A big data-based investment promotion clue analysis system

Abstract

This invention relates to the field of internet search services and discloses a big data-based investment lead analysis system, comprising: a query parsing unit that calls an event sequence template defining meta-event types and standard relative time window constraint intervals; a distributed retrieval and aggregation unit that retrieves and aggregates meta-event types to entity objects; a temporal logic verification unit that statistically analyzes the global document frequency of meta-events and generates a scaling factor through reverse mapping, dynamically corrects the time window, and determines only entity objects that pass the corrected dynamic verification interval as qualified; and an object sorting unit that generates a sorting score based on the template matching completeness. This invention constructs a reverse coupling mechanism between density and window to adaptively compress temporal logic boundaries in high background noise environments, maintaining the retrieval signal-to-noise ratio and logical confidence.

Description

Technical Field

[0001] This invention relates to a big data-based investment lead analysis system, belonging to the field of internet search service technology. Background Technology

[0002] Currently, internet search engines and big data analysis systems primarily rely on inverted indexes and algorithms based on word frequency statistics or semantic vector similarity to process user queries. These retrieval methods are based on the core assumption that the complete information meeting user needs exists in a single or a few highly relevant documents. Existing technologies focus on optimizing the matching accuracy between a single document and query keywords, improving precision and recall through semantic expansion or contextual understanding. This processing model performs exceptionally well for clear, single-fact queries, becoming the standard paradigm for internet information retrieval. However, in scenarios involving the analysis of complex behavioral intentions of specific entities, target information features are fragmented and temporally discrete. Key clues representing the entity's true intentions are not concentrated in a single document but are scattered across independent data objects published at different times, from diverse sources, and with vastly different semantic spans. When analyzing a company's implicit expansion intentions, relevant evidence includes the release of technical personnel recruitment information at a specific time and the release of a factory environmental impact assessment announcement several months later. While the semantics of these independent data objects may not be directly related, their temporal sequence and logic form a tight causal chain. Existing single-document relevance retrieval technologies treat these data as isolated events, making it difficult to identify logical loops formed over specific time intervals. This leads to the underestimation of entities with high-value combined features due to insufficient weight of single-point information.

[0003] To address the challenge of cross-document information association, existing technologies utilize statistical models for multi-source data fusion. For instance, Chinese invention patent CN114676226A discloses a method, system, device, and medium for discovering investment leads by fusing patent data. This method extracts key patent features and park investment demand features, and uses a Logistic regression algorithm to establish a fusion model to calculate matching probabilities. However, such solutions focus on static attribute feature classification and probability prediction, neglecting the dynamic evolution logic of business behavior over time. The authenticity of investment leads depends on the specific order and time interval of events. Simple attribute regression models struggle to eliminate false leads with inverted time sequences or discrete logic. Furthermore, models based on fixed parameters cannot perceive real-time changes in the network information environment. The lack of a dynamic tightening and adaptive mechanism for judgment criteria during periods of industry hotspots leads to a large number of false positive entries with overlapping features but lacking execution logic. Simply lowering the keyword matching threshold to cover more clues introduces a large amount of noisy data containing keywords but lacking contextual logical connections. During periods of industry hotspots or in environments with high information density, the signal-to-noise ratio imbalance is particularly severe. Conventional retrieval systems lack a strong constraint filtering mechanism based on the order of events and time windows, making it impossible to distinguish between accidental semantic overlap and necessary logical connections. This results in a large number of false positive entries in the search results, increasing the cost of manual identification. The inherent differences in the flow speed of different administrative regions or industries make it difficult for a uniform static time window to adapt to diverse execution environments, exacerbating the risk of missed or false positives.

[0004] Therefore, how to construct a system that can accurately aggregate and adaptively sort target entities across single document boundaries, based on the temporal combination features and logical topological relationships of discrete data fragments, in complex noisy environments, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A big data-based investment promotion lead analysis system, comprising:

[0006] The query parsing unit is used to respond to retrieval commands and call preset event sequence templates. The event sequence templates limit at least two logically related meta-event types and define the standard relative time window constraint interval that the meta-event types must satisfy.

[0007] The distributed retrieval and aggregation unit is used to perform concurrent retrieval in the inverted index for the meta-event type to obtain a set of candidate documents. The candidate documents are merged into the corresponding unique entity objects using the subject identifier in the candidate documents. The entity objects are associated with a time series set containing the event type and the occurrence timestamp.

[0008] The temporal logic verification unit is used to perform dynamic temporal constraint verification for each entity object. It counts the global document frequency of the meta-event type corresponding to the time series coverage of the entity object in the inverted index, calculates the scaling factor corresponding to the global document frequency based on the preset reverse mapping relationship, and uses the scaling factor to multiply and correct the upper and lower limits of the standard relative time window constraint interval to generate a dynamic verification interval. It determines whether there is a meta-event type combination in the time series set that satisfies the event sequence template definition, and calculates the difference between the occurrence timestamps of each meta-event in the combination. When the difference falls within the dynamic verification interval, the entity object is determined to pass the verification.

