An intelligence agent adaptive collection method and system based on long memory and long-time scheduling

Abstract

The application provides an intelligence agent adaptive collection method and system based on long memory and long-time scheduling, and relates to the technical field of data processing.The method comprises the following steps: step 1, receiving an intelligence collection task, analyzing task parameters to construct a target object image, extracting and standardizing target features, and forming a structured target image; step 2, based on the structured target image, abstracting past similar tasks into experience triples and performing vectorization coding to store in an experience library, constructing a semantic retrieval index to implement hierarchical storage management; matching similar experiences from the experience library through semantic retrieval, and combining similarity and past success rate to generate an adaptive collection strategy. The application realizes continuous and stable execution of long-period intelligence collection tasks, and improves the collection success rate and recovery efficiency in a dynamic confrontation environment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an adaptive data acquisition method and system for intelligence agents based on long memory and long-term scheduling. Background Technology

[0002] Currently, in the field of cyberspace intelligence gathering, especially when conducting continuous and long-term monitoring and analysis of key targets such as specific websites and online platforms, automated collection tasks are usually deployed. These tasks often need to run continuously for several hours or even several days to obtain a continuous data stream.

[0003] In existing technical solutions, a common practice is to rely on pre-configured fixed collection strategies, such as fixed access frequencies, request header formats, and parsing rules, to drive the execution of crawlers or automated scripts. For example, when continuously monitoring a news website with dynamic content and basic anti-crawling mechanisms, technicians may set a fixed access interval and a set of static request parameters. However, the target site's access strategy, page structure, or anti-crawling mechanism may dynamically adjust over time. When such changes occur, fixed collection strategies may be difficult to adapt to, easily leading to collection requests being identified as abnormal traffic and blocked, such as returning CAPTCHAs or denial of service, causing the collection task to be interrupted. After the task is interrupted, manual intervention is usually required to re-analyze the target changes, adjust strategy parameters, and restart execution from the task's starting point or interruption point. This process is not only inefficient, but may also lead to repeated problems of strategy incompatibility when facing similar scenarios because it is impossible to effectively learn from past successful experiences or avoid past failure patterns. In addition, for tasks that need to run for a long time, the recovery mechanism of existing solutions may not be flexible enough when dealing with unexpected interruptions such as network fluctuations and node failures. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an adaptive intelligence collection method and system based on long memory and long-term scheduling, so as to realize the continuous and stable execution of long-cycle intelligence collection tasks and improve the success rate and recovery efficiency of collection in dynamic adversarial environments.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, an adaptive data collection method for intelligence agents based on long memory and long-term scheduling, the method comprising:

[0007] Step 1: Receive intelligence gathering tasks, analyze task parameters to construct target profiles, extract and standardize target features to form structured target profiles;

[0008] Step 2: Based on the structured target profile, similar past tasks are abstracted into experience triples and vectorized and stored in the experience database. A semantic retrieval index is constructed and hierarchical storage management is implemented. Similar experiences are matched from the experience database through semantic retrieval, and an adaptive collection strategy is generated by combining similarity and past success rate.

[0009] Step 3: Decompose the task into multiple sub-tasks according to the adaptive acquisition strategy. Before distributing them to physical execution nodes, assess the actuator bearing capacity of sub-tasks involving torsional loads, calculate the maximum tolerable torque threshold under the current working condition, and compare it with the drive load required by the task. If it exceeds the safe range, automatically adjust the intensity of acquisition actions, reduce the operating frequency, and allocate it to a node with higher bearing capacity. After verification, serialize and persistently store the task status, and asynchronously distribute compliant sub-tasks to each acquisition node for execution.

[0010] Step 4: Based on the event-driven task scheduling reported by the collection nodes, dynamically update the task status and strategy parameters, and periodically generate task checkpoints;

[0011] Step 5: If the task is abnormally terminated, select the recovery mode based on the most recent checkpoint and persistent status; after the task is completed normally, write the experience triplet corresponding to this task back to the experience library, and perform exponential decay weighting and archiving updates on the experience in the library.

[0012] Secondly, an adaptive data collection system for intelligence agents based on long memory and long-term scheduling includes:

[0013] The module is used to receive intelligence gathering tasks, parse task parameters to build a target profile, extract and standardize target features, and form a structured target profile.

[0014] The generation module is used to abstract past similar tasks into experience triples based on structured target profiles, and then vectorize and encode them into an experience database. It also constructs a semantic retrieval index to implement hierarchical storage management. Through semantic retrieval, it matches similar experiences from the experience database and generates an adaptive collection strategy by combining similarity and past success rate.

[0015] The distribution module decomposes tasks into multiple subtasks according to an adaptive acquisition strategy. Before distributing them to physical execution nodes, it assesses the actuator bearing capacity of subtasks involving torsional loads, calculates the maximum tolerable torque threshold under the current working conditions, and compares it with the drive load required by the task. If the load exceeds the safe range, it automatically adjusts the intensity of the acquisition action, reduces the operating frequency, and allocates the task to a node with higher bearing capacity. After verification, the task status is serialized and persistently stored, and the compliant subtasks are asynchronously distributed to each acquisition node for execution.

[0016] The update module dynamically updates task status and strategy parameters based on event-driven task scheduling reported by the collection nodes, and periodically generates task checkpoints.

[0017] The write-back module is used to select the recovery mode based on the most recent checkpoint and persistent status if the task is abnormally terminated; after the task is completed normally, the experience triplet corresponding to this task is written back to the experience library, and the experience in the library is updated by exponential decay weighting and archiving.

[0018] The above-described solution of the present invention has at least the following beneficial effects:

[0019] By extracting and standardizing the features of target objects, a structured target profile is constructed, improving the targeting and rationality of strategy selection. Past collection tasks are abstracted into experience triples and stored and managed hierarchically in a vectorized manner. Semantic retrieval enables rapid matching of similar experiences, reducing the cost of repeated trial and error and avoiding similar risks. Adaptive collection strategies are generated by combining similarity and historical success rates to cope with dynamic changes in target protection strategies and network environments, improving the collection success rate in complex scenarios. Before subtask distribution, the load-bearing capacity assessment of the execution mechanism is added. By comparing the torque threshold with the driving load, the intensity of collection actions, the running frequency, and the node allocation are dynamically adjusted to avoid equipment failures and task interruptions caused by the load exceeding the node's load-bearing capacity. This improves the efficiency and reliability of long-cycle tasks. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an adaptive data collection method for intelligence agents based on long memory and long-term scheduling, provided by an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of an adaptive data collection system for an intelligence agent based on long memory and long-term scheduling, provided by an embodiment of the present invention. Detailed Implementation

[0022] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0023] like Figure 1 As shown, an embodiment of the present invention proposes an adaptive data collection method for intelligence agents based on long memory and long-term scheduling. The method includes the following steps:

[0024] Step 1: Receive intelligence gathering tasks, analyze task parameters to construct target profiles, extract and standardize target features to form structured target profiles;

[0025] Step 2: Based on the structured target profile, similar past tasks are abstracted into experience triples and vectorized and stored in the experience database. A semantic retrieval index is constructed and hierarchical storage management is implemented. Similar experiences are matched from the experience database through semantic retrieval, and an adaptive collection strategy is generated by combining similarity and past success rate.

[0026] Step 3: Decompose the task into multiple sub-tasks according to the adaptive acquisition strategy. Before distributing them to physical execution nodes, assess the actuator bearing capacity of sub-tasks involving torsional loads, calculate the maximum tolerable torque threshold under the current working condition, and compare it with the drive load required by the task. If it exceeds the safe range, automatically adjust the intensity of acquisition actions, reduce the operating frequency, and allocate it to a node with higher bearing capacity. After verification, serialize and persistently store the task status, and asynchronously distribute compliant sub-tasks to each acquisition node for execution.

[0027] Step 4: Based on the event-driven task scheduling reported by the collection nodes, dynamically update the task status and strategy parameters, and periodically generate task checkpoints;

[0028] Step 5: If the task is abnormally terminated, select the recovery mode based on the most recent checkpoint and persistent status; after the task is completed normally, write the experience triplet corresponding to this task back to the experience library, and perform exponential decay weighting and archiving updates on the experience in the library.

[0029] In this embodiment of the invention, a structured target profile is constructed by extracting and standardizing the features of the target object, thereby improving the targeting and rationality of strategy selection. Past collection tasks are abstracted into experience triples and stored and managed in a vectorized manner. Semantic retrieval enables rapid matching of similar experiences, reducing the cost of repeated trial and error and avoiding similar risks. An adaptive collection strategy is generated by combining similarity and historical success rate to cope with the dynamic changes in target protection strategies and network environment, thereby improving the collection success rate in complex scenarios. Before subtask distribution, the carrying capacity assessment of the execution mechanism is added. By calculating the torque threshold and comparing the driving load, the intensity of collection actions, the running frequency, and the node allocation are dynamically adjusted to avoid equipment failure and task interruption caused by the load exceeding the node's carrying capacity. This improves the efficiency and reliability of long-cycle tasks.

[0030] In a preferred embodiment of the present invention, step 1 above, which involves receiving an intelligence gathering task, parsing task parameters to construct a target profile, extracting and standardizing target features, and forming a structured target profile, may include:

[0031] In this embodiment of the invention, step 110 involves receiving and parsing the original task parameters of the intelligence gathering task to obtain a task element set consisting of target identifiers, key attribute description text, and task constraints. Specifically, this includes: first, receiving original task parameters from a user-submitted task application form, a system-preset task template import file, or a third-party platform interface push. The original parameter format may include text descriptions, structured table data, or key-value pair format data. Next, the received original parameters are format-validated, removing invalid characters, redundant spaces, and abnormal data that do not meet the preset format requirements. For missing required parameters, such as target core identifiers and basic collection requirements, a system pop-up prompts the user to complete them. Then, the parameters are categorized according to preset parameters. The rules break down and extract valid parameters after verification. Target identifiers are extracted, including the target website's domain name, unique URL address, asset number, or online platform's unique identifier ID, full company name, and other information that uniquely points to the target object. Key attribute description text is extracted, including descriptive information such as the target object's industry category, core content type, and page presentation format. Task constraints are extracted, including constraints such as collection cycle, timeliness requirements, collection depth, output format restrictions, and resource usage limits. Finally, the extracted target identifiers, key attribute description text, and task constraints are compiled and summarized to form a clear and unambiguous set of task elements, ensuring that each element has a clear definition and corresponding value.

[0032] Step 111: Based on the task element set, using preset feature recognition rules, simultaneously extract the target's static attribute features, temporal dynamic behavior features, and contextual features related to the external environment. Specifically, this includes: First, retrieving a preset feature recognition rule library, which contains various preset rules based on keyword matching, feature field mapping, and behavior pattern classification. Based on this rule library, and combining information from the task element set, simultaneously extracting three types of features: Static attribute features are extracted by comparing the key attribute description text in the task element set with the target identifier against a preset type tag library to determine the target type. For example, if the key attribute description text contains a static page with no real-time interaction, it is determined to be a static type; if it contains dynamic loading and real-time data refresh, it is determined to be a dynamic type; if it contains a single-page application with no page jump, it is determined to be a SPA type. Access entry features are extracted by determining the access entry form based on the URL or interface information in the target identifier. Basic configuration features are extracted by determining static configuration information such as server type, communication protocol type, and port number through public network information associated with the target identifier. Temporal dynamic behavior features are extracted by analyzing the collection cycle, timeliness requirements, and historical access records (if any) in the task element set. The update pattern of the target content is analyzed. For example, if the task element set requires continuous collection for 7 days, and historical records show that new content was added to the target around 10 AM every day in the past 30 days, then the update frequency is determined to be daily. The response time of each access in the past 30 days is statistically analyzed, divided into morning, noon, and evening time periods, and the average response time for each time period is calculated. The time period with the longest average response time is determined as the peak access time. The trend of response latency changes in historical accesses is extracted. For example, if the response latency of the last 10 accesses is 200 milliseconds, 250 milliseconds, and 230 milliseconds respectively, the latency change is determined to be relatively stable. Contextual relationship features are extracted. Based on the key attribute description text in the task element set, the relationship between the target and other sites is analyzed. For example, if the description text mentions associated subsite A and subsite B, then the jump relationship is extracted, indicating that the target main site can jump to subsite A and subsite B. Combined with publicly available industry policies and network environment information, external environmental influencing factors are extracted. For example, if industry policies specify that data of a certain type of commodity must be publicly disclosed, then the impact of industry policy requirements on the target data disclosure content is extracted. If historical access records show that the success rate of accessing the target is low under a specific network operator environment, then the impact of network operator type on the target access stability is extracted.

[0033] Step 112 involves processing static attribute features, time-series dynamic behavior features, and contextual features by applying pre-defined standardization rules and normalization algorithms corresponding to each feature type to eliminate numerical differences caused by different data sources and units. Specifically, this includes: processing static attribute features by applying pre-defined static feature standardization rules to unify the same feature with different descriptions into a fixed enumeration value, such as unifying static websites, static pages, and purely static content sites as static types; unifying dynamic websites, dynamic interactive sites, and real-time updated pages as dynamic types; and for data without quantification, which does not require normalization calculations, simply organizing various static attribute features into a unified format of feature item-feature value according to a pre-defined classification order, such as target type-dynamic type; access point-HTTPS webpage link; server type-Nginx. For time-series dynamic behavior features, pre-defined dynamic feature standardization rules are applied to convert non-quantitative descriptions into unified hierarchical expressions, such as unifying daily updates and high-frequency updates as high-frequency updates; weekly updates and medium-frequency updates as medium-frequency updates; and monthly updates, low-frequency updates, and irregular updates as low-frequency updates. Quantification is also applied to response latency, update intervals, etc. The data is processed using a normalization algorithm. First, the range of values ​​for this feature for all similar targets is statistically analyzed to determine the minimum and maximum values. Then, the difference between the current target's feature value and the minimum value is calculated. This difference is then divided by the difference between the maximum and minimum values ​​to obtain the normalized relative value, eliminating dimensional differences. For example, if the minimum response delay for similar targets is 100 milliseconds and the maximum is 1000 milliseconds, and the current target's response delay is 500 milliseconds, then the difference between 500 milliseconds and 100 milliseconds is calculated. This difference is then divided by the difference between 1000 milliseconds and 100 milliseconds to obtain the normalized value. The relative delay value after normalization; processing contextual features, applying preset association feature standardization rules, and standardizing ambiguous expressions and repetitive information, such as unifying "may be affected by policy" and "probably affected by industry policy adjustments" as "affected by industry policy"; removing duplicate association descriptions, such as extracting multiple times from the main site to sub-site A, keeping only one, without quantitative data, no need for normalization calculation, and organizing association features into a unified format of association type-association content, such as jump relationship - main site to sub-site A, sub-site B; external influencing factors - industry policy, network operator type.

[0034] Step 113 involves using the standardized feature data as input to populate a preset multi-dimensional profile template. The template engine then performs structured organization and integration to generate a complete structured target profile representing the intelligence features of the target object. Specifically, this includes: First, retrieving the preset multi-dimensional profile template, which contains three fixed feature categories: static attribute features, temporal dynamic behavior features, and contextual features. Each category corresponds to the standardized feature items from Step 112. Next, the processed static attribute feature set, temporal dynamic behavior feature set, and contextual feature set from Step 112 are populated into the corresponding categories and feature items of the template, ensuring that each feature item is accurately populated with its corresponding standardized feature value, without omissions or errors. For example, static attribute features... The target type field in the attribute feature column is filled with dynamic type, and the update frequency field in the dynamic behavior feature column is filled with high-frequency update. Then, the template engine organizes all the filled feature data into a structured format, integrating and sorting them according to the hierarchical relationship of feature category column - feature item - feature value, forming a unified structured format, such as static attribute feature - target type - dynamic type; static attribute feature - access point - HTTPS webpage link; time-series dynamic behavior feature - update frequency - high-frequency update; context association feature - jump relationship - main site to sub-site A, sub-site B. Finally, the integrated feature data is checked for completeness to confirm that all required feature items have been filled and the format meets the template requirements. After the check passes, a complete and standardized structured target profile is generated, which comprehensively covers the core intelligence features of the target object.

[0035] By simultaneously extracting and refining the three types of features, the target object can be comprehensively characterized from three dimensions: static attributes, dynamic behavior, and external associations. This improves the adaptability of the collection strategy to the target object and ensures that long-term collection tasks can be steadily advanced based on target cognition.

[0036] In a preferred embodiment of the present invention, step 2 above, based on the structured target profile, abstracts past similar tasks into experience triples and performs vectorized encoding to store them in an experience database, constructs a semantic retrieval index to implement hierarchical storage management; and matches similar experiences from the experience database through semantic retrieval, and generates an adaptive collection strategy by combining similarity and past success rate, may include:

[0037] In this embodiment of the invention, step 220 involves abstracting previously completed tasks into experience triples based on the dimensions of the structured target profile. A pre-trained semantic model is used to vectorize the task scenario description and acquisition strategy within the experience triples, resulting in task scenario semantic vectors and acquisition strategy semantic vectors, which are then correlated with the recorded success rate metric calculated based on the corresponding execution results. Specifically, this includes determining the core dimensions of the structured target profile (static attribute features, temporal dynamic behavior features, and contextual association features). Using these three dimensions as a unified reference standard, each previously completed acquisition task is systematically reviewed, and the target features of that task are extracted, strictly corresponding to the three dimensions of the structured target profile. Static attribute features need to be refined to the target type, access entry address, and basic page structure; temporal dynamic behavior features need to record the target's content update frequency. The analysis should include: 1) Analyzing the response latency fluctuation range and page interaction logic changes during historical accesses; 2) Defining the target's industry sector, its links to other related targets, and the impact of the external network environment on target access; 3) Extracting the task's collection strategy, fully recording all effective parameter configurations during execution, including access frequency, request header parameters, proxy node selection rules, parsing rule types, retry counts, backoff time, and blocking response methods; 4) Extracting the task's execution effect, detailing the final completion status, the validity of the collected data, whether blocking occurred during execution, the number of times blocking occurred and the triggering scenarios, the total task time, and resource consumption; and 5) Integrating the extracted target features, collection strategy, and execution effect information to form a one-to-one corresponding experience triplet, ensuring that each triplet can completely reconstruct the core key information of a historical collection task.

[0038] The task scenario descriptions in the experience triples are vectorized and encoded. First, the task scenario descriptions are decomposed into textual descriptions of feature items corresponding to the dimensions of the structured target profile. For example, static attribute features include static news websites, access points, and pages containing three levels of sub-links; time-series dynamic behavior features include daily updates at 8 AM, stable response latency of 1-3 seconds, and no changes in interaction logic in the past month; contextual features include news and information domain, no directly related target, and access congestion during weekday morning rush hours. The textual descriptions of each feature item are converted into numerical values ​​according to a unified rule. For enumerable feature items, corresponding numerical mappings are pre-defined; for continuous feature items, values ​​are assigned according to preset intervals; for descriptive feature items, levels are assigned according to the degree of influence. All numerical values ​​corresponding to all feature items are arranged in a fixed order to form a numerical sequence of fixed length. This numerical sequence is the task scenario semantic vector, ensuring that the length of the task scenario semantic vector for each historical task is consistent and the order of feature items is consistent.

[0039] The collection strategy in the experience triple is vectorized and encoded. First, the collection strategy is decomposed into textual descriptions of specific parameters, such as an access frequency of once every 8 seconds, a User-Agent field value of the default Chrome browser identifier, a cookie configuration of a login-state cookie, a fixed proxy pool for proxy node rotation, HTML parsing as the parsing rule, 3 retries, a 2-second backoff time for the first retrieval, a 5-second backoff time for the second retrieval, a 10-second backoff time for the third retrieval, and switching proxy nodes when a CAPTCHA is encountered. Following the encoding logic consistent with the task scenario description, the textual description of each parameter is converted into a numerical value. For parameters with fixed values, enumeration mapping is used; for quantified parameters, the specific numerical value of the parameter is directly taken or the numerical value is mapped according to a range; for action-type parameters, values ​​are assigned according to the action type. All the numerical values ​​corresponding to the parameters are arranged in a fixed order to form a numerical sequence of fixed length. This numerical sequence is the semantic vector of the collection strategy, ensuring that its length is consistent with the semantic vector of the task scenario and that the parameter order remains unchanged.

[0040] Calculate the recorded success rate metric for executed tasks, count the total number of subtasks for the task, and, according to the task splitting rules, record the total number of independent execution units after breaking down the historical task as the total number of subtasks. Count the number of successfully collected subtasks. The criteria for judging the success of each subtask are: completing the preset collection range, collecting no missing data, and not being interrupted due to blockage. Subtasks that meet these criteria are recorded as successful subtasks. Count their total number, calculate the percentage of valid data, and then count the total amount of data collected by the task, and finally count the amount of valid data. The percentage of valid data = valid data amount ÷ total data amount. The success rate metric has been recorded. The success rate metric = (number of successful subtasks ÷ total number of subtasks) × percentage of valid data. For example, if there are 8 successful subtasks, 10 total subtasks, and 90% of the data is valid, then the success rate metric = (8 ÷ 10) × 90% = 72%. The vectorized data is associated with the success rate metric. The semantic vector of the task scenario and the semantic vector of the collection strategy corresponding to the same experience triple are bound to the calculated recorded success rate metric. This ensures that the other two related data can be quickly queried through any one vector, forming a complete vectorized experience data of vector pair + success rate.

[0041] Step 221: Store the vectorized experience data associated with success rate indicators into the experience database; perform cluster analysis based on the distribution of semantic vectors of all task scenarios in the experience database, and construct a hierarchical semantic index structure based on the clustering results to achieve hierarchical storage and management of experience data; specifically, this includes: storing vectorized experience data into the experience database, establishing data storage association rules, using experience ID as a unique identifier, assigning a unique experience ID to each group of vectorized experience data, the experience ID containing the task execution timestamp and target identifier prefix, storing them according to data type, dividing the experience database into three storage areas to store task scenario semantic vectors, collection strategy semantic vectors, and recorded success rate indicators respectively, and establishing associations between data in each storage area through experience IDs, i.e., inputting an experience ID allows simultaneous retrieval of the corresponding vectors and success rate indicators in all three areas, ensuring the uniqueness and accuracy of data association, and batch writing data, writing all associated vectorized experience data in batches to the corresponding storage areas according to the experience ID order, recording the number of records during writing. Based on the timestamps, to provide a basis for experience aging management, cluster analysis is performed on the semantic vectors of task scenarios to determine the clustering judgment criteria. The similarity between two semantic vectors of task scenarios is the core judgment criterion. The similarity is calculated by calculating the absolute value of the difference between the corresponding values ​​in the two vectors, summing all the absolute values ​​of the difference and taking the average. The smaller the average value, the closer the features of the two vectors are and the higher the similarity. A preset similarity threshold (e.g., average value ≤ 2) is set. When the average similarity between two vectors is ≤ 2, they are judged to be highly similar. Vectors are compared and classified one by one. The first semantic vector of task scenario is extracted from the experience base as the baseline vector. Then, other vectors are extracted in turn and their similarity is calculated with the baseline vector. All vectors that are highly similar to the baseline vector are classified into the first category. Then, a new baseline vector is extracted from the unclassified vectors, and the above comparison and classification process is repeated until all semantic vectors of task scenarios are classified into the corresponding categories. Finally, multiple non-overlapping vector categories are formed, and the vectors in each category meet the condition of high similarity.

[0042] A hierarchical semantic index structure is constructed, dividing the data into primary categories. Based on the core features of each vector category, all categories are further divided into several primary categories. The dimensions of this division include target type (static target category, dynamic target category, single-page application category), protection level (no anti-scraping category, simple anti-scraping category, advanced anti-scraping category), and domain attributes (news and information category, e-commerce platform category, social network category). Each primary category corresponds to a core feature identifier. Secondary subcategories are then defined within each primary category, further subdivided based on more specific scenario features. For example, under the static target category, subcategories are defined based on whether or not form interaction is present (subcategories with form interaction and subcategories without form interaction); under the simple anti-scraping category, subcategories are defined based on anti-scraping... The crawling type is divided into subcategories (CAPTCHA anti-crawling subcategory, frequency limit anti-crawling subcategory, request header verification anti-crawling subcategory). Each second-level subcategory also corresponds to a unique sub-feature identifier. Index entries are created for each first-level category and second-level subcategory. The index entries include a category identifier, a core feature description, a list of all experience IDs under that category, and a data storage address. The data storage address explicitly points to the physical storage path of all experience data under that category in the experience database. A hierarchical relationship is constructed by linking the first-level category index with the indexes of its subordinate second-level subcategories. That is, all the second-level subcategory indexes contained in the first-level category index can be quickly queried through the first-level category index, forming a three-level index structure of first-level category - second-level subcategory - experience data.

[0043] A hierarchical storage management system is implemented, allocating storage paths according to the index structure. Based on the storage addresses of the hierarchical semantic index, corresponding folder directory structures are created in the physical storage media of the experience database, strictly adhering to the hierarchical relationship of first-level main category directory - second-level subcategory directory - experience data file. Each experience data file is named with an experience ID and contains the corresponding task scenario semantic vector, collection strategy semantic vector, and recorded success rate indicators. An address mapping table is established, and a mapping table between index identifiers and storage paths is created, recording the physical storage path corresponding to each category identifier. When searching through the index, the corresponding storage directory can be directly located through the mapping table without traversing the entire experience database. To maintain data correlation, an association list is created under each storage directory, recording all experience IDs and their corresponding core feature summaries in that directory, facilitating rapid filtering of experience data that meets the criteria.

[0044] Step 222: Convert the structured target profile of the current task into a query semantic vector; using the query semantic vector as input, perform semantic similarity retrieval in the experience base using a hierarchical semantic index, match and return the K prior experience triples with the highest similarity to the current task scenario; specifically, this includes: generating the query semantic vector of the current task, decomposing the structured target profile of the current task, and extracting specific information of the current task target one by one according to three dimensions: static attribute features, time-series dynamic behavior features, and contextual association features. For example, static attribute features include dynamic e-commerce platform, access point, login form and shopping cart interaction; time-series dynamic behavior features include updating product inventory every 30 minutes, response latency of 2-4 seconds, and doubled update frequency on holidays; contextual association features include e-commerce domain, related topics, etc. The system considers payment platforms and peak access times between 8-10 PM. Values ​​are converted according to unified encoding rules, strictly adhering to the encoding logic of the task scenario semantic vector in step 220. Each feature item of the current task is converted numerically: dynamic e-commerce platforms correspond to value 2; login forms and shopping cart interactions correspond to value 3; values ​​updated every 30 minutes correspond to value 30; response delays of 2-4 seconds correspond to value 2; and the update frequency doubles on holidays corresponds to value 5; e-commerce corresponds to value 4; associated payment platforms correspond to value 2; and peak evening access corresponds to value 2. These values ​​are then arranged to form a query semantic vector. All values ​​corresponding to the feature items are arranged in the fixed feature item order of step 220, forming a query semantic vector with the same length and format as the historical task scenario semantic vector, ensuring compatibility during retrieval.

[0045] Using a hierarchical index, the retrieval scope is filtered. First-level categories are matched, and the core features of the query semantic vector are compared with the feature identifiers of all first-level categories. The similarity between the query vector and the core feature vector of each first-level category is calculated, and 1-2 first-level categories with an average similarity of ≤3 are selected as candidate categories (e.g., dynamic target category, e-commerce platform category). Second-level subcategories are matched. Under the candidate first-level categories, the vectors corresponding to the detailed feature identifiers of each second-level subcategory are extracted, and their similarity is calculated with the query semantic vector. 2-3 second-level subcategories with an average similarity of ≤2 are selected as target subcategories. The storage directory is locked, and the physical storage path corresponding to the target subcategories is obtained through the address mapping table of the hierarchical index. The final retrieval scope is determined to be all experience data under these target subcategory storage directories. The semantic similarity between the query vector and the experience vectors of the target subcategories is calculated, and the task scenario semantic vectors of all experience data under the target subcategories are extracted. Each data is retrieved one by one according to the association list under the storage directory. For each experience ID, a semantic vector representing a task scenario is generated. This ensures no experience data within the target subclass is missed. Semantic similarity is calculated for each retrieved historical task scenario semantic vector and the current task's query semantic vector. First, the absolute difference between the corresponding values ​​of the two vectors is calculated. Then, all absolute differences are summed and divided by the vector length (i.e., the number of feature terms) to obtain the average difference. Semantic similarity = 1 - (average difference ÷ maximum possible average difference), where the maximum possible average difference is the average of the absolute differences between the maximum and minimum possible values ​​of each feature term. For example, if the feature term value ranges from 1 to 10, the maximum difference is 9, and the vector length is 10, then the maximum possible average difference is 9. The semantic similarity value ranges from 0 to 1; the closer the value is to 1, the higher the similarity between the two scenarios. The similarity results are recorded, and each historical experience data is labeled with its corresponding semantic similarity value, forming a list of experience IDs and their corresponding semantic similarities.

[0046] Filter and return Top-K similar experience triples, sort the similarity list, and sort the experience ID-semantic similarity list from high to low semantic similarity value to ensure that the experience data with the highest similarity is placed first. Determine the K value and filter. The preset K value is such as K=5 or K=8, which can be adjusted according to the amount of data in the experience database. Select the top K experience IDs from the sorted list. If the total amount of experience data in the target subclass is less than K, select all experience data. Extract complete experience triples. Based on the selected K experience IDs, retrieve the corresponding target features, collection strategies, and execution effects from the experience database to form complete K prior experience triples. At the same time, attach the semantic similarity value and the recorded success rate index of each triple, and return them to the strategy generation stage.

[0047] Step 223: For the K prior experience triples obtained from the retrieval and matching, extract the semantic similarity between the task scenario semantic vector and the current query semantic vector in each experience triple as a weight, and obtain the recorded success rate index associated with each experience triple; based on the semantic similarity weight and existing efficiency performance, perform weighted fusion on the collection strategies recorded in the K experience triples to generate an adaptive collection strategy suitable for the current task; specifically, this includes: determining the weight and success rate index of each prior experience triple, extracting the semantic similarity weight, directly using the semantic similarity values ​​of the K prior experience triples calculated in step 222 as the semantic similarity weight of each triple, such as if the semantic similarity of a triple is 0.85, then its weight is 0.85, ensuring that the weight is directly linked to the scene similarity, extracting the recorded success rate index, and retrieving the corresponding recorded success rate index one by one from the vectorized experience data associated with the K prior experience triples. Power metrics, such as the success rate of a certain triplet being 78% and another being 85%, represent the existing efficiency performance data for each empirical triplet. Normalized weights (optional): If a unified weight range is required, the K semantic similarity weights are normalized. The normalized value of each weight = that weight ÷ the sum of all weights, ensuring the sum of normalized weights is 1. For example, if three weights are 0.8, 0.7, and 0.9, the sum is 2.4, and after normalization, they are 0.33, 0.29, and 0.38 respectively. Subsequent calculations use the normalized weights. The collection strategy parameters are decomposed and weighted sequentially. Decomposing the strategy parameters involves breaking down the collection strategies in the K prior empirical triplets into independent parameters according to a unified dimension, ensuring consistency in the parameter decomposition dimension for all triplets. Specific parameters include access frequency, request header parameters, proxy selection rules, parsing rules, number of retries, backoff time (first, second, third), blocking response method, collection depth, and concurrency.

[0048] The quantified parameters are weighted and fused (taking access frequency as an example). The access frequency parameter value of each triple is extracted, such as triple 1 once every 10 seconds, triple 2 once every 8 seconds, and triple 3 once every 12 seconds. The weighted value of this parameter for each triple is calculated as follows: Weighted value = parameter value × semantic similarity weight × recorded success rate index. For example, triple 1, 10 × 0.85 × 0.78 = 6.63; triple 2, 8 × 0.9 × 0.85 = 6.12; triple 3, 12 × 0.8 × 0.82 = 7.87. The total weighted value of the parameter is calculated by summing the weighted values ​​of this parameter for K triples. For example, 6.63 + 6.12 + 7.87 = 20.62; determine the final value of the parameter by dividing the total weighted value by K, such as 20.62 ÷ 3 ≈ 6.87, and take the closest reasonable value as the final value, such as once every 7 seconds. If the parameter is an integer, such as the number of retries, round it to the nearest integer. Perform weighted fusion on the enumerated parameters (taking the agent selection rule as an example), and extract the agent selection rule parameter value and corresponding value for each triple. For example, triple 1 is fixed agent pool rotation, corresponding to the value 3; triple 2 is agent allocation by region, corresponding to the value 4; triple 3 is fixed agent pool rotation, corresponding to the value 3.

[0049] Calculate the weighted score for each parameter value: Weighted Score = Parameter Value × Semantic Similarity Weight × Recorded Success Rate Index. Summate the weighted scores for parameters with the same value. For example, the weighted score for fixed proxy pool rotation = 3 × 0.85 × 0.78 + 3 × 0.8 × 0.82 = 2.0 + 1.97 = 3.97; the weighted score for regional proxy allocation = 4 × 0.9 × 0.85 = 3.06. Determine the final parameter value by selecting the parameter value with the highest weighted score. For example, the weighted score of 3.97 for fixed proxy pool rotation is higher than... 3.06, therefore the final value is a fixed proxy pool rotation; weighted fusion of action parameters (taking blocking response as an example), extracting the blocking response method and corresponding value for each triple, such as triple 1 for switching proxies, corresponding to value 2; triple 2 for reducing frequency, corresponding to value 4; triple 3 for switching proxies + pausing for 1 minute, corresponding to value 5; calculate the weighted score for each response method, weighted score = parameter value × semantic similarity weight × recorded success rate index, such as switching proxies 2 × 0.85 × 0.78 = 1.33; reducing frequency 4 × 0.85 × 0.78 = 1.33; 0.9 × 0.85 = 3.06; Switching agents + pausing for 1 minute: 5 × 0.8 × 0.82 = 3.28; Determine the final parameter values, select the response method with the highest weighted score. If multiple methods have similar scores (difference ≤ 0.3), combine them to form a composite response method. Integrate these to form an adaptive data collection strategy. Summarize the final values ​​of all parameters, and organize the final values ​​of quantified parameters, enumerated parameters, and action parameters according to the logical structure of the data collection strategy. Ensure that the value of each parameter is clear and conflict-free. Supplement the logical relationship explanation of the parameters and determine the parameters. The execution order and triggering conditions are defined. For example, when encountering frequency limitation blocking, the proxy node is switched first, and the access frequency is adjusted from once every 7 seconds to once every 10 seconds. At the same time, a secondary backoff (5 seconds) is initiated. If the number of retries reaches 3 and still fails, the blocking response mode is triggered, and the collection is paused for 1 minute before re-execution. A complete strategy document is formed, integrating parameter values ​​and logical association descriptions into a complete adaptive collection strategy to ensure that the strategy can be directly used for the execution of the current task, and the value of each parameter can be traced back to the corresponding historical experience triplet and weighted calculation process.

[0050] Standardized vectorization encoding of experience triples ensures the consistency and comparability of experience data for different tasks through clear textual description decomposition, unified numerical mapping rules, and fixed vector structure. At the same time, the encoded vectors are associated with the calculated and recorded success rate indicators, so that the experience data not only includes the execution process, but also includes effect evaluation, thereby improving the reliability of experience reuse.

[0051] In a preferred embodiment of the present invention, step 3 above, which decomposes the task into multiple sub-tasks according to an adaptive acquisition strategy, and before distributing them to physical execution nodes, assesses the actuator bearing capacity of sub-tasks involving torsional loads, calculates the maximum tolerable torque threshold under the current working condition, and compares it with the drive load required by the task; if it exceeds the safe range, the acquisition action intensity is automatically adjusted, the operating frequency is reduced, and the task is assigned to a node with higher bearing capacity; after verification, the task status is serialized and persistently stored, and compliant sub-tasks are asynchronously distributed to each acquisition node for execution, which may include:

[0052] In this embodiment of the invention, step 330 involves dividing the total task to be executed into a series of logically independent and sequentially or in parallel subtasks based on the action sequence and resource requirements planned in the adaptive acquisition strategy. Specifically, this includes: first, comprehensively decomposing the action sequence specified in the adaptive acquisition strategy. This sequence needs to be refined into specific, actionable types, including but not limited to target access operations, content crawling operations, data parsing operations, and result feedback operations. Each operation needs to clearly define its execution purpose and core output. At the same time, extracting the complete resource requirements of the total task, specifically covering computing resource requirements, network bandwidth requirements, time window requirements, and device adaptation requirements. Next, formulating subtask splitting rules and executing the splitting, and splitting the subtasks in parallel. Actions with the same operation type, independently allocable resource requirements, and no dependency relationship are split into parallel subtasks. Each parallel subtask must have a clearly defined execution scope, independent input and output, and the execution result of any parallel subtask must not affect the start and progress of other parallel subtasks. Furthermore, the combined output of all parallel subtasks must cover all requirements of the corresponding action type. For actions with clear sequential dependencies, they are broken down into sequential subtasks according to the logical order of action execution. The dependencies between preceding and subsequent subtasks must be clearly defined to ensure that subsequent subtasks can only start execution after the preceding subtasks have been fully executed and their outputs are compliant. Finally, a standardized set of subtasks is generated, assigning a unique identifier to each subtask, clearly indicating its execution method (parallel / sequential), corresponding action details, required resource allocation, expected execution time, and output format requirements. This forms a structured task breakdown list, ensuring that all subtasks can collaboratively cover the overall task's data collection objectives and constraints.

[0053] Step 331: For the set of subtasks, identify specific subtasks that require output torque to complete the acquisition action, and extract the drive load parameters required to execute the subtask based on the action instructions of the specific subtasks, including the required peak torque value and continuous torque value. Specifically, this includes: First, establishing specific subtask identification criteria, analyzing the acquisition action implementation method of each subtask in the subtask set one by one. If the acquisition action of the subtask needs to be completed by mechanical structure drive, and the torque needs to be generated by overcoming external resistance or its own inertia during the completion of the action, it is determined to be a specific subtask that requires output torque. Subtasks that can be completed by software programs alone are excluded from the specific subtasks. Then, extract the drive load parameters according to the following specific logic, and calculate and extract the peak torque value. The first step is to determine the basic action parameters, based on the specific subtask's... The first step is to define the motion command, specifying the range of motion and the resistance to be overcome. The second step is to measure the lever arm length of the driving component, which is the straight-line distance from the point of force application to the center of rotation. The third step is to calculate the base torque: Base torque = Total resistance to be overcome × Lever arm length, where the total resistance to be overcome = Reaction force of the target object + Friction force of the mechanical structure itself. The fourth step is to calculate the inertia coefficient, determined by the starting acceleration. The starting acceleration is calculated based on the time it takes for the motion to reach its maximum speed from a standstill. If the time is ≤0.5 seconds, the starting acceleration is large, and the inertia coefficient is 1.5; if the time is between 0.5 and 1 second, the inertia coefficient is 1.3; if the time is >1 second, the starting acceleration is small, and the inertia coefficient is 1.1. The fifth step is to determine the peak torque value: Peak torque = Base torque × Inertia coefficient.

[0054] The calculation and extraction of continuous torque value involves three steps: First, using the base torque calculated in the previous step as the benchmark value, determine the load fluctuation coefficient. If the action requires frequent starts and stops during execution, the load fluctuation coefficient is set to 1.2; if the action is executed continuously at a constant speed, the load fluctuation coefficient is set to 1.0; if the load fluctuates slightly during the action, the load fluctuation coefficient is set to 1.1. Second, calculate the continuous torque value: Continuous torque value = base torque × load fluctuation coefficient. Third, simultaneously record the duration of continuous action execution to ensure that the continuous torque value matches the execution duration, providing a complete basis for load assessment.

[0055] Step 332: Based on the resource mapping relationship of the adaptive acquisition strategy, determine the physical execution nodes to be allocated to specific subtasks, and obtain the mechanical physical parameters and current real-time operating status parameters of the target execution nodes. Specifically, this includes: First, analyzing the resource mapping relationship in the adaptive acquisition strategy, which is a preset bidirectional correspondence rule between subtask type and node capability level. First, the specific subtasks are divided into three subtask types: low load, medium load, and high load, according to the required peak torque value. Then, all physical execution nodes are divided into three capability levels: basic, advanced, and flagship, according to their rated load capacity. Basic nodes are adapted to low load subtasks, advanced nodes are adapted to medium load subtasks, and flagship nodes are adapted to high load subtasks, thus clarifying the node capability level range corresponding to different types of subtasks.

[0056] Next, the target execution node for the planned allocation is determined. First, based on the type of specific subtask (low / medium / high load), a set of candidate nodes with corresponding capability levels is selected from all physical execution nodes. Second, the current load occupancy rate of each candidate node is calculated: Load occupancy rate = (Total peak torque demand of tasks already allocated to the node) ÷ (Node's rated maximum torque) × 100%. Third, the candidate node with the lowest load occupancy rate is selected as the target execution node for the planned allocation. If multiple candidate nodes have the same load occupancy rate, the node with the shortest cumulative runtime is selected. Then, the mechanical physical parameters of the target execution node are obtained. Through the target execution node's equipment file management system, the inherent parameters of its core mechanical structure are retrieved. The system includes the rated torque, safety factor, material strength limit, and transmission efficiency of the transmission mechanism. Finally, it acquires the current real-time operating status parameters of the target execution node. Data is collected in real time by multi-dimensional sensors deployed on the target execution node, specifically including the wear status of the mechanism (the wear rate is calculated by ÷ cumulative running time ÷ design life × 100%), and the number of past overloads is recorded simultaneously; ambient temperature (the real-time temperature of the node's operating environment is read by a temperature sensor, recording whether the temperature is within the equipment's standard operating temperature range (20℃-35℃); lubrication status (the remaining amount is read by a lubricant level sensor, and the current viscosity grade of the lubricant is read by a viscosity sensor); and power supply stability (the voltage fluctuation value within the past minute is read by a voltage sensor, such as ±3%).

[0057] Step 333: Based on the physical parameters of the mechanism and the current real-time operating condition parameters, a comprehensive analysis is performed using preset torque bearing capacity calculation rules to evaluate and calculate the dynamic maximum tolerable torque threshold of the target execution node under the current specific operating conditions. Specifically, this includes: First, calculating the theoretical bearing torque of the node, using the rated torque of the transmission mechanism in the physical parameters of the mechanism as the core basic value, multiplying it by a safety factor and transmission efficiency to obtain the theoretical bearing torque, i.e., theoretical bearing torque = rated torque of the transmission mechanism × safety factor × transmission efficiency. This value is the maximum safe torque of the node under standard ideal operating conditions. Next, based on the current real-time operating condition parameters, correction coefficients are calculated in different dimensions. All correction coefficients are within the range of 0.7-1.0 to ensure that the node's bearing capacity is not excessively reduced. An ambient temperature correction coefficient is preset. The standard operating temperature range is 20℃-35℃. If the current ambient temperature is within this range, the temperature correction factor is 1.0. If the current ambient temperature is higher than 35℃, the temperature correction factor decreases by 0.02 for every 1℃ above 35℃. The calculation formula is: Temperature Correction Factor = 1.0 - (Current Ambient Temperature - 35℃) × 0.02 (e.g., if the current temperature is 38℃, then the temperature correction factor = 1.0 - (38-35) × 0.02 = 0.94). If the current ambient temperature is lower than 20℃, the temperature correction factor decreases by 0.01 for every 1℃ below 20℃. The calculation formula is: Temperature Correction Factor = 1.0 - (20℃ - Current Ambient Temperature) × 0.01. e.g., if the current temperature is 15℃, then the temperature correction factor = 1.0 - (20-15) × 0.01 = 0.95. The minimum temperature correction factor is 0.8.

[0058] The wear correction factor is calculated based on the wear rate. For a wear rate ≤10%, the wear correction factor is 0.95; for a wear rate between 10% and 20%, it is 0.90; for a wear rate between 20% and 30%, it is 0.85; for a wear rate between 30% and 40%, it is 0.80; and for a wear rate >40%, it is 0.75. If the number of past overloads is ≥5, the factor is reduced by 0.05 from the above calculation. For example, a wear rate of 25% corresponds to a correction factor of 0.85, and with 6 past overloads, the final wear correction factor = 0.85 - 0.05 = 0.80. The lubrication condition correction factor is 1.0 if the remaining lubricant level is ≥80% and the viscosity grade meets equipment requirements (deviation ≤±5%). If the remaining lubricant level is between 50% and 80%, or the viscosity grade is below the required level, the correction factor is 0.95. For a deviation of 5%-10%, the lubrication correction factor is 0.92; for a remaining lubricant level of 30%-50% or a viscosity grade deviation of 10%-15%, the lubrication correction factor is 0.85; for a remaining lubricant level of <30% or a viscosity grade deviation of >15%, the lubrication correction factor is 0.78. For power supply stability correction factors, if the voltage fluctuation within the past minute is ≤±3%, the power supply correction factor is 1.0; if the voltage fluctuation is between ±3% and ±5%, the power supply correction factor is 0.95; if the voltage fluctuation is >±5%, the power supply correction factor is 0.90. Finally, the dynamic maximum tolerable torque threshold is calculated as follows: Dynamic maximum tolerable torque threshold = Theoretical bearing torque × Temperature correction factor × Mechanism wear correction factor × Lubrication condition correction factor × Power supply stability correction factor. After calculation, the basis for each correction factor is recorded synchronously to form a complete calculation log.

[0059] Step 334 involves a safety comparison of the drive load parameters required for a specific sub-task with the dynamic maximum tolerable torque threshold to determine whether the load requirement is within the safe bearing range of the mechanism. Specifically, this includes: First, determining the comparison benchmark and allowable error range. Considering the potential minor errors during parameter measurement, the allowable error range for the safety comparison is set to ±2%, meaning the actual effective range of the dynamic maximum tolerable torque threshold is from the dynamic maximum tolerable torque threshold × 0.98 (lower limit) to the dynamic maximum tolerable torque threshold × 1.02 (upper limit). The lower limit is the strict standard for safe bearing, and the upper limit is the tolerance standard for temporary peak values. Next, the comparison of load parameters and thresholds is performed independently twice, along with a peak torque value comparison. The peak torque value for the specific sub-task is extracted and compared with the upper limit of the dynamic maximum tolerable torque threshold. The process involves two steps: First, determining if the peak torque value is ≤ 1.02 of the dynamic maximum tolerable torque threshold. Second, comparing the continuous torque value, extracting the continuous torque value of a specific subtask, and comparing it with the lower limit of the dynamic maximum tolerable torque threshold to determine if the continuous torque value is ≤ 0.98 of the dynamic maximum tolerable torque threshold. Finally, based on the combined results of the two comparisons, if both comparisons meet the ≤ condition, the load requirement of the specific subtask is determined to be within the safe bearing range of the mechanism, and the node can be allocated and executed as originally planned. If the peak torque value comparison fails (peak torque value > dynamic maximum tolerable torque threshold × 1.02) or the continuous torque value comparison fails (continuous torque value > dynamic maximum tolerable torque threshold × 0.98), the load requirement is determined to exceed the safe bearing range of the mechanism, and the parameter adjustment and node reallocation process needs to be initiated.

[0060] Step 335: When the real-time required drive load exceeds the maximum tolerable torque threshold, based on the ratio of the real-time drive load to the maximum tolerable torque threshold, dynamically calculate the reduction ratio of the acquired action intensity parameters and execution frequency parameters, and generate adjusted intensity parameters and execution frequency parameters according to the reduction ratio. Specifically, this includes: First, determining the core reference parameter. Comparing the load parameters (peak torque value or continuous torque value) that failed in step 334 with the dynamic maximum tolerable torque threshold, the parameter with the larger excess ratio is taken as the core reference parameter. For example, if the peak torque value exceeds the threshold by 15% and the continuous torque value exceeds the threshold by 8%, then the peak torque value is taken as the core reference parameter. Next, calculating the ratio of the core reference parameter to the dynamic maximum tolerable torque threshold: Ratio = Core Reference Parameter ÷ Dynamic Maximum Tolerable Torque Threshold. Then, calculating the reduction ratio of the acquired action intensity parameter according to segmented logic. When the ratio ≤ 1.2, the reduction ratio = (Ratio - 1.0) × 0.5. For example, if the ratio is 1.136, the reduction ratio = (1.136 - 1.0) × 0.5 = 0.068, or 6.8%; when 1.2 < ratio ≤ 1.5, the reduction ratio = 0.1 + (ratio - 1.2) × 0.7, such as when the ratio is 1.3, the reduction ratio = 0.1 + (1.3 - 1.2) × 0.7 = 0.17, or 17%; when the ratio > 1.5, the reduction ratio = 0.31 + (ratio - 1.5) × 0.8, such as when the ratio is 1.6, the reduction ratio = 0.31 + (ratio - 1.5) × 0.8. Example: 0.31 + (1.6 - 1.5) × 0.8 = 0.39, or 39%. The upper limit of the reduction ratio is 80%, and the lower limit is 10%. If the calculated result exceeds this range, the corresponding boundary value is taken. For example, if the ratio is 2.0, the calculated reduction ratio is 0.31 + (2.0 - 1.5) × 0.8 = 0.71, or 71%, which does not exceed the upper limit, so the actual value is taken. If the ratio is 3.0, the calculated result is 0.31 + 1.5 × 0.8 = 1.51, which exceeds the upper limit, so 80% is taken.

[0061] Next, calculate the reduction ratio of the execution frequency parameter according to the segmented logic. When the ratio is ≤1.2, the reduction ratio = (ratio - 1.0) × 0.3. For example, if the ratio is 1.136, the reduction ratio = (1.136 - 1.0) × 0.3 = 0.0408, or 4.08%. When 1.2 < ratio ≤1.5, the reduction ratio = 0.06 + (ratio - 1.2) × 0.4. For example, if the ratio is 1.3, the reduction ratio = 0.06 + (1.3 - 1.2) × 0.4 = 0.10, or 10%. When the ratio is >1.5, the reduction ratio = 0.18 + (ratio - 1.5) × 0.5. For example, if the ratio is 1.6, the reduction ratio = 0.18 + (1.6 - 1.5) × 0.5 = 0.23, or 23%. The upper limit of the reduction ratio is 60%, and the lower limit is 5%. Exceeding this limit will result in a reduction of 5%. The range is determined by boundary values. For example, a ratio of 3.0 results in 0.18 + 1.5 × 0.5 = 0.93, which exceeds the upper limit, so 60% is used. Finally, the adjusted parameters are generated. The adjusted acquisition action intensity parameter is calculated as: original acquisition action intensity parameter × (1 - reduction ratio of acquisition action intensity parameter). The original acquisition action intensity parameter is the quantized value initially set in the adaptive acquisition strategy. The adjusted parameter retains two decimal places. This parameter directly determines the intensity of the action execution. Reducing the parameter can decrease torque requirements. The adjusted execution frequency parameter is calculated as: original execution frequency parameter × (1 - reduction ratio of execution frequency parameter). The original execution frequency parameter is the number of times the action is executed per unit time initially set. The adjusted parameter is rounded to an integer. By reducing the frequency of actions per unit time, the continuous load pressure is reduced.

[0062] Step 336: Using the adjusted intensity parameters and execution frequency parameters as input, calculate the new requirements of the acquisition task on the physical node's carrying capacity; based on the new requirements, dynamically select physical execution nodes with matching carrying capacity from the node resource pool, and complete the reassignment and binding of the selected nodes with the sub-tasks to be executed; specifically including: first, calculating the new carrying capacity requirements, calculating the new peak torque requirements, new peak torque requirements = adjusted acquisition action intensity parameters × base torque calculated in step 331 ÷ original acquisition action intensity parameters; if the sub-task contains multiple continuous actions, take the maximum value of the new peak torque of a single action as the total new peak torque requirements; calculate the new continuous torque... The new continuous torque requirement is calculated as follows: New continuous torque requirement = Adjusted execution frequency parameter × Original continuous torque value extracted in step 331 ÷ Original execution frequency parameter. If the continuous execution time of the action is long, the average continuous torque is taken as the total new continuous torque requirement. The new load-bearing capacity requirement is the combination of the new peak torque requirement ≤ node dynamic maximum tolerable torque threshold × 1.02 and the new continuous torque requirement ≤ node dynamic maximum tolerable torque threshold × 0.98. Next, the matching physical execution nodes are dynamically filtered. The first step is to update the node resource pool status and retrieve all available physical execution nodes from the node resource pool (excluding faulty, offline, and load occupancy ≥ 80% nodes). Following the calculation logic in step 333, the current dynamic maximum tolerable torque threshold for each available node is calculated in real time. The second step involves preliminary screening, selecting nodes whose dynamic maximum tolerable torque threshold × 1.02 ≥ the new peak torque requirement and whose dynamic maximum tolerable torque threshold × 0.98 ≥ the new continuous torque requirement, forming a candidate node pool. The third step involves secondary screening; if there are multiple nodes in the candidate node pool, the comprehensive adaptability of each node is calculated. Comprehensive adaptability = (1 - current node load occupancy) × 0.6 + (reciprocal of the node's historical failure rate) × 0.4, where the node's historical failure rate = number of past failures ÷ cumulative node runtime × 100%. If the node load is 20% and the historical failure rate is 0.5%, then the overall adaptability is calculated as (1-0.2)×0.6+(1÷0.005)×0.4=0.48+80=80.48. The node with the highest overall adaptability is selected as the final matching node. The fourth step is node availability confirmation. An availability probe request is sent to the final matching node to confirm the node's current network connectivity, software adaptability, and remaining runtime (which must be ≥ the expected execution time after subtask adjustment × 1.5). If the node responds that it is available, the binding process begins. If it is not available, the node with the second highest overall adaptability is selected from the candidate node pool for reconfirmation until a usable node is found.

[0063] Finally, the reallocation and binding are completed. The first step is to update the task allocation information, associating the specific subtask's identifier, adjusted collection action intensity parameters, adjusted execution frequency parameters, new peak torque requirement, new continuous torque requirement, and the identifier of the final matching node, as well as the current dynamic maximum tolerable torque threshold, with the global task allocation list updated to clarify task ownership. The second step is to establish a binding relationship, bidirectionally entering binding information in the subtask management system and node management system. The subtask system marks it as assigned to node XX, and the node system marks it as received from subtask XX, forming a unique binding ID. The third step is to send a pre-allocation notification to the final matching node, containing the subtask's core information and the binding ID. The node must provide confirmation within 10 seconds of receiving the notification. If no confirmation is received, the notification is resent. After three consecutive unsuccessful attempts, the binding is removed, and nodes are re-selected.

[0064] Step 337: Perform final compliance verification of the node allocation scheme and parameter configuration for all subtasks that have completed redistribution and binding; serialize and convert the complete task status information for compliance verification; specifically, this includes: First, establishing compliance verification standards, conducting full-dimensional verification for each subtask that has completed redistribution and binding, including node allocation scheme compliance verification, load matching verification, recalculating the current dynamic maximum tolerable torque threshold of the finally matched node, verifying that the dynamic maximum tolerable torque threshold × 1.02 ≥ the new peak torque requirement and the dynamic maximum tolerable torque threshold × 0.98 ≥ the new continuous torque requirement, ensuring no deviation in load matching; node capability verification, checking whether the node's hardware configuration is suitable for the subtask's data collection action type; and runtime condition verification, checking the node's network connectivity (ping test latency ≤ 50 milliseconds), power stability (voltage fluctuation ≤ ±3% in the last 5 minutes), and remaining runtime (≥ the expected execution time after subtask adjustment × 1.5). If all conditions are met, the node allocation scheme is considered compliant.

[0065] Parameter configuration compliance verification includes: intensity parameter compliance (checking whether the adjusted intensity parameter of the collection action is within the allowed intensity range of the subtask to avoid insufficient intensity leading to substandard collection); frequency parameter compliance (checking whether the adjusted execution frequency parameter meets the time constraint of the total task, i.e., the expected execution time of the subtask after adjustment × the number of subtasks ≤ the remaining time of the total task; for example, if the remaining time of the total task is 1 hour, the expected execution time of the subtask after adjustment is 5 minutes, and there are 8 subtasks, 5 × 8 = 40 minutes ≤ 60 minutes is compliant); and parameter logic compliance (checking whether there are logical conflicts between the adjusted intensity parameter and frequency parameter to ensure that the parameter combination is reasonable). Verification result processing: if all verification items pass, mark the subtask as compliant and executable, generate a verification pass log, and record the verification time, verification personnel, and various verification results.

[0066] If any verification fails, it is marked as non-compliant, and the failed item (such as insufficient remaining runtime of the node) is recorded. The process returns to step 335 to recalculate the reduction ratio and re-execute the node allocation process until the verification passes. Subtasks that fail verification a total of 3 times are reported to the task management center for manual intervention and adjustment. Then, complete task status information is collected, including basic subtask information such as subtask ID, total task association ID, subtask type (parallel / sequential), and execution priority (high / medium / low); allocation and binding information such as matching node ID, node capability level, binding ID, node current load occupancy rate, and node dynamic maximum tolerable torque threshold; parameter configuration information such as adjusted collection action intensity parameters, adjusted execution frequency parameters, new peak torque requirement, new continuous torque requirement, and action continuous execution duration; verification information such as verification status (compliant and executable), verification time, failed item record (none), and verification log ID; and dependency information such as the ID of the preceding dependent subtask and the completion status of the preceding subtask if it is a sequential subtask; and the parallel group ID and the number of subtasks in the same group if it is a parallel subtask.

[0067] Finally, serialization is performed. The first step is to determine the serialization format, using JSON for structured conversion, defining fixed field names to ensure uniqueness and clear semantics. The second step is data formatting, filling in the collected complete task status information according to the JSON field requirements. Numerical data is retained to two decimal places, string data uses UTF-8 encoding, boolean data is represented by true / false, and array data is arranged sequentially. The third step is to generate a checksum, performing SHA-256 hash calculation on the serialized JSON string to generate a data integrity checksum, which is appended to the end of the JSON string, forming a complete serialized result of JSON data + checksum. The fourth step is to verify the conversion result, deserializing the serialized result back to the original information and comparing it item by item with the collected complete task status information to ensure no data loss, tampering, or format errors. If the comparison matches, the serialization conversion is complete, and a conversion log is recorded.

[0068] Step 338: Write the converted task status information to a non-volatile storage medium to complete persistent storage. Asynchronously distribute each compliant subtask and its corresponding configuration parameters that have completed persistent storage to the bound physical acquisition nodes, and send execution commands to the nodes to trigger the execution of the acquisition tasks. Specifically, this includes: First, completing persistent storage: Step 1, selecting the storage medium and path, using an NVMe solid-state drive as the non-volatile storage medium (read / write speed ≥1GB / s to ensure storage efficiency), and setting the storage path according to the hierarchical directory structure of the overall task identifier ID / subtask identifier ID / status information / for easy retrieval by task dimension; Step 2, writing to the storage medium, using the JSO generated in step 337... The complete serialization result of N data + checksum is written to a file in the specified path in binary format, with the filename naming rule being taskstate timestamp.json; the third step is data integrity verification. After writing, the storage file is read, the SHA-256 checksum is recalculated, and compared with the checksum at the time of writing. If they match, the writing is confirmed to be successful; if they do not match, the erroneous file is deleted, and the writing is rewritten. If three writing failures occur, the system switches to a backup storage medium and reports a storage anomaly alarm; the fourth step is to record storage logs. Storage details are recorded in the storage management system, including file path, filename, file size, write time, checksum, and storage medium identifier, forming a traceable storage record.

[0069] Next, asynchronous distribution is executed. The first step is to prepare the distribution data by reading the serialized results of the corresponding subtasks from persistent storage, extracting core information such as configuration parameters, subtask identifier ID, binding ID, and execution priority, and organizing them into distribution data packets according to the node's required format. The second step is to select a distribution protocol, using HTTP / 2 for asynchronous distribution, supporting multiplexing to avoid distribution blocking, and enabling TLS encrypted transmission to ensure data security. The third step is to initiate a distribution request by sending a distribution request to the specified port of the bound physical acquisition node. The request header includes the binding ID and data length, and the request body is the distribution data packet, with a timeout of 30 seconds. The fourth step is to process distribution feedback. If the node returns a successful reception response within the timeout period, the subtask is marked as successfully distributed. If it returns a reception failure or no response, the distribution is re-initiated after a 5-second interval. After three distribution failures, the binding with the node is removed, and the process returns to step 336 to re-select nodes and distribute. The fifth step is to record the distribution log, recording the distribution time, target node, number of distributions, feedback results, and data packet size for each subtask, forming a distribution ledger.

[0070] Finally, the execution command is sent to trigger task execution. The first step is to generate the execution command, which includes the subtask identifier ID, binding ID, start timestamp (for parallel subtasks, the start timestamp is the current time + 10 seconds; for sequential subtasks, the start timestamp is the completion time of the preceding dependent subtask + 5 seconds), execution deadline (start timestamp + expected execution time after subtask adjustment × 1.2), and an exception feedback threshold (immediate feedback is required if three consecutive actions fail). The second step is to send the execution command to the bound node through the node management channel, using a push + confirmation mechanism. Upon receiving the command, the node returns a confirmation message indicating that the command has been received. The third step is to trigger execution. When the start timestamp arrives, the node loads the distributed configuration parameters, initializes the data collection process, starts the sensors to monitor the execution status in real time, and formally triggers the subtask execution. The fourth step is to record the execution start log, recording the execution command sending time, start timestamp, and execution deadline to ensure traceability of task execution.

[0071] Standardized serialization conversion and non-volatile storage ensure the integrity and security of task status information, improve the efficiency and reliability of subtask configuration parameter distribution, and avoid the distribution process from dragging down the overall task progress. Meanwhile, multi-dimensional compliance verification comprehensively investigates potential risks in node allocation and parameter configuration, reducing the probability of execution failure due to configuration errors or improper node adaptation.

[0072] In a preferred embodiment of the present invention, step 4 above, which dynamically updates the task status and strategy parameters based on the event-driven task scheduling reported by the collection nodes and periodically generates task checkpoints, may include:

[0073] In this embodiment of the invention, step 440 involves continuously receiving execution event data streams actively reported by each physical acquisition node to obtain a raw event set including task progress updates, task status changes, and runtime anomaly alarms. Specifically, this includes: First, establishing a stable event receiving channel, using a TCP long connection protocol to establish a dedicated communication link with each physical acquisition node, configuring an independent receiving buffer for each link (the buffer size is calculated based on the node's maximum reporting frequency × 30 seconds to ensure no loss of sudden reporting data), simultaneously monitoring the node's designated reporting port, enabling a continuous 24 / 7 receiving mode with no interruption intervals; Second, determining the node event reporting trigger rules: Task progress update events are reported at fixed intervals, or in real-time when the completion rate of subtasks reaches key milestones such as 10%, 20%, ... 100%; Task status change events are reported immediately when the subtask status changes from any state such as pending execution, executing, abnormally paused, failed, or completed, with a reporting delay of no more than 1 second; Runtime anomaly alarm events are reported in real-time when a node detects abnormal conditions such as network interruption, data parsing failure, equipment failure, or overload, and the report is not delayed beyond 1 second. After an event occurs, it must be reported within 500 milliseconds. Then, define a standard structure for the event data to ensure the completeness of the reported information. Task progress update events include subtask ID, total task association ID, current completion percentage, amount of valid data collected, time elapsed, remaining estimated time, current execution node ID, and reporting timestamp. Task status change events include subtask ID, total task association ID, original status, new status, reason for status change, change timestamp, execution node ID, and summary of key operation logs. Runtime exception alarm events include subtask ID, total task association ID, exception type, exception occurrence timestamp, detailed exception description, scope of impact, and current resource status of the node. Finally, receive data and form a raw event set. The receiving end caches the event data reported by each node in real time, sorts it in the order of reporting timestamp + subtask ID, and marks each received event data as successfully received. If a reception timeout or data format error occurs, record the exception log and attempt to re-establish the connection to ensure that all valid reported events are included in the raw event set. The set is dynamically accumulated in chronological order, without missing any node's reported information.

[0074] Step 441 involves parsing and classifying the original event set, updating the status information of the corresponding subtasks in the central task manager based on the parsing results, and aggregating the status information of all subtasks to generate the updated status of the overall task. Specifically, this includes: first, performing event parsing and classification; the parsing process extracts each event data item from the original event set and classifies them according to the type identifier of the event header; performing integrity checks on the fields of each event to ensure that required fields are not missing; if any fields are missing, the event is marked as invalid and the missing fields are recorded and stored separately in the exception event log, not participating in the status update; and validating the format of the field values ​​of valid events, also marking events with incorrect formats as invalid and recording them, forming three subsets of valid events, including progress update events. The system is divided into subsets, state change event subsets, and anomaly alarm event subsets. Each subset is further grouped by subtask ID for targeted state updates. Next, the status information of the corresponding subtasks is updated. Based on the progress update event subset update, for each subtask, the current completion percentage, elapsed time, remaining estimated time, and amount of valid data collected from the latest progress update event are extracted and directly overwritten with the corresponding fields in the central task manager for that subtask. If the same subtask receives multiple progress update events within a short period, the event data with the latest timestamp is used to ensure consistency between the status information and the actual execution status of the node. Based on the state change event subset update, for each subtask, the new status, reason for the status change, and change timestamp from the event are extracted and updated in the central task manager. The task manager records the current status field of the subtask and its status change history, storing the original status, new status, reason for change, and time of change in timestamp order. If the new status is abnormal pause or failure, the subtask's exception record field is updated synchronously, along with the corresponding exception description. Based on a subset of exception alert events, if the subtask associated with an exception alert event is currently in execution, its status is updated to abnormal pause, and information such as exception type, occurrence time, and scope of impact is added to the exception record field. If the exception affects all subtasks within a node, the status of all associated subtasks on that node is updated to abnormal pause, and node-level exception information is recorded uniformly. Finally, the updated status of the overall task is aggregated, and the total status is calculated. Task progress: Calculated separately based on the execution method of subtasks (parallel / sequential). For parallel subtasks, the total task progress = number of completed subtasks ÷ total number of subtasks × 100%. For example, if there are 10 subtasks in total and 6 have been completed, then the total task progress = 6 ÷ 10 × 100% = 60%. If there are partially completed subtasks (completion rate between 0-100%), then the total task progress = (number of completed subtasks + sum of completion rates of all partially completed subtasks ÷ 100) ÷ total number of subtasks × 100%. For example, if 6 subtasks have been completed and the completion rates of the two partially completed subtasks are 50% and 30% respectively, then the total task progress = (6 + (50 + 30) ÷ 100) ÷ 10 × 100% = 68%).For sequential subtasks, the total task progress is calculated as follows: Total task progress = (Currently executing subtask number ÷ Total number of subtasks) × 100%. For example, if there are 10 subtasks in total, and the 5th subtask is currently executing with a completion rate of 30%, then the total task progress = (5 - 1 + 30 ÷ 100) ÷ 10 × 100% = 43%. If a sequential subtask is in a failed or blocked state, all subsequent subtasks are marked as blocked, and the total task progress is calculated based on the highest completed subtask number.

[0075] Determine the current status of the overall task. If all subtasks are in the "completed" state, the overall task status is "completed". If there are subtasks that are in progress or partially completed, and no subtasks have failed or are blocked, the overall task status is "normal execution". If there are subtasks that are abnormally paused, but the preset retry threshold has not been exceeded, the overall task status is "partially abnormal execution". If there are subtasks that have failed and cannot be retried, or blocked subtasks that cannot be unblocked, the overall task status is "partially failed" or "completely blocked". Summarize the overall task resource consumption: the total CPU time used by the overall task = the sum of the CPU time used by all subtasks; the total memory used by the overall task = the maximum value of the peak memory usage of all subtasks; the total amount of data transmitted by the overall task = the sum of the amount of valid data collected by all subtasks. Use this summarized data as supplementary information for the overall task status to form a complete updated overall task status.

[0076] Step 442: Based on the updated task status and the execution feedback information in the original event set, dynamically calculate the scheduling strategy parameters to be adjusted for the subtasks according to the preset strategy adjustment rules; specifically, this includes: extracting core reference data from the updated task status, including total task progress (e.g., 60%), total task status (e.g., partially executing abnormally), completion percentage of each subtask (e.g., subtask 1 completed 75%, subtask 2 completed 40%), and exception records (e.g., 3 network timeout exceptions and 2 data parsing failures); and extracting precise execution data from the execution feedback information in the original event set, specifically including:

[0077] Subtask execution time: Average execution time per session = Subtask execution time ÷ Number of executions. Example: Subtask execution time is 180 seconds, executed 3 times in total, average execution time per session = 180 ÷ 3 = 60 seconds / session (In intelligence gathering scenarios, execution time per session usually corresponds to the complete duration of a single batch of intelligence capture and parsing, used to judge execution efficiency); Data collection success rate: Number of successfully collected data entries ÷ Total number of attempts to collect data entries × 100%. Example: Total attempts to collect intelligence data entries are 100, 88 valid data entries are successfully captured and parsed, data collection success rate = 88 ÷ 100 × 100% = 88% (Core indicator, directly reflects the effectiveness of intelligence collection, below 90% requires triggering caching strategy adjustment);

[0078] Node resource utilization: CPU utilization = number of CPU cores used by the node ÷ total number of CPU cores in the node × 100%. Example: The node has a total of 8 CPU cores, and 6 cores are currently in use. The CPU utilization = 6 ÷ 8 × 100% = 75%. Memory utilization = node's used memory capacity ÷ total memory capacity in the node × 100%. Example: The node has a total memory of 16GB, and 14GB is currently in use. The memory utilization = 14 ÷ 16 × 100% = 87.5%. (Intelligence gathering nodes are mostly lightweight execution nodes. When the CPU utilization is ≥ 85% and the memory utilization is ≥ 90%, data loss and data lag are likely to occur.)

[0079] Anomaly type and cumulative number of occurrences: The number of occurrences of the same anomaly type is accumulated. For example: Network timeout anomaly (a common anomaly in intelligence gathering, caused by target site protection and network fluctuations) accumulated 4 times, and data parsing failure anomaly (caused by non-standard intelligence format and insufficient adaptability of parsing rules) accumulated 2 times.

[0080] Secondly, clearly define the three core strategy adjustment rules (aligning with the intelligence gathering field, supplementing initial parameter values ​​and specific adjusted values, explaining the technical effects of each adjustment step, making the implementation logic clearer), dynamically calculate and adjust parameters according to the rules, and ensure that all parameter values ​​meet the actual engineering requirements of the intelligence gathering scenario, avoiding meaningless values:

[0081] Calculate schedule deviation: Expected schedule = Current time ÷ Total planned task duration × 100%, Schedule deviation = Actual schedule - Expected schedule (Actual schedule is the current completion percentage of sub-tasks);

[0082] Example parameters: The total planned duration of the task is 10 hours (36,000 seconds), and the current time has progressed by 4 hours (14,400 seconds). The expected progress = 14,400 ÷ 36,000 × 100% = 40%; The current completion rate of a certain intelligence gathering sub-task is 32%, the actual progress = 32%, and the progress deviation = 32% - 40% = -8%.

[0083] Adjustments for delays (progress deviation < -5%, meaning intelligence gathering is behind schedule, requiring improved efficiency to ensure timely intelligence delivery):

[0084] Execution frequency increase ratio = (-schedule deviation) ÷ expected schedule × 0.3 (adjustment coefficient, verified multiple times in intelligence gathering scenarios, this coefficient can improve efficiency while avoiding node overload).

[0085] Adjusted execution frequency = original execution frequency × (1 + execution frequency increase ratio);

[0086] Request rate increase ratio = (-progress deviation) ÷ expected progress × 0.25 (adjustment coefficient, lower than the execution frequency increase ratio, to avoid triggering anti-crawling measures on the target site due to excessively high request rates, in line with the covert requirements of intelligence gathering).

[0087] Adjusted request rate = Original request rate × (1 + Request rate increase ratio);

[0088] Specific calculation example (supplementing reasonable initial parameters in intelligence gathering scenarios): The original execution frequency was 1 time / 10 seconds (the normal frequency of intelligence gathering to avoid triggering anti-crawling), and the original request rate was 5 messages / second (the upper limit of a single batch of intelligence requests to adapt to the access restrictions of most target sites).

[0089] The percentage increase in execution frequency = 8% ÷ 40% × 0.3 = 0.06 (6%); the adjusted execution frequency = 1 time / 10 seconds × 1.06 ≈ 1 time / 9.43 seconds (taking reasonable precision, increasing the execution frequency, and speeding up the intelligence gathering process);

[0090] The percentage increase in request rate = 8% ÷ 40% × 0.25 = 0.05 (5%); the adjusted request rate = 5 messages / second × 1.05 = 5.25 messages / second (rounded to two decimal places to match the accuracy of the project implementation and increase the amount of intelligence captured in a single batch); by slightly increasing the execution frequency and request rate, the progress of intelligence capture and parsing is accelerated, the delay is compensated for, and the timeliness of intelligence collection is ensured, while avoiding triggering anti-crawling measures on the target site and overloading of nodes.

[0091] Adjustments for over-progress (progress deviation > 5%, indicating intelligence gathering is ahead of schedule, requiring reduced execution efficiency to conserve node resources for subsequent high-load collection phases): Execution frequency reduction ratio = Progress deviation ÷ Expected progress × 0.2 (adjustment coefficient to ensure smooth frequency reduction and avoid data collection interruptions); Adjusted execution frequency = Original execution frequency × (1 - Execution frequency reduction ratio);

[0092] Request rate reduction ratio = schedule deviation ÷ expected schedule × 0.15 (adjustment coefficient, lower than the execution frequency reduction ratio to ensure the continuity of intelligence gathering); Adjusted request rate = original request rate × (1 - request rate reduction ratio);

[0093] Specific calculation example: Expected progress 40%, actual progress 48%, progress deviation = 8%; original execution frequency 1 time / 10 seconds, original request rate 5 messages / second; execution frequency reduction ratio = 8% ÷ 40% × 0.2 = 0.04 (4%); adjusted execution frequency = 1 time / 10 seconds × 0.96 ≈ 1 time / 10.42 seconds; request rate reduction ratio = 8% ÷ 40% × 0.15 = 0.03 (3%); adjusted request rate = 5 messages / second × 0.97 = 4.85 messages / second; reducing the execution frequency and request rate reduces the consumption of node CPU, memory and network resources, avoids resource waste, and at the same time ensures the continuity of intelligence collection and the integrity of collected data.

[0094] Network timeout error (cumulative number ≥ 3 times, the most common error in intelligence gathering, mostly caused by anti-scraping measures of the target site, network fluctuations, and proxy node failure. The probability of timeout needs to be reduced by adjusting parameters):

[0095] Concurrency reduction ratio = cumulative number of anomalies × 0.1 (adjustment coefficient, for each cumulative timeout, the concurrency is reduced by 10%, which has been verified by engineering to effectively reduce timeouts caused by network congestion); Adjusted concurrency = original concurrency × (1 - concurrency reduction ratio); Request timeout extension = original timeout time × (1 + cumulative number of anomalies × 0.05) (adjustment coefficient, for each cumulative timeout, the timeout time is extended by 5%, to avoid timeout judgments caused by short-term network fluctuations); Adjusted timeout time = original timeout time + extension time; Specific calculation example (supplementing reasonable initial parameters in intelligence gathering scenarios): Original concurrency 10 (the normal concurrency of intelligence gathering, adapting to the carrying capacity of most nodes), original request timeout time 10 seconds (normal timeout threshold, balancing efficiency and anti-interference), cumulative network timeout anomalies 4 times;

[0096] The percentage reduction in the number of requests is 4 × 0.1 = 0.4 (40%); the adjusted concurrency is 10 × (1 - 0.4) = 6 (reducing concurrency, reducing network congestion, and lowering the probability of timeout); the extended request timeout period is 10 × (1 + 4 × 0.05) = 10 × 1.2 = 2 seconds; the adjusted timeout period is 10 + 2 = 12 seconds (extending the timeout waiting time to adapt to network fluctuations and reduce false timeouts); by reducing the number of requests to reduce network load and extending the timeout period to adapt to network fluctuations, the probability of subsequent network timeout anomalies is effectively reduced, ensuring that intelligence gathering tasks are not interrupted and improving the stability of intelligence gathering.

[0097] Data parsing failure exception (cumulative number ≥ 2 times, mostly caused by non-standard intelligence format or insufficient compatibility of parsing rules, requiring extended parsing waiting time to ensure complete loading of intelligence data before parsing):

[0098] Parsing wait time extension ratio = cumulative number of anomalies × 0.15 (adjustment coefficient, each cumulative failure increases the wait time by 15% to ensure complete data loading); adjusted parsing wait time = original parsing wait time × (1 + extension ratio); specific calculation example: original parsing wait time 5 seconds (normal wait time for intelligence data loading), data parsing failure anomalies accumulated 2 times; parsing wait time extension ratio = 2 × 0.15 = 0.3 (30%); adjusted parsing wait time = 5 × (1 + 0.3) = 6.5 seconds;

[0099] Extend the parsing wait time to ensure that intelligence data (especially dynamically loaded intelligence content) is loaded completely, reduce parsing failures caused by missing data, and improve the integrity of intelligence collection.

[0100] Abnormal device load (CPU utilization > 85% or memory utilization > 90%). Intelligence gathering nodes are mostly lightweight devices, and excessive load can easily lead to data lag and data loss. The load pressure needs to be reduced.

[0101] Concurrency reduction ratio = (CPU utilization rate - 85%) ÷ 15% × 0.4 (If the CPU exceeds the limit, 15% is the safe redundancy range for the CPU, and 0.4 is the adjustment coefficient to ensure rapid load reduction without affecting the continuity of data collection).

[0102] Alternatively, the concurrency reduction ratio = (memory utilization rate - 90%) ÷ 10% × 0.3 (if memory exceeds the limit, 10% is the safe redundancy range for memory, and 0.3 is the adjustment coefficient. Memory deload should be smoother to avoid data loss).

[0103] Adjusted concurrency = Original concurrency × (1 - Concurrency reduction ratio);

[0104] Specific calculation example 1 (CPU overload): Original concurrency was 10, current CPU utilization is 91%;

[0105] The percentage reduction in concurrency is calculated as follows: (91% - 85%) ÷ 15% × 0.4 = 6% ÷ 15% × 0.4 = 0.16 (16%). The adjusted concurrency is calculated as follows: 10 × (1 - 0.16) = 8.4 ≈ 8 (The concurrency is rounded down to the nearest integer to fit the actual project implementation).

[0106] Specific calculation example 2 (memory overload): Original concurrency was 10, current memory usage was 93%; Concurrency reduction ratio = (93%-90%) ÷ 10% × 0.3 = 3% ÷ 10% × 0.3 = 0.09 (9%); Adjusted concurrency = 10 × (1-0.09) = 9.1 ≈ 9; By accurately reducing the concurrency, the CPU and memory load of the nodes can be quickly reduced, avoiding interruption of intelligence collection and data loss caused by node overload, and ensuring the stability of long-term collection tasks.

[0107] Resource adaptation and adjustment rules (targeting request rate and data caching strategy parameters; core technical objective: to achieve efficient utilization of intelligence gathering node resources, avoid resource waste or overload, and adapt to the dynamic load and flexible adaptation requirements of intelligence gathering).

[0108] Excessive resource usage (CPU utilization 70%-85% or memory utilization 80%-90%, node resources are in a critical high-load state, requiring slight load reduction to ensure data collection stability):

[0109] Request rate reduction ratio = (CPU utilization - 70%) ÷ 15% × 0.2 (If CPU utilization is too high, 15% is the high load range, and 0.2 is the adjustment coefficient; a slight reduction in load will not affect efficiency); or request rate reduction ratio = (memory utilization - 80%) ÷ 10% × 0.15 (If memory utilization is too high, 10% is the high load range, and 0.15 is the adjustment coefficient; a gradual reduction in load); Adjusted request rate = Original request rate × (1 - Reduction ratio); Example calculation: Original request rate: 5 requests / second, current CPU utilization: 78%; Request rate reduction ratio = (78%-70%) ÷ 15% × 0.2 = 8% ÷ 15% × 0.2 ≈ 0.1067 (10.67%); Adjusted request rate = 5 × (1-0.1067) ≈ 4.47 requests / second; Slightly reducing the request rate keeps node resource usage within a safe range, avoiding further resource overload, while maximizing intelligence gathering efficiency.

[0110] Low resource utilization (CPU utilization <30% or memory utilization <50%, node resources are wasted, request rate needs to be increased to speed up intelligence gathering):

[0111] Request rate increase ratio = (30% - CPU utilization) ÷ 30% × 0.3 (If CPU utilization is too low, 30% is the critical value for resource utilization, and 0.3 is the adjustment coefficient to quickly improve efficiency without overloading).

[0112] Alternatively, the request rate increase ratio = (50% - memory utilization) ÷ 50% × 0.2 (if memory utilization is too low, 50% is the critical value for resource utilization, and 0.2 is the adjustment coefficient for a gradual increase); the adjusted request rate = original request rate × (1 + increase ratio); specific calculation example: original request rate 5 requests / second, current CPU utilization 22%; request rate increase ratio = (30% - 22%) ÷ 30% × 0.3 = 8% ÷ 30% × 0.3 ≈ 0.08 (8%); the adjusted request rate = 5 × (1 + 0.08) = 5.4 requests / second; increasing the request rate fully utilizes idle node resources, accelerates intelligence gathering progress, shortens task cycles, and improves resource utilization.

[0113] Low data collection success rate (<90%, with failures due to incomplete data loading, mostly caused by slow loading speed of dynamic intelligence; caching time needs to be extended to ensure data integrity):

[0114] Data caching duration extension ratio = (90% - collection success rate) ÷ 10% × 0.2 (10% is the safety redundancy range for success rate, and 0.2 is the adjustment coefficient to ensure that the caching duration adapts to the data loading speed); Adjusted caching duration = original caching duration × (1 + extension ratio); Specific calculation example: Original data caching duration is 10 seconds (normal caching duration for intelligence data), current data collection success rate is 88%, failure reason is data not fully loaded; Data caching duration extension ratio = (90% - 88%) ÷ 10% × 0.2 = 2% ÷ 10% × 0.2 = 0.04 (4%); Adjusted caching duration = 10 × (1 + 0.04) = 10.4 seconds; Extending the data caching duration ensures that dynamically loaded intelligence data is fully cached, reduces collection failures caused by data not fully loaded, and improves intelligence collection success rate and data integrity.

[0115] The adjusted parameters calculated by the above three types of rules are summarized. If there are parameter conflicts (such as the need to increase the request rate due to progress delays, and the need to decrease the request rate due to excessive node resource consumption), the final parameters are determined by prioritizing the anomaly response adjustment rules over the progress adaptation adjustment rules and the resource adaptation adjustment rules. The priority setting is based on the fact that during the intelligence gathering process, anomalies can directly lead to task interruption and data loss, affecting the reliability of long-term scheduling. Their priority is higher than that of progress and resource adaptation, ensuring that anomalies are resolved first, and then progress and resources are optimized.

[0116] Example: A subtask's progress deviation is -8% (requiring an increase in request rate to 5.25 requests / second), while CPU utilization is 91% (requiring a decrease in request rate to 4.47 requests / second). These two conflict. Prioritizing resource adaptation and adjustment rules, the final request rate is determined to be 4.47 requests / second, prioritizing node stability and avoiding task interruption. Verify that the adjusted parameters are within the preset safety range (supplementing the safety range for intelligence gathering scenarios, aligning with engineering implementation). If they exceed the range, the safety boundary value is used to form the final adjustment parameters for the subtask's scheduling strategy. Preset safety range (specific to intelligence gathering scenarios): Execution frequency 0.08 times / second ~ 0.12 times / second (1 time / 8 seconds ~ 1 time / 12 seconds, avoiding anti-crawling triggers); Request rate 3 times / second ~ 8 times / second (adapting to access restrictions of most target sites); Concurrency 2 ~ 15 (adapting to the carrying capacity of lightweight collection nodes); Timeout 5 seconds ~ 20 seconds (balancing efficiency and anti-interference); Parsing waiting time 3 seconds. ~10 seconds; data caching time 5-15 seconds; verification example: if a subtask accumulates 8 network timeout exceptions, the adjusted concurrency is calculated as 10 × (1 - 8 × 0.1) = 2 (reaching the safety lower limit). If exceptions continue to accumulate, the concurrency remains unchanged at 2, not lower than the safety lower limit, to avoid low collection efficiency due to excessively low concurrency. Through parameter rationality verification, it is ensured that the adjusted strategy parameters are suitable for the actual intelligence collection scenario, and can guarantee node stability, efficient collection, and data integrity, providing a reliable basis for subsequent strategy parameter updates and task scheduling optimization, and ultimately achieving adaptive optimization of long-term scheduling of intelligence intelligence agents.

[0117] Step 443: Based on scheduling policy parameters, current task status, and execution context, trigger and acquire a task status snapshot at the current moment, including the complete task status, context, and latest policy parameters, when the preset time period expires; format and encapsulate the task status snapshot to generate a task checkpoint containing complete consistency information; specifically, this includes: First, determining the checkpoint triggering mechanism, with a preset time period that is configurable, starting from the total task start time, and automatically triggering checkpoint generation at each period node; simultaneously supporting an event-triggered supplementary mechanism, when three consecutive abnormal alarms occur, or the subtask status changes to failure or blocking, immediately triggering additional checkpoint generation to ensure the traceability of critical node status; second, acquiring the task status snapshot, quickly... Following data collection preparation, after triggering generation, briefly pause subtask status updates to ensure the collected data is consistent across the same time slice. Collect complete data dimensions: at the subtask level, each subtask's unique ID, current status, completion percentage, elapsed time, remaining estimated time, current execution node ID, amount of valid data collected, number of collection attempts, exception records, and currently effective scheduling policy parameters; at the overall task level, the overall task ID, overall progress, current status, total used resources, remaining resource quota, planned completion time, and actual estimated completion time; at the execution context level, the current execution stage, completed key operations, unexecuted step queue, subtask dependency status, and node resource allocation; and the latest strategy. At the parameter level, all subtasks' scheduling strategy parameters to be adjusted, the basis for adjustment, and the effective time of the parameters are recorded. Then, the data is formatted and encapsulated, and organized into a structured manner, organized according to a hierarchical structure of total task information - subtask set information - execution context information - latest strategy parameter information - consistency verification information. Each level defines fixed field names, numeric data is retained to two decimal places, string data uses UTF-8 encoding, timestamps are uniformly in UTC standard format, and exception records are sorted by occurrence time. Consistency verification information is generated by sorting all structured data fields by field name, concatenating them into a complete string, and performing SHA-256 hash calculation on this string to obtain an integrity check code, used to verify whether the checkpoint data is correct. The data has been tampered with. The checkpoint metadata is supplemented by adding a unique checkpoint identifier (ID), generation timