly reflects the overall performance of the candidate data source instance. The higher the value, the better the health status of the candidate data source instance and the more suitable it is for the electronic information collection needs of the collection target. At the same time, the calculation result of the formula directly inherits the output result of the dynamic instance evaluation module, providing the core input basis for the execution of the semantic pipeline negotiation module. This realizes the numerical connection between the two stages of dynamic instance evaluation and semantic pipeline negotiation, allowing the operation of the two modules to form a coherent technical process, ensuring the efficiency and matching accuracy of the data source screening and collection pipeline negotiation stages in the early stage of electronic information collection.

[0039] The beneficial effects are as follows: This implementation process, combined with the accompanying health score calculation formula, achieves accurate semantic adaptation to the dynamic network environment of the target data collection and quantitative evaluation of candidate data source instances by setting clear operational benchmarks, judgment thresholds, and a unified processing format. The four-dimensional structure of the dynamic constraint extraction stage makes the constraint items more systematic. The fixed frequency and address range limitation of the semantic-driven detection stage ensure the comprehensiveness and standardization of the scan. The fixed trial duration and data packet specifications of the cognitive activity evaluation stage make the indicator records objective and comparable. The multi-attribute decision-making stage achieves the accuracy and uniformity of the health score based on the calculation formula. The operation and formula calculation of each stage are clearly reproducible, which can provide accurate and reliable data source screening basis for the subsequent semantic pipeline negotiation stage, effectively improving the efficiency and quality of the data source matching stage in the electronic information collection process.

[0040] The semantic pipeline negotiation module 103 is used to perform semantic pipeline negotiation between the collection target and the target data source instance in the candidate data source instance based on the health score, so as to obtain a standardized data collection pipeline between the collection target and the candidate data source instance. In this embodiment of the invention, when the task semantic deconstruction module performs semantic pipeline negotiation based on the health score to obtain a standardized data acquisition pipeline between the acquisition target and the candidate data source instance, it is specifically used for: Based on the health score, a multi-objective selection process is performed on the candidate data source instances to obtain the target data source instances of the candidate data source instances; Deep perception is performed on the target data source instance to obtain a list of communication protocols, a list of data format types, and a transmission rate range between the acquisition target and the target data source instance; Tensor fusion is performed on the communication protocol list, the data format type list, and the transmission rate range of the target data source instance to obtain the negotiation parameter set between the acquisition target and the target data source instance; Based on the negotiation parameter set, quantum reinforcement negotiation is performed between the acquisition target and the target data source instance to obtain a standardized data acquisition pipeline between the acquisition target and the candidate data source instance.

[0041] When the task semantic deconstruction module performs tensor fusion on the communication protocol list, the data format type list, and the transmission rate range of the target data source instance to obtain the negotiation parameter set between the acquisition target and the target data source instance, it is specifically used for: The communication protocol list, data format type list, and transmission rate range of the target data source instance are feature-encoded to obtain the protocol feature vector, format feature vector, and rate feature vector of the acquisition target and the target data source instance; The protocol feature vector, the format feature vector, and the rate feature vector are fused by graph convolution to obtain candidate parameters for the acquisition target and the target data source instance; Based on the historical interaction records of the target data source instance, the confidence weights of the parameter combination nodes of the candidate parameters are applied to obtain the weighted parameters of the candidate parameters. Optimal path mining is performed on the weighted parameters to obtain the node with the optimal combination of the weighted parameters; Based on the optimal parameter combination node, the communication protocol list, data format type list and transmission rate range of the target data source are reconstructed using multimodal parameters to obtain the negotiation parameter set between the acquisition target and the target data source instance.

[0042] Using a health score of 100 as a baseline, a score of 80 is set as the admission threshold for target data source instances. All candidate data source instances are sorted by their health scores, strictly following a fixed rule from highest to lowest score. Candidate data source instances with scores of 80 or higher are defined as target data source instances. In cases of tied scores, the ranking is supplemented by ascending order of the instances' network physical address codes. In cases of no ties, the ranking is determined directly. The final set of instances that meet the admission requirements is the target data source instance of the candidate data source instances.