[0009] The object sorting unit is used to retain only the entity objects that pass the validation, and generate a sorting score based on the completeness of the matching of the entity objects with the event sequence template, and output a list of entity objects sorted by the sorting score.

[0010] Preferably, the object sorting unit includes a temporal residual weighting module. This module is used to obtain a preset ideal time interval value from the event sequence template, calculate the absolute deviation between the actual occurrence time interval of the meta-event in the entity object and the ideal time interval value, and substitute the absolute deviation into the following formula to calculate the attenuation coefficient. : ,in, The preset sensitivity constant, The actual time interval. As the ideal time interval value, the temporal residual weighting module uses the attenuation coefficient to weight the basic score of the entity object.

[0011] Preferably, the system further includes a source heterogeneity verification unit. The source heterogeneity verification unit is used to parse the network address attribute of the source document of each meta-event in the time series set associated with the entity object and map it to a preset source category label; count the number of types of source category labels corresponding to all meta-events hit by the entity object, and determine whether the number of types is greater than or equal to a preset minimum heterogeneity threshold; when the number of types meets the minimum heterogeneity threshold, the entity object is allowed to enter the object sorting unit; otherwise, a blocking signal is generated to remove the entity object.

[0012] Preferably, the event sequence template invoked by the query parsing unit also includes at least one semantic anchor slot definition, and the time sequence logic verification unit is also used to perform slot consistency verification operations: for the first-order event and the second-order event in the time sequence set associated with the entity object, extract the first attribute value set and the second attribute value set from the original documents corresponding to the first-order event and the second-order event respectively according to the semantic anchor slot definition; determine whether there is an intersection between the first attribute value set and the second attribute value set; determine that the first-order event and the second-order event satisfy slot consistency only when there is an intersection or either attribute value set is an empty set.

[0013] Preferably, the event sequence template further defines the suppression event type and the associated suppression time window. The object sorting unit is also used to perform mask suppression operation, detect whether there is a third-dimensional event of the suppression event type in the time sequence set of the entity object; if so, calculate the relative time difference between the occurrence timestamp of the third-dimensional event and the occurrence timestamp of the hit meta-event in the entity object; determine whether the relative time difference falls within the suppression time window; if so, generate a forced blocking mask and set the sorting score of the entity object to the preset minimum threshold.

[0014] Preferably, the timing logic verification unit further includes an environmental baseline calibration module. The environmental baseline calibration module is used to extract the environmental attribute identifier of the entity object, call the corresponding historical average time interval value from the preset baseline parameter library based on the environmental attribute identifier, calculate the ratio of the historical average time interval value to the preset standard reference time interval value to generate an environmental calibration coefficient, and use the environmental calibration coefficient to pre-correct the boundary value of the standard relative time window constraint interval defined in the event sequence template to generate a dedicated baseline interval for the entity object, and use the dedicated baseline interval as the base interval for subsequent application scaling coefficients.

[0015] Preferably, when the timing logic verification unit extracts the first attribute value set, if the original document corresponding to the first meta-event does not directly contain the content pointed to by the semantic anchor slot definition, it parses the platform attribute information of the published original document and maps the platform attribute information to the corresponding attribute value and adds it to the first attribute value set.

[0016] Preferably, when performing concurrent retrieval, the distributed retrieval and aggregation unit is also used to preprocess the documents in the inverted index, extract the unified social credit code or the normalized subject name in the document as the subject identifier; if there is no explicit subject identifier in the document, the candidate entity name in the document is extracted using a pre-trained named entity recognition model, and the candidate entity name is mapped to the existing entity object based on the preset coreference resolution rules.

[0017] Preferably, the reverse mapping relationship is configured as follows: when the global document frequency is lower than the preset base frequency threshold, the scaling factor is a value of one or greater than one, so as to maintain or relax the standard relative time window constraint range; when the global document frequency is higher than the base frequency threshold, the scaling factor is a value of less than one, and the value of the scaling factor is negatively correlated with the excess of the global document frequency, so as to compress the standard relative time window constraint range.

[0018] Preferably, the system also includes an index maintenance unit, which is used to monitor the data change flow in the inverted index database in real time; when a new document is detected, the system extracts the meta-event type, occurrence timestamp and subject identifier of the new document, and increments the new document to the time series set of the corresponding entity object; if the new document causes a change in the time series set of the entity object, the system triggers the timing logic verification unit to re-execute the dynamic timing constraint verification for the changed entity object.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. In the analysis of investment leads using big data, a reverse coupling mechanism is established between relative time window constraints and the global document frequency of meta-events. This addresses the signal-to-noise ratio imbalance problem of static retrieval rules in non-stationary data streams. During the retrieval process, the distribution density of the current time slice of meta-events is statistically analyzed in real time, and the allowable width of the time window is dynamically adjusted. When the background noise increases due to the explosion of data across the entire network, the effective time span threshold is automatically compressed using the principle of probability sparsity to increase the concurrency threshold of event combinations. The adaptive passband adjustment logic does not rely on high-computing-power semantic models. Physical-level temporal convergence is used to eliminate logical false positives caused by high-frequency occasional events randomly falling into the wide window, ensuring that the retrieval system maintains a constant confidence level under different data density environments.

[0021] 2. This invention avoids the limitation of traditional Boolean retrieval, which can only determine whether a match is valid. It introduces a continuous evaluation dimension based on temporal residuals, calculates the absolute physical deviation between the actual occurrence interval of the meta-event and the preset ideal interval of the template, and uses a bell-shaped distribution feature mapping function to convert the deviation into a nonlinear sorting decay coefficient. The processing directly decodes the objective physical quantity of time distance into the urgency weight of the retrieval result. A smooth scoring penalty is applied to loosely matched objects at the edge of the time window, so that the system can automatically stratify in the massive number of hit results and give priority to outputting entities with the most compact temporal structure and the most agile logical response, realizing a leap from simple content relevance matching to process quality evaluation.

[0022] 3. By utilizing semantic slot attribute consistency verification and source category topological heterogeneity verification, a closed-loop anti-counterfeiting mechanism is constructed for discrete data aggregation logic. The semantic slot mechanism extracts the co-occurrence features of document place names or product names, blocking crosstalk between data belonging to the same entity but different logical chains in multi-task concurrent scenarios. Source heterogeneity verification requires that the hit meta-event sequence covers different preset source categories, eliminating forged combinations generated by self-verification from a single source. Both mechanisms perform hard constraint filtering on the candidate set from the orthogonal dimensions of content consistency and source independence in the data processing flow, ensuring that the final aggregated output entity object has complete logical self-consistency and is supported by a multi-party mutual evidence chain. Attached Figure Description

[0023] Figure 1This is a diagram illustrating the data flow logic and functional architecture of the investment lead analysis system of this invention.

[0024] Figure 2 This is a graph showing the correlation between the entity normalization similarity threshold and retrieval performance in this invention.

[0025] Figure 3 This is a diagram illustrating the collaborative composition of multi-dimensional technical elements in the high-confidence clue analysis of this invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] This invention provides a big data-based business lead analysis system. The system operates in a server environment including a high-performance computing cluster and a large-capacity storage array. It connects a query parsing unit, a distributed retrieval and aggregation unit, a time-series logic verification unit, an object sorting unit, and an index maintenance unit via a high-speed data bus. The query parsing unit acts as the command entry point, responding to retrieval commands from user terminals containing specific business intent keywords. Based on these keywords, it retrieves matching event sequence templates from a pre-set template database. Each event sequence template defines an ordered tuple containing at least two logically related meta-event types, and specifies the necessary conditions for each adjacent pair of meta-event types. The standard relative time window constraint interval, for example, for the analysis target of lithium battery capacity expansion, the template defines the first meta-event type as recruitment of specific technical personnel and the second meta-event type as public announcement of environmental impact assessment, and sets that the difference between the timestamps of the two must be within a time interval of 30 to 90 days; after receiving the parsed list of meta-event types, the distributed retrieval and aggregation unit performs concurrent retrieval for the meta-event types in the inverted index to obtain a set of candidate documents. This unit initiates the entity normalization process, extracting the unified social credit code explicitly recorded in the candidate documents as the subject identifier. If there is no explicit subject identifier in the document, a pre-trained named entity recognition model is called to scan the document text to extract the candidate entity identifier. Entity names are selected, and coreference resolution rules based on edit distance or semantic vector cosine similarity are used to map candidate entity names with different expressions to existing unique entity objects. After merging, each entity object is associated with a time series set sorted by occurrence timestamp, including event type, occurrence timestamp, source network address, and related semantic attribute values. The index maintenance unit monitors the data change flow of the inverted index database in real time. When a new document is detected, its features are extracted and incrementally mounted to the time series set of the corresponding entity object. If this mounting operation causes a change in the set state, the time series logic verification unit is triggered to re-execute the verification for the entity object. The distributed retrieval and aggregation unit performs entity normalization processing. The pre-trained named entity recognition model adopts a BERT-BiLSTM-CRF deep network architecture. The BERT pre-training layer reads the original text stream of the input document and outputs a character vector matrix. The BiLSTM layer extracts bidirectional semantic features from the character vector matrix. The CRF conditional random field layer calculates the globally optimal label path based on the feature sequence and outputs standardized entity names. For the extracted candidate entity names, the system executes a matching procedure based on Levenshtein edit distance hard constraint: calculate the edit distance between the candidate name and the standard name of the existing entity object. When the ratio of the edit distance to the string length is less than the preset normalization threshold of 0.15, the coreference resolution operation is triggered, and the document is attached to the entity object.

[0028] The temporal logic verification unit performs dynamic temporal constraint verification for each entity object, statistically analyzing the global document frequency of the event type in the inverted index corresponding to the time series coverage of that entity object. This statistical value characterizes the information density within the current time slice. The unit processes this global document frequency according to a preset inverse mapping relationship: when the global document frequency is lower than a preset baseline frequency threshold, a scaling factor of one or greater than one is generated to maintain or relax the standard relative time window constraint interval; when the global document frequency is higher than the baseline frequency threshold, a scaling factor of less than one is generated, and this factor is negatively correlated with the magnitude of the frequency exceedance. The unit uses this scaling factor to multiply and correct the upper and lower limits of the standard relative time window constraint interval defined in the event sequence template, generating a dynamic verification interval. The environmental baseline calibration module extracts the environmental attribute identifier of the entity object, namely the administrative division code of the registered location or the industry classification code, and calls the corresponding historical average time interval value from the preset baseline parameter library based on the identifier. The module calculates the ratio of the historical average time interval value to the preset standard reference time interval value to generate an environmental calibration coefficient. The environmental calibration coefficient is used first to pre-correct the boundary value of the standard relative time window constraint interval to generate a dedicated baseline interval. The aforementioned scaling factor is applied to the dedicated baseline interval. Finally, the unit traverses the time series set of the entity object to determine whether there is a combination of meta-event types that meets the template definition. The difference between the timestamps of each meta-event in the combination is calculated. When the difference falls within the dynamic verification interval after double correction, the entity object is determined to have passed the time series verification.

[0029] Before performing the timing verification, the timing logic verification unit performs slot consistency verification on the event sequence template containing semantic anchor slot definitions. This unit extracts the first attribute value set and the second attribute value set from the original documents corresponding to the first meta-event and the second meta-event, respectively. The extracted content includes the project location name or product name. If the original document does not directly contain the content pointed to by the slot definition, this unit parses the platform attribute information that published the original document, maps the platform name to the corresponding attribute value and adds it to the attribute value set. This unit calculates the intersection of the two attribute value sets. When there is a non-empty intersection or either attribute value set is empty, it is determined that the slot consistency is satisfied. At the same time, the information source heterogeneity verification unit parses the network address attribute of the source document for each meta-event in the time sequence set associated with the entity object and maps it to a preset information source category label, namely, government announcement category and news media category. For third-party recruitment, this unit counts the number of source category tags corresponding to all meta-events hit by the entity object. When the number of categories is greater than or equal to the preset minimum heterogeneity threshold, the entity object is allowed to enter the object sorting unit; otherwise, a blocking signal is generated to remove the entity object. In the timing logic verification unit, the step of determining whether there is an intersection is executed according to the standardized coding mapping logic. The system calls the preset administrative division code database and the national economic industry classification code table, extracts the text attribute values ​​from the first meta-event and the second meta-event, and maps them to unique digital code identifiers. The text attribute values ​​include place names and product names. The system performs a code comparison operation. When the two sets have completely identical digital code identifiers, the intersection is determined to be non-empty. The digital code comparison mechanism excludes character-level matching failures caused by text abbreviations, aliases, or differences in full-width and half-width formats.

[0030] The object sorting unit retains entity objects that have passed all the above checks and generates a sorting score based on the completeness of the matching of the entity objects to the event sequence template. To quantify the urgency of clues, the temporal residual weighting module of this unit obtains the preset ideal time interval value in the event sequence template, calculates the absolute deviation between the actual occurrence time interval of the meta-events in the entity objects and the ideal time interval value, and substitutes the absolute deviation into the formula. Calculate the attenuation coefficient ,in, The preset sensitivity constant, The actual time interval. To achieve the ideal time interval value, this module utilizes an attenuation coefficient. The basic scores of entity objects are weighted. In addition, the object sorting unit performs a mask suppression operation to detect whether there are any third-dimensional events of the suppression event type in the time series set of entity objects, such as records of dishonesty execution. If they exist, the unit calculates the relative time difference between the timestamp of the occurrence of the third-dimensional event and the timestamp of the occurrence of the hit meta-event in the entity object, and determines whether the relative time difference falls within the suppression time window. If so, the unit generates a forced blocking mask and sets the sorting score of the entity object to the preset minimum threshold.

[0031] Example 1: In the application scenario of investment promotion for the new energy vehicle power battery industry chain, due to the concentrated release of industry subsidy policies, a massive influx of news reports and corporate announcements containing keywords such as battery capacity expansion and billions of yuan in investment into the inverted index database in a short period of time has led to a sharp drop in the signal-to-noise ratio of single keyword searches. A large number of entities that have released investment intentions but have not taken any substantial action are mixed with real projects, forming an extremely high screening barrier. The system responds to the search command and calls the pre-set power battery production base landing event sequence template, defining the recruitment of specific process engineers as the first event and the public announcement of the construction environmental impact assessment as the second event, and defining the industry engineering construction standards. Both must meet the standard relative time window constraint range of 60 to 120 days. When the distributed retrieval and aggregation unit performs concurrent retrieval, the time sequence logic verification unit simultaneously monitors that the global document frequency of the above-mentioned meta-event type in the current time slice is higher than the historical baseline frequency threshold. This unit triggers the density and window reverse coupling mechanism, generates a scaling factor with a value less than one according to the preset reverse mapping relationship, and uses this scaling factor to automatically compress the standard relative time window constraint range to a more stringent dynamic verification range, such as narrowing it to 70 to 90 days, filtering out those follower entities that have news releases but have not completed the key action sequence within the strict engineering takt time.

[0032] Based on these stringent timing constraints, the system further performs source heterogeneity verification and slot consistency verification. For entity objects falling into the dynamic verification interval, the source heterogeneity verification unit parses the source network address attributes of the first-order and second-order events, retaining only entities whose first-order events originate from third-party recruitment platforms and whose second-order events originate from government information disclosure platforms. It utilizes the topological independence of different source categories to eliminate forged clues that only publish false combined information on a single company's official website. Simultaneously, the timing logic verification unit extracts the semantic slot values ​​of the project location from the two meta-event documents and calculates their intersection, ensuring that recruitment activities and environmental impact assessment announcements point to the same administrative region. Finally, the object sorting unit quantifies the value of the sparse entity set that has passed the above multiple logic verifications. The timing residual weighting module calculates the absolute deviation between the actual time interval between recruitment and environmental impact assessment events in the entity objects and the ideal engineering interval value preset in the template, and substitutes this absolute deviation into the attenuation formula. It calculates weighting coefficients and applies non-linear weighting to the basic scores of entity objects, so that entities whose execution rhythm is closest to the industry standard engineering cycle get the highest ranking position. It automatically converges thousands of high-noise data into a single-digit number of high-confidence business leads, realizing the deterministic transformation from massive unstructured text to high-value business intelligence.

[0033] Example 2: To verify the effectiveness of the investment promotion lead analysis system based on the temporal template and density inverse coupling mechanism in complex network environments, this example constructs a high-performance simulation experimental platform with 30 computing nodes. This platform is equipped with a distributed inverted index library, which contains 500 million unstructured business news and announcement data spanning 24 months. The data is collected from the public internet. To simulate high background noise in a real industrial environment, the experimental group actively injects interference data with a signal-to-noise ratio of 1:10 into the original dataset. For each real investment promotion project event, the system randomly generates 10 interference data points constructed by an automatic text generation algorithm. These data contain the same keywords but lack temporal logical connections, thus constructing a high-density false positive test environment to assess the system's stability and precision under extreme noise conditions. The core objective of the experiment is to quantitatively evaluate the system's dynamic adaptability under different background noise densities. Therefore, global document frequency was set as the core independent variable and divided into three gradient levels: low density, medium density, and high density, corresponding to 1.0 times, 5.0 times, and 20.0 times the baseline value, respectively. During the experiment, the system performed a search on a preset lithium battery expansion event sequence template. The standard relative time window defined by the template was 30 to 90 days. The experiment recorded the final number of clue outputs and monitored the key intermediate feature data generated within the system, especially the dynamic changes in the scaling factor and the corresponding adjustments to the dynamic verification interval boundary. In order to demonstrate the non-obviousness of the technical effect, three control groups were set up: control group A used the traditional fixed time window search, always maintaining a period of 30 to 90 days; control group B was sorted only based on keyword co-occurrence frequency; and control group C introduced a time window but lacked the density reverse coupling mechanism. Table 1 shows the comparison of key performance indicators between the experimental group and each control group under different background noise densities.

[0034] Table 1: Comparison of Retrieval Performance under Different Noise Environments

[0035]

[0036] Data shows that in a low-density environment, i.e., at 1.0 times the background noise density, the experimental group of this invention generated a scaling factor of 1.05 greater than 1, and the verification interval was fine-tuned to 28 to 95 days. At this time, the F1 score of the experimental group of this invention was 0.871, which was comparable to that of the control group A, indicating that the system did not sacrifice conventional performance in a low-noise environment. When the background noise density surged to 20.0 times, the precision of the control group A dropped from 82.5% to 14.2%, indicating that the fixed window could not resist the random intrusion of high-frequency noise, and the control group B, due to the complete lack of... Under order constraints, the precision rate was only 8.5%. In contrast, the experimental group of this invention, under 20.0 times higher noise, automatically generated a scaling factor of 0.25, forcibly narrowing the verification interval to 42 to 78 days. This narrowband filtering effect, although causing the recall rate to drop from 91.5% to 84.5%, successfully maintained the precision rate at a high level of 72.6%, and its F1 score of 0.781 was more than three times that of the control group A under the same conditions. Further analysis of the time-series residual weighted data output by the system, see Table 2, shows how the object sorting unit utilizes... and The deviation is used to stratify the value of entities.

[0037] Table 2: Example Table of Correlation between Temporal Residuals and Ranking Weights

[0038]

[0039] Table 2 shows the attenuation coefficient. It exhibits a non-linear negative correlation with the time interval deviation, Entity 001 Because the actual interval benchmark matches the ideal value, the Entity receives the full weight of 1.00 and the highest score. 003 Although it is on the edge of the dynamic verification interval of 42 to 78 days, the huge deviation of 34 days causes the weight to decay sharply to 0.32, thus being demoted in the sorting. This continuous evaluation mechanism based on residuals effectively distinguishes the differences between entities with valid logic and those with tight execution rhythms. In summary, the experimental data of this embodiment confirms that the present invention, through the dual mechanism of density and window inverse coupling and temporal residual weighting, can suppress logic false positives in high-noise environments while maintaining high sensitivity in low-noise environments.

[0040] Example 3: This example combines Figures 1 to 3 This section describes a big data-based investment lead analysis system, such as... Figure 1As shown, the entire data processing flow of the system begins with the receipt of the user's retrieval command. The query parsing unit responds to the command and calls the event sequence template that defines the standard relative time window. The distributed retrieval and aggregation unit performs concurrent retrieval of the inverted index and entity object normalization in the inverted index library that stores meta-events and entities. This inverted index library also accepts the monitoring data change stream and incremental mounting operation from the index maintenance unit. The aggregated entity data enters the time-series logic verification unit, which performs reverse coupling correction of the time window and dynamic time-series constraint verification in combination with the parameters of the environmental baseline calibration module. The data stream that passes the verification passes through the source heterogeneity verification unit for parsing source category labels and topological heterogeneity filtering. Finally, it enters the object sorting unit. With the support of the time-series residual weighting module, this unit performs time-series residual weighting and mask suppression operations, and finally outputs the sorted entity object list.

[0041] like Figure 2 As shown, the horizontal axis represents the similarity threshold. The graph shows a range from 0.1 to 0.9, with the vertical axis representing the numerical value of the performance index. The dotted line for recall shows a monotonically decreasing trend as the threshold increases, while the dashed line for precision shows a monotonically increasing trend. The solid line for the F0.5 index, the core criterion for judgment, exhibits a convex distribution characteristic of first increasing and then decreasing, indicating that at a specific threshold point, the system can achieve optimal overall performance under the condition of prioritizing precision. Figure 3 As shown, to achieve the ultimate goal of high-confidence investment lead analysis, the system adopts a multi-dimensional collaborative technical architecture. Under the temporal logic constraint branch, dynamic time window correction, temporal residual weighted attenuation coefficient, and environmental baseline calibration are deployed. Under the semantic content verification branch, event sequence template definition, slot consistency verification, and mask suppression operations are performed. Under the signal-to-noise ratio control branch, source heterogeneity verification, global document frequency statistics, and density reverse coupling mechanism are implemented. Under the system architecture support branch, incremental index maintenance, entity object normalization, and distributed retrieval and aggregation are included. All elements together constitute a complete closed loop of analysis logic.

[0042] Example 4: In the initialization phase for a specific application scenario in the integrated circuit manufacturing industry chain, the system faces the engineering challenge of parameter adaptation for the industry's unique long-cycle construction characteristics. Specifically, it needs to establish an accurate density inverse mapping function and sorting sensitivity benchmark for the specific event sequence of wafer fab construction. The system initiates a baseline frequency calibration procedure, using distributed retrieval and aggregation units to perform a full scan of the historical data from the past thirty-six months in the inverted index database. It extracts the daily average document frequency of the two meta-event types: lithography machine procurement bidding and cleanroom renovation bidding, and calculates the arithmetic mean to determine the benchmark frequency threshold. Based on this physical benchmark, the system concretizes the inverse mapping relationship into a piecewise nonlinear control function, which is used to calculate the scaling factor in real-time retrieval. The function is defined as: when the real-time global document frequency... With reference frequency threshold ratio When less than or equal to 1.2, the scaling factor Locked to 1.0 to maintain standard probe bandwidth; when the ratio When the value is greater than 1.2, the system performs logarithmic decay calculation. This ensures that when industry enthusiasm experiences an exponential and irrational surge, the time window constraint range can smoothly converge with a logarithmic trend, thereby physically preventing signal truncation due to an excessively narrow window or noise avalanche due to an excessively wide window.

[0043] To eliminate the sensitivity constant in object sorting units To mitigate the reliance on experience, the system executes an offline optimization procedure based on historical truth values. The system loads 500 verified, implemented wafer fab projects as a positive sample set and 5,000 confirmed false rumor projects as a negative sample set to construct a discriminative power verification environment. The system then uses a sensitivity constant... As a variable parameter, it is calculated by iterating through values ​​from 0.01 to 1.00 in steps of 0.01, for each... The step size and time-series residual weighting module calculate the average ranking scores for the positive and negative sample sets respectively, and obtain the Euclidean distance between them as a discriminant index. As the value increases, the discriminant exhibits a bell-shaped distribution pattern, first rising and then falling, and... The system reaches its peak value at 0.12, and therefore solidifies 0.12 as the optimal sensitivity constant for this industry scenario. This calibration process shifts the core parameter setting weight of the sorting algorithm from manual experience to statistical optimization logic based on objective historical data. Through the above deterministic calibration and optimization process, the system completes the initial configuration for the integrated circuit industry. In live operation, facing the information flow explosion caused by sudden positive developments in the industry, the timing logic verification unit uses the ratio monitored in real time. Automatically generate scaling factors using functions The standard relative time window constraint interval is precisely compressed to 57% of its original width. Simultaneously, the object sorting unit utilizes the calibrated... The system sorts entity objects that meet the time constraints, successfully placing the three target entities whose construction rhythm best matches the industry standard project cycle at the top of the list, while suppressing low-quality clues that meet the time logic but have large time interval deviations at the bottom of the list, thus achieving adaptive matching and high-precision response to industry-specific patterns.

[0044] Example 5: Before applying the investment lead analysis system to a new vertical industry or geographical region, a standardized offline calibration and data filling procedure is executed to ensure that the historical average time interval values ​​in the baseline parameter library and the preset benchmark frequency threshold have sufficient statistical representativeness. This procedure activates the data acquisition module to perform a full backtracking of the historical public data of the target industry or region, with the time span set to the past thirty-six months to sixty months, ensuring coverage of at least one complete industry economic cycle. Natural language processing algorithms are used to extract all confirmed and implemented real project cases from the backtracking data to build a truth sample library. For each project entity in this sample library, the system automatically calculates the actual time interval between its core meta-event pairs and applies box plot statistics to remove outliers. Finally, the arithmetic mean and standard deviation of the remaining samples are calculated and solidified as the historical average time interval baseline for this specific environment. At the same time, the daily average document frequency distribution of each meta-event type on the time axis is statistically analyzed to identify the stable noise level under non-sudden conditions and set it as the benchmark frequency threshold, thereby completing the parameter initialization for a specific environment and avoiding the parameter black box risk caused by differences in industry attributes.

[0045] Furthermore, to ensure consistent system performance under complex and ever-changing network and hardware conditions, a pre-deployment calibration procedure must be performed before the system is officially launched. This procedure requires stress testing the system's retrieval and verification performance in the target deployment environment using a pre-built standard test dataset. This standard test dataset contains a preset proportion of real clues and interfering noise, and the noise data generation mechanism covers various forgery modes such as text homogenization, temporal randomization, and single source. After the system loads this dataset, it automatically runs the entire processing logic and monitors the convergence speed of the dynamic verification interval and the score distribution of the temporal residual weighting module in real time. If the precision or recall rate output by the system deviates from the preset performance index range, the procedure will trigger an automatic fine-tuning mechanism to make step-by-step adjustments to the scaling coefficient calculation logic or sensitivity constant in the inverse mapping relationship until the system reaches the optimal signal-to-noise ratio balance point under the current hardware resources and network latency conditions.

[0046] Example 6: To ensure the accuracy of the coreference resolution module in handling heterogeneous entity names, the system executes a standardized calibration procedure for semantic similarity thresholds before deployment. The engineering team constructs a benchmark test set containing 100,000 pairs of entity name samples. This test set covers full and abbreviation names, former and current names, and confusion cases with similar literal meanings but different entities. Ground truth labels are manually added. The system uses a pre-defined semantic vector model to calculate the cosine similarity between sample pairs, generating a similarity score set ranging from zero to one. The system then sets the similarity threshold... As the independent variable, a linear scan from zero to one is performed with a step size of 0.01, and the precision at each step size is calculated. With recall Considering that in investment promotion risk control scenarios, incorrectly merging events from two different entities can lead to serious logical misjudgments, the engineering procedures prioritize the weighted harmonic mean index based on precision rate. As the objective function for optimization, The threshold is set to 0.5 to reduce the weight of recall; the system draws the threshold and... The relationship curve is used to locate the extreme points of the curve and calculate the similarity value corresponding to each point. The threshold for entity normalization in the production environment is solidified to ensure that when the system is faced with a massive number of newly added documents, entity merging is only performed when the semantic similarity strictly meets the statistically optimal solution.

[0047] To address the physical I / O bottleneck in distributed retrieval and aggregation units when handling high-concurrency time-series verification, the system implements an entity affinity-based index sharding strategy. The system abandons the traditional document ID-based random sharding mode and instead adopts a directed routing mechanism based on the hash value of the subject identifier. The system defines the sharding key calculation formula as follows: ,in, For normalized entity unique identifiers, The strategy, which determines the number of physical storage nodes, forces all discrete event documents belonging to the same entity object to be physically routed and stored in the index shard of the same computing node, regardless of the publication time or source channel. During the retrieval phase, the processing unit can send instructions to a specific node to complete the timestamp extraction and difference calculation of all related events in the local memory of that node, eliminating network latency caused by cross-node data transmission and shuffling. Through this physical-level architectural optimization, the system can still maintain the end-to-end response time of a single query at the millisecond level when processing complex time-series templates spanning several years.

[0048] 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.

[0049] 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. A big data-based investment lead analysis system, characterized in that, include: The query parsing unit is used to respond to retrieval commands and call a preset event sequence template. The event sequence template limits at least two logically related meta-event types and defines the standard relative time window constraint interval that the meta-event types must satisfy. The distributed retrieval and aggregation unit is used to perform concurrent retrieval in the inverted index for the meta-event type to obtain a set of candidate documents. The candidate documents are merged into the corresponding unique entity objects using the subject identifier in the candidate documents. The entity objects are associated with a time series set containing the event type and the occurrence timestamp. The temporal logic verification unit is used to perform dynamic temporal constraint verification for each entity object. It counts the global document frequency of the meta-event type corresponding to the time series coverage of the entity object in the inverted index, calculates the scaling factor corresponding to the global document frequency based on the preset reverse mapping relationship, and uses the scaling factor to multiply and correct the upper and lower limits of the standard relative time window constraint interval to generate a dynamic verification interval. It determines whether there is a meta-event type combination in the time series set that satisfies the event sequence template definition, and calculates the difference between the occurrence timestamps of each meta-event in the combination. When the difference falls within the dynamic verification interval, the entity object is determined to pass the verification. The object sorting unit is used to retain only the entity objects that pass the validation, and generate a sorting score based on the completeness of the matching of the entity objects with the event sequence template, and output a list of entity objects sorted by the sorting score.

2. The investment lead analysis system based on big data according to claim 1, characterized in that, The object sorting unit includes a temporal residual weighting module. This module is used to obtain the preset ideal time interval value in the event sequence template, calculate the absolute deviation between the actual occurrence time interval of the meta-event in the entity object and the ideal time interval value, and substitute the absolute deviation into the following formula to calculate the attenuation coefficient. : ,in, The preset sensitivity constant, The actual time interval. As the ideal time interval value, the temporal residual weighting module uses the attenuation coefficient to weight the basic score of the entity object.

3. The investment lead analysis system based on big data according to claim 1, characterized in that, The system also includes a source heterogeneity verification unit, which is used to parse the network address attribute of the source document of each meta-event in the time series set associated with the entity object and map it to a preset source category label. The system counts the number of source category labels corresponding to all meta-events hit by an entity object and determines whether the number of categories is greater than or equal to a preset minimum heterogeneity threshold. When the number of categories meets the minimum heterogeneity threshold, the entity object is allowed to enter the object sorting unit; otherwise, a blocking signal is generated to remove the entity object.

4. The investment lead analysis system based on big data according to claim 1, characterized in that, The event sequence template called by the query parsing unit also contains at least one semantic anchor slot definition. The timing logic verification unit is also used to perform slot consistency verification operations: for the first-order event and the second-order event in the time sequence set associated with the entity object, the first attribute value set and the second attribute value set are extracted from the original documents corresponding to the first-order event and the second-order event respectively according to the semantic anchor slot definition; it is determined whether there is an intersection between the first attribute value set and the second attribute value set; only when there is an intersection or either of the attribute value sets is an empty set, it is determined that the first-order event and the second-order event satisfy slot consistency.

5. The investment lead analysis system based on big data according to claim 1, characterized in that, The event sequence template also defines the suppression event type and the associated suppression time window. The object sorting unit is also used to perform mask suppression operation, detect whether there is a tertiary event of the suppression event type in the time sequence set of entity objects; if so, calculate the relative time difference between the occurrence timestamp of the tertiary event and the occurrence timestamp of the hit meta events in the entity object; and determine whether the relative time difference falls within the suppression time window. If so, a forced blocking mask is generated, setting the sorting score of the entity object to the preset minimum threshold.

6. The investment promotion lead analysis system based on big data according to claim 1, characterized in that, The timing logic verification unit also includes an environmental baseline calibration module, which is used to extract the environmental attribute identifier of the entity object and call the corresponding historical average time interval value from the preset baseline parameter library based on the environmental attribute identifier. Calculate the ratio of the historical average time interval value to the preset standard reference time interval value to generate the environmental calibration coefficient; use the environmental calibration coefficient to pre-correct the boundary values ​​of the standard relative time window constraint interval defined in the event sequence template to generate a dedicated baseline interval for the entity object, and use the dedicated baseline interval as the base interval for subsequent application scaling coefficients.

7. The investment lead analysis system based on big data according to claim 4, characterized in that, When the timing logic verification unit extracts the first attribute value set, if the original document corresponding to the first meta-event does not directly contain the content pointed to by the semantic anchor slot definition, it parses the platform attribute information of the published original document and maps the platform attribute information to the corresponding attribute value and adds it to the first attribute value set.

8. The investment lead analysis system based on big data according to claim 1, characterized in that, When performing concurrent retrieval, the distributed retrieval and aggregation unit is also used to preprocess documents in the inverted index, extracting the unified social credit code or normalized subject name from the document as subject identifier; if there is no explicit subject identifier in the document, the pre-trained named entity recognition model is used to extract candidate entity names from the document, and the candidate entity names are mapped to existing entity objects based on the preset coreference resolution rules.

9. The investment lead analysis system based on big data according to claim 1, characterized in that, The reverse mapping relationship is configured as follows: when the global document frequency is lower than the preset base frequency threshold, the scaling factor is a value of one or greater than one; when the global document frequency is higher than the base frequency threshold, the scaling factor is a value of less than one, and the value of the scaling factor is negatively correlated with the extent of the global document frequency exceeding the threshold, and the compression standard is constrained by the relative time window range.

10. A big data-based investment lead analysis system according to claim 1, characterized in that, The system also includes an index maintenance unit, which monitors the data change flow in the inverted index database in real time. When a new document is detected, the system extracts the meta-event type, occurrence timestamp, and subject identifier of the new document and increments the new document to the time series set of the corresponding entity object. If the new document causes a change in the time series set of the entity object, the system triggers the timing logic verification unit to re-execute the dynamic timing constraint verification for the changed entity object.

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