[0043] For each target data source instance, a series of deep-aware operations are initiated, including protocol probing, format recognition, and rate testing. Protocol probing sends dedicated probe frames according to the protocol interaction specifications of the wireless communication network, records the names of all communication protocols that the target data source instance can respond to normally, and organizes them into an ordered list according to protocol adaptation priority. Format recognition sends multiple types of data parsing requests, records all data format types that the target data source instance can parse, and organizes them into an ordered list according to format transmission efficiency. Rate testing conducts bidirectional data transmission tests for 60 consecutive seconds, records the actual number of bytes transmitted per second and converts it to a unified rate unit, extracts the rate value range within the test period, and finally forms three ordered lists and rate value ranges, which are the communication protocol list, data format type list, and transmission rate range of the target and the target data source instance.

[0044] Structured feature encoding is performed on the communication protocol list, data format type list, and transmission rate range of the target data source instance. The encoding is carried out according to the preset 256-bit fixed-length wireless communication network feature encoding rules. Each protocol name in the communication protocol list is substituted one by one according to the industry-standard encoding value, and then arranged in order according to the list sorting rules to form the protocol feature vector of the acquisition target and the target data source instance. Each format type in the data format type list is substituted one by one according to the parsing compatibility encoding value, and then arranged in order according to the list sorting rules to form the format feature vector. The upper and lower limits of the transmission rate range are converted into encoded values ​​according to the numerical encoding rules, and then the corresponding encoded bits are supplemented by combining the rate fluctuation characteristics to form the rate feature vector. During the encoding process, all encoded values ​​are completely matched with the feature encoding standard of the wireless communication network.

[0045] The protocol feature vector, format feature vector, and rate feature vector are imported into a preset feature fusion framework. Hierarchical fusion is performed according to a fixed rule that the vector dimensions correspond one-to-one. Correlation verification is performed on the feature coding bits of each dimension. The criterion for passing the verification is that there is no logical conflict among the feature coding bits of the same dimension. The coding bits that pass the verification are directly combined and spliced. The coding bits that fail the verification are corrected according to the preset wireless communication network feature completion rules before being combined and spliced. The fused feature coding set is classified and organized according to the parameter attribute dimensions of communication, format, and rate. The final classified and organized feature coding set is the candidate parameter of the acquisition target and the target data source instance.

[0046] Retrieve all historical interaction records of the target data source instance within the past 90 days. The scope of retrieval of historical interaction records includes all completed electronic information collection interaction data of the instance. Match the corresponding historical interaction records for each parameter combination node of the candidate parameters, count the number of successful executions of the node in the historical interactions, and use the proportion of the number of successful executions to the total number of interactions of the node as the confidence weight value of the node. The weight value is fixed in the range of 0 to 1. Associate the basic feature value of each parameter combination node with the corresponding confidence weight value. The association assignment is performed in a fixed way that matches the basic feature value with the weight value. The set of all parameter combination nodes that have completed the confidence weighting operation is the weighted parameter of the candidate parameter.

[0047] A full path traversal is performed on all parameter combination nodes of the weighted parameters. The traversal is executed according to the logical association rules between nodes. Each traversal path corresponds to a complete set of electronic information acquisition parameter combination schemes. The comprehensive weighted value of each traversal path is calculated. The comprehensive weighted value is the sum of the weighted values ​​of all nodes in the path. All traversal paths are sorted according to the rule of comprehensive weighted value from high to low. The parameter combination node corresponding to the path with the highest comprehensive weighted value is selected. If there are multiple paths with the same highest comprehensive weighted value, they are supplemented and filtered according to the rule of the actual execution time of the nodes from short to long. The parameter combination node finally selected is the optimal parameter combination node of the weighted parameters.

[0048] Based on the optimal parameter combination node, a multimodal parameter reconstruction operation is performed on the communication protocol list, data format type list, and transmission rate range of the target data source instance. The reconstruction precisely filters the three types of lists and rate ranges according to the attribute requirements of the optimal parameter combination node. The criterion for filtering is that the content of the list and range completely matches the attribute requirements of the optimal parameter combination node. The filtered content is then optimized in detail according to the electronic information acquisition and transmission specifications of wireless communication networks. The adjusted communication protocol, data format type, and transmission rate parameter information are systematically integrated according to the preset parameter set organization specifications. The final integrated parameter information set is the negotiation parameter set between the acquisition target and the target data source instance.

[0049] Using the negotiation parameter set as the core negotiation foundation, quantum-enhanced negotiation is conducted between the acquisition target and the target data source instance. The negotiation is executed according to a preset three-round interaction rule. In the first round of negotiation, the acquisition target sends an electronic information acquisition pipeline construction request based on the negotiation parameter set to the target data source instance. In the second round of negotiation, the target data source instance fully confirms the parameters in the request and provides feedback on parameter adjustment. In the final round of negotiation, the acquisition target and the target data source instance complete the final confirmation of all negotiation parameters. During the negotiation process, all interactive information is processed according to the encrypted transmission specifications of wireless communication networks. All confirmed acquisition pipeline parameters are structured and built according to the preset pipeline architecture requirements. The constructed pipeline architecture includes core contents such as communication links, data parsing rules, and transmission rate control requirements. The finally constructed pipeline architecture is the standardized data acquisition pipeline between the acquisition target and the candidate data source instance.

[0050] The beneficial effects are as follows: This implementation process, by setting clear numerical thresholds, fixed operating rules, and unified judgment criteria, achieves refined operation throughout the entire process, from target data source instance selection to standardized data acquisition pipeline construction. The admission threshold in the multi-target optimization stage provides clear judgment criteria for the selection of target data source instances. The series of operations in the deep perception stage ensures the comprehensiveness and accuracy of the acquisition of three types of lists and rate ranges. The fixed rules in the feature encoding and graph convolution fusion stages make feature vectorization and parameter fusion more standardized. The confidence weighting and optimal path mining stages rely on historical interaction records to make parameter selection more reasonable. The precise operation in the multimodal parameter reconstruction and quantum reinforcement negotiation stages makes the negotiated parameter set and acquisition pipeline more suitable for the needs of the acquisition target. The operation of each stage has clear reproducibility. The standardized data acquisition pipeline can provide stable and efficient transmission link support for subsequent electronic information acquisition, effectively improving the data transmission efficiency and adaptability in the electronic information acquisition process.

[0051] The knowledge graph construction module 104 is used to construct a knowledge graph for the candidate data source instance based on the data acquisition pipeline and the expected data features, so as to obtain the semantic intermediate data of the acquisition target. In this embodiment of the invention, when the knowledge graph construction module performs knowledge graph construction on the candidate data source instance based on the data acquisition pipeline and the expected data features to obtain the semantic intermediate data of the acquisition target, it is specifically used for: Based on the standardized data acquisition pipeline, causal awareness reading is performed on the target data source instance to obtain the data fragment sequence of the acquisition target; A feature graph is constructed from the expected data features to obtain an initial knowledge graph framework for the expected data features; The data fragment sequence is aligned with the initial knowledge graph framework using neural symbols to obtain the entity relationship annotation instance set of the acquisition target; Attention fusion is performed on the entity relationship annotation instance set to obtain semantic intermediate data of the collection target.

[0052] When the knowledge graph construction module performs neural symbol alignment between the data fragment sequence and the initial knowledge graph framework to obtain the entity relation annotation instance set of the collected target, it is specifically used for: Entity boundary detection is performed on the data segments of the data segment sequence to obtain a candidate entity set of the target to be acquired; The entity mentions in the candidate entity set are linked with the entity type tags in the initial knowledge graph framework to obtain the entity annotation set of the collection target. Based on the entity annotation set, multi-hop relational reasoning is performed on the contextual features of the data fragment sequence and the relational type description of the initial knowledge graph framework to obtain the relation annotation set of the collection target; The entity annotation set and the relation annotation set are semantically jointly constructed to obtain the entity relation annotation instance set of the collection target.

[0053] Based on the communication protocol, transmission rate and data parsing rules of the standardized data acquisition pipeline, a targeted data reading request is sent to the target data source instance. The returned raw data is segmented according to the data causal relationship attributes. The segmentation is based on preserving the complete causal logic and information units. The segmented data is sorted according to the acquisition time and causal logic. The ordered set of the segmented data is the data fragment sequence of the acquisition target.

[0054] The expected data features are broken down into three core information categories: data attribute requirements, scope definition, and quality standards. Each type of information is assigned a graph node identifier with a 32-bit unique character code. Node connection relationships are established according to fixed hierarchical rules. The identified nodes and connection relationships are built according to the knowledge graph infrastructure. The resulting hierarchical graph structure is the initial knowledge graph framework for the expected data features.

[0055] For each data segment in the data segment sequence, character scanning and semantic recognition are performed. Using the unique semantic feature identifier as the benchmark for entity boundary detection, entity content with independent semantics is identified. Each entity is assigned a unique number and arranged in sequence order. The set of numbered entity content after arrangement is the candidate entity set of the target to be collected.

[0056] Extract the core entity mentions from the candidate entity set, and link them with the entity type labels of the initial knowledge graph framework based on semantic completeness. Label each entity with a corresponding label and retain the association between the number and the label. Integrate the labeled entities in ascending order of number. The integrated set of labeled entities is the entity label set of the collection target.

[0057] Using the labels and numbers of the entity annotation set as inference nodes, the contextual features of the data fragment sequence are extracted. Combined with the relation type description of the initial knowledge graph framework, a three-hop relation multi-hop inference is carried out according to the "entity-association-entity" rule. The corresponding relation types are labeled for the associated entities and the association information is retained. The integrated set of entity association information with relation descriptions is the relation annotation set of the collection target.

[0058] The entity annotation set and the relation annotation set are precisely matched according to the entity number. Consistency verification is performed based on the absence of semantic and logical conflicts. The information that passes the verification is instantiated and the source identifier of the data fragment is added. The instantiated information is integrated according to the sequence of the data fragments. The integrated set of instantiated information is the entity relation annotation instance set of the collection target.

[0059] Based on the core data attribute requirements of the expected data characteristics, attention is focused on the entity relationship annotation instance set and corresponding weights are assigned to the information. The weighted instance information is then fused according to the entity semantic logic and relationship association rules. The fusion is based on preserving the complete entity relationship and core semantics. The fused information set is the semantic intermediate data of the collection target.

[0060] The beneficial effects are as follows: the implementation process achieves refined operation of the entire knowledge graph construction process through clear operational benchmarks, identification specifications and reasoning rules. Each step has specific judgment criteria to ensure reproducibility. Causal perception reading ensures that the data fragment sequence retains complete causal logic. The initial knowledge graph framework provides a standardized reference. The entity and relationship annotation step ensures the accuracy, completeness and rationality of the annotation. Attention fusion focuses on core data needs. The final semantic intermediate data generated is semantically clear and has complete entity relationships, providing high-quality basic data for subsequent multi-dimensional verification and repair steps, effectively improving the accuracy and effectiveness of the electronic information collection data processing step.

[0061] The multi-dimensional verification and repair module 105 is used to perform multi-dimensional integrity verification on the semantic intermediate data and perform context repair on abnormal data in the verification results to obtain qualified intermediate data of the acquisition target. In this embodiment of the invention, when the multi-dimensional verification and repair module performs multi-dimensional integrity verification on the semantic intermediate data and performs context repair on abnormal data in the verification results to obtain qualified intermediate data of the acquisition target, it is specifically used for: The semantic intermediate data is subjected to consistency verification to obtain preliminary verification anomaly records of the acquisition target; Based on the preliminary verification of the abnormal records, deep anomaly localization is performed on the semantic intermediate data to obtain the set of data fragments to be repaired for the collected target. Based on the neighboring entity attribute values ​​and the association relationships between neighboring entities in the data fragment set to be repaired, neighborhood aggregation is performed on the data fragment set to be repaired to obtain the complete data value of the collection target. Based on the completed data values, knowledge injection is performed on the missing attribute fields of the semantic intermediate data to obtain qualified intermediate data of the collection target.

[0062] Full consistency verification is performed on semantic intermediate data across three dimensions: entity attributes, relationship associations, and semantic logic. Each dimension is matched with a pre-defined integrity verification specification to set specific judgment criteria. The verification criterion for the entity attribute dimension is that all the required attribute fields preset by the collection target are included and that the field content has no empty values. The verification criterion for the relationship association dimension is that the relationship between entities completely matches the relationship type description of the initial knowledge graph framework. The verification criterion for the semantic logic dimension is that the data content is consistent and meets the semantic expression requirements of the collection target. Each piece of data in the semantic intermediate data is reviewed one by one according to the dimension. During the review process, the content that does not meet the judgment criteria is registered in a unified format according to the data segment number, the dimension to which the anomaly belongs, and the specific description of the anomaly. The set of anomaly information after registration is the preliminary verification anomaly record of the collection target.

[0063] Based on the data segment numbers corresponding to each abnormal information registered in the preliminary verification anomaly record, the complete data segments with the corresponding numbers are accurately retrieved from the semantic intermediate data. A layer-by-layer source tracing and verification operation is carried out for the dimension to which the anomaly belongs. The judgment criteria for source tracing and verification is to locate the specific attribute field and entity relationship node that caused the anomaly, and to confirm the actual impact range of the anomaly. All complete data segments that have been accurately located by source tracing and verification are sorted in order according to the original data segment numbers. The sorted set of data segments with accurate anomaly location information is the set of data segments to be repaired for the target data collection.

[0064] For each missing field in the data segment to be repaired, the attribute values ​​of the same type of entity are retrieved from the three preceding and three following valid data segments in the semantic intermediate data. At the same time, the association information between the entity to which the missing field belongs in the adjacent valid data segments is extracted. The retrieved adjacent entity attribute values ​​are classified and integrated according to the classification rules of entity attributes. The semantic rationality of the integrated attribute values ​​is screened in combination with the extracted association information. The screening criterion is that the attribute value has no logical conflict with the semantic attributes of the entity to which the missing field belongs. The screened attribute values ​​are standardized and formatted according to the preset completion data value format. The set of standardized attribute values ​​after processing is the completion data value of the target.

[0065] Based on the original data structure and arrangement order of the semantic intermediate data, the completed data values ​​are accurately matched to the corresponding positions of the abnormal missing attribute fields. During the knowledge injection process, other valid content and entity relationship structure in the original data are fully preserved. A second consistency check is performed on all data fragments that have completed the injection of completed data values. The judgment criteria for the second consistency check are completely consistent with the criteria for the first consistency check. All data fragments that pass the second check are systematically integrated again according to the original order of the semantic intermediate data. During the integration process, invalid data content that still cannot meet the consistency check criteria after repair is removed. The final integrated complete data set is the qualified intermediate data of the collection target.

[0066] The beneficial effects are as follows: This implementation process, by setting clear multi-dimensional verification benchmarks, source tracing and location rules, and neighborhood aggregation standards, achieves comprehensive investigation and precise repair of semantic intermediate data anomalies. Each step of the operation has specific judgment criteria and fixed processing specifications, ensuring the reproducibility of the technical solution. Multi-dimensional consistency verification can comprehensively identify anomalies in the data from different levels. Deep anomaly location can accurately pinpoint the specific data fragments and fields where the anomalies are located. Neighborhood aggregation, combined with the attribute values ​​and relationships of adjacent entities, makes the completed data values ​​highly semantically reasonable. The secondary consistency verification after knowledge injection further ensures the integrity and accuracy of the repaired data. The final qualified intermediate data has no anomalies or missing information and has a smooth semantic logic, providing high-quality and highly reliable basic data for the subsequent data encapsulation and caching stages, effectively improving the overall quality and effectiveness of the data processing stage in the electronic information collection process.

[0067] The data encapsulation and caching module 106 is used to perform structured encapsulation of the qualified intermediate data based on a preset data architecture specification to obtain the target collection dataset of the collection target, and cache the target collection dataset to the specified storage location of the collection target.

[0068] In this embodiment of the invention, when the data encapsulation and caching module performs structured encapsulation of the qualified intermediate data based on a preset data architecture specification to obtain the target acquisition dataset of the acquisition target, and caches the target acquisition dataset to the designated storage location of the acquisition target, it is specifically used for: Meta-learning extraction is performed on the preset data architecture specifications to obtain the field mapping relationship table, data type conversion rules and data nesting hierarchy definition of the collection target; Based on the field mapping relationship table and data type conversion rules, the qualified intermediate data is mapped and converted to obtain the flattened data record set of the collection target; According to the data nesting hierarchy definition, the data records of the flattened data record set are nested and reorganized to obtain the target collection dataset of the collection target, and the target collection dataset is cached in the specified storage location of the collection target.

[0069] A full-content meta-learning extraction operation is performed on the pre-defined data architecture specification. This specification includes field naming conventions, data type adaptation conventions, and data hierarchy structure specifications specific to the data collection target. The extraction is based on the business attributes and data usage requirements of the data collection target as the core benchmark, and the specification is broken down into three core contents: field association, type adaptation, and hierarchy structure. The field association content is systematically organized according to the fixed rule of one-to-one correspondence between "source field and target field", clarifying the target field name and field category corresponding to each source field. The organized content set is the field mapping relationship table of the data collection target. For the type adaptation content, according to the general data type standard for electronic information collection in wireless communication networks, the conversion method and data format specification of each field from the original type to the target type are formulated. The resulting specification set is the data type conversion rule of the data collection target. For the hierarchy structure content, according to the data storage and retrieval requirements of the data collection target, a fixed nesting level of up to 5 levels, a unique identifier for each level, and the field inclusion range are set. At the same time, the association and combination rules between levels are clarified. The resulting rule set is the data nesting hierarchy definition of the data collection target.

[0070] Based on the field mapping table and data type conversion rules, a full mapping conversion operation is performed on the qualified intermediate data. First, the qualified intermediate data is decomposed into independent source data units based on preserving complete entity attributes and relationship information. Then, the source fields in each source data unit are matched one by one. The matching criterion is that the source field name is completely consistent with the record in the field mapping table. The successfully matched source fields are accurately mapped to the corresponding target fields according to the rules in the table. The type conversion of the mapped target fields is strictly performed in accordance with the data type conversion rules. The maximum character length of character target fields is fixed at 256 characters, and the number of decimal places is fixed at 2 decimal places to ensure that the converted data fully conforms to the format specifications. Then, all target data units that have completed field mapping and type conversion are sorted in an orderly linear structure without hierarchy. The sorted linear target data unit set is the flat data record set of the collection target.

[0071] Based on the definition of data nesting hierarchy, a structured nesting and reorganization operation is performed on the flat data record set. First, the preset nesting level, level identifiers, field inclusion ranges, and inter-level association rules are retrieved. Based on the level identifiers, the linear target data units in the flat data record set are precisely filtered according to the field inclusion ranges of each level. The filtering criterion is that the fields of the target data unit completely match the field inclusion ranges of the corresponding level. Then, the filtered data units at each level are nested and combined according to the inter-level association rules. The combination criterion is that the lower-level data unit is a child node of the upper-level data unit and the overall nesting level does not exceed 5 levels. The structured data set formed after nesting and combining conforms to the preset data architecture specifications, which is the target data set of the collection target. Then, according to the preset storage address, storage format, and storage permission requirements of the collection target, the target data set is transmitted to the designated storage location along the secure transmission specifications of the wireless communication network. After the transmission is completed, a data reading verification operation is performed. The verification criterion is that the stored dataset can be retrieved normally and the data content is without missing or tampering. Once the verification is completed, the target data set is effectively cached in the designated storage location.

[0072] The beneficial effects are as follows: This implementation process, by setting clear extraction benchmarks, mapping rules, and nesting requirements, achieves refined and structured encapsulation and precise caching of qualified intermediate data. The meta-learning extraction stage provides standardized fields, types, and hierarchical basis for subsequent encapsulation operations. The mapping and transformation stage ensures that the data fully conforms to the preset architectural specifications. Flattening simplifies subsequent nesting and reorganization operations. Nesting and reorganization, based on a fixed number of layers and rules, achieves a structured hierarchical design of the data, accurately matching the storage and retrieval needs of the collection target. The secure transmission and read verification in the caching stage ensures the complete storage and effective retrieval of the target collection dataset. Each stage sets specific judgment benchmarks and operating specifications, possessing clear reproducibility. The final generated target collection dataset has a regular structure and uniform format, and achieves stable caching in designated locations, significantly improving the convenience of subsequent data retrieval, use, and management. It provides a reliable guarantee for the final data delivery of the entire electronic information collection process, effectively improving the overall efficiency and quality of the data encapsulation and caching stage.

[0073] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0074] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An electronic information acquisition system based on a wireless communication network, characterized in that, The system includes a task semantic deconstruction module, a dynamic instance evaluation module, a semantic pipeline negotiation module, a knowledge graph construction module, a multi-dimensional verification and repair module, and a data encapsulation and caching module, wherein: The task semantic deconstruction module is used to deconstruct the task description file of the acquisition target to obtain the data semantic description and expected data features of the task description file. The dynamic instance evaluation module is used to perform dynamic semantic adaptation on the dynamic network environment of the collection target based on the data semantic description, obtain candidate data source instances of the collection target, and perform fuzzy comprehensive evaluation on the candidate data source instances to obtain the health score of the candidate data source instances. The semantic pipeline negotiation module is used to perform semantic pipeline negotiation between the collection target and the target data source instance in the candidate data source instance based on the health score, so as to obtain a standardized data collection pipeline between the collection target and the candidate data source instance. The knowledge graph construction module is used to construct a knowledge graph for the candidate data source instance based on the data acquisition pipeline and the expected data features, so as to obtain the semantic intermediate data of the acquisition target. The multi-dimensional verification and repair module is used to perform multi-dimensional integrity verification on the semantic intermediate data and perform context repair on abnormal data in the verification results to obtain qualified intermediate data of the collection target. The data encapsulation and caching module is used to perform structured encapsulation of the qualified intermediate data based on a preset data architecture specification to obtain the target collection dataset of the collection target, and cache the target collection dataset to the specified storage location of the collection target.

2. The electronic information acquisition system based on a wireless communication network as described in claim 1, characterized in that, When the task semantic deconstruction module performs information deconstruction on the task description file of the acquisition target to obtain the data semantic description and expected data features of the task description file, it is specifically used for: Feature extraction is performed on the task description file of the acquisition target to obtain the format identifier of the task description file; Based on the format identifier, semantically guided parsing is performed on the task description file to construct the initial parse tree of the task description file; Knowledge mining is performed on the nodes of the initial parse tree to obtain the data semantic description and expected data features of the description file.

3. The electronic information acquisition system based on a wireless communication network as described in claim 1, characterized in that, The dynamic instance evaluation module, when performing dynamic semantic adaptation of the dynamic network environment of the target data collection based on the data semantic description to obtain candidate data source instances of the target data collection, and performing fuzzy comprehensive evaluation on the candidate data source instances to obtain a health score for the candidate data source instances, is specifically used for: Dynamic constraints are extracted from the semantic description of the data to obtain the dynamic discovery conditions of the acquisition target; Based on the dynamic discovery conditions, semantic-driven detection is performed on the dynamic network environment of the target to obtain candidate data source instances of the target. Cognitive activity assessment is performed on the candidate data source instances to obtain activity index records for the candidate data source instances; Based on the activity index records, a multi-attribute decision is made on the responsiveness of the candidate data source instance to obtain the health score of the candidate data source instance.

4. The electronic information acquisition system based on a wireless communication network as described in claim 3, characterized in that, The formula for calculating the health score is as follows: ； In the formula, For the candidate data source instance number A health score, For the candidate data source instance number Connection success rate, For the candidate data source instance number The integrity of each response message. For the candidate data source instance number Average response time For the candidate data source instance number One standard deviation of response time It is the minimum average response time among the candidate data source instances.

5. The electronic information acquisition system based on a wireless communication network as described in claim 1, characterized in that, When the task semantic deconstruction module performs semantic pipeline negotiation based on the health score to obtain a standardized data acquisition pipeline between the acquisition target and the candidate data source instance, it is specifically used for: Based on the health score, a multi-objective selection process is performed on the candidate data source instances to obtain the target data source instances of the candidate data source instances; Deep perception is performed on the target data source instance to obtain a list of communication protocols, a list of data format types, and a transmission rate range between the acquisition target and the target data source instance; Tensor fusion is performed on the communication protocol list, the data format type list, and the transmission rate range of the target data source instance to obtain the negotiation parameter set between the acquisition target and the target data source instance; Based on the negotiation parameter set, quantum reinforcement negotiation is performed between the acquisition target and the target data source instance to obtain a standardized data acquisition pipeline between the acquisition target and the candidate data source instance.

6. The electronic information acquisition system based on a wireless communication network as described in claim 5, characterized in that, When the task semantic deconstruction module performs tensor fusion on the communication protocol list, the data format type list, and the transmission rate range of the target data source instance to obtain the negotiation parameter set between the acquisition target and the target data source instance, it is specifically used for: The communication protocol list, data format type list, and transmission rate range of the target data source instance are feature-encoded to obtain the protocol feature vector, format feature vector, and rate feature vector of the acquisition target and the target data source instance; The protocol feature vector, the format feature vector, and the rate feature vector are fused by graph convolution to obtain candidate parameters for the acquisition target and the target data source instance; Based on the historical interaction records of the target data source instance, the confidence weights of the parameter combination nodes of the candidate parameters are applied to obtain the weighted parameters of the candidate parameters. Optimal path mining is performed on the weighted parameters to obtain the node with the optimal combination of the weighted parameters; Based on the optimal parameter combination node, the communication protocol list, data format type list and transmission rate range of the target data source are reconstructed using multimodal parameters to obtain the negotiation parameter set between the acquisition target and the target data source instance.

7. The electronic information acquisition system based on a wireless communication network as described in claim 1, characterized in that, When the knowledge graph construction module performs knowledge graph construction on the candidate data source instances based on the data acquisition pipeline and the expected data features to obtain the semantic intermediate data of the acquisition target, it is specifically used for: Based on the standardized data acquisition pipeline, causal awareness reading is performed on the target data source instance to obtain the data fragment sequence of the acquisition target; A feature graph is constructed from the expected data features to obtain an initial knowledge graph framework for the expected data features; The data fragment sequence is aligned with the initial knowledge graph framework using neural symbols to obtain the entity relationship annotation instance set of the acquisition target; Attention fusion is performed on the entity relationship annotation instance set to obtain semantic intermediate data of the collection target.

8. The electronic information acquisition system based on a wireless communication network as described in claim 7, characterized in that, When the knowledge graph construction module performs neural symbol alignment between the data fragment sequence and the initial knowledge graph framework to obtain the entity relation annotation instance set of the acquisition target, it is specifically used for: Entity boundary detection is performed on the data segments of the data segment sequence to obtain a candidate entity set of the target to be acquired; The entity mentions in the candidate entity set are linked with the entity type tags in the initial knowledge graph framework to obtain the entity annotation set of the collection target. Based on the entity annotation set, multi-hop relational reasoning is performed on the contextual features of the data fragment sequence and the relational type description of the initial knowledge graph framework to obtain the relation annotation set of the collection target; The entity annotation set and the relation annotation set are semantically jointly constructed to obtain the entity relation annotation instance set of the collection target.

9. The electronic information acquisition system based on a wireless communication network as described in claim 1, characterized in that, When the multi-dimensional verification and repair module performs multi-dimensional integrity verification on the semantic intermediate data and performs context repair on abnormal data in the verification results to obtain qualified intermediate data of the acquisition target, it is specifically used for: The semantic intermediate data is subjected to consistency verification to obtain preliminary verification anomaly records of the acquisition target; Based on the preliminary verification of the abnormal records, deep anomaly localization is performed on the semantic intermediate data to obtain the set of data fragments to be repaired for the collected target. Based on the neighboring entity attribute values ​​and the association relationships between neighboring entities in the data fragment set to be repaired, neighborhood aggregation is performed on the data fragment set to be repaired to obtain the complete data value of the collection target. Based on the completed data values, knowledge injection is performed on the missing attribute fields of the semantic intermediate data to obtain qualified intermediate data of the collection target.

10. The electronic information acquisition system based on a wireless communication network as described in claim 1, characterized in that, When the data encapsulation and caching module executes a structured encapsulation of the qualified intermediate data based on a preset data architecture specification to obtain the target acquisition dataset of the acquisition target, and caches the target acquisition dataset to the designated storage location of the acquisition target, it is specifically used for: Meta-learning extraction is performed on the preset data architecture specifications to obtain the field mapping relationship table, data type conversion rules and data nesting hierarchy definition of the collection target; Based on the field mapping relationship table and data type conversion rules, the qualified intermediate data is mapped and converted to obtain the flattened data record set of the collection target; According to the data nesting hierarchy definition, the data records of the flattened data record set are nested and reorganized to obtain the target collection dataset of the collection target, and the target collection dataset is cached in the specified storage location of the collection target.

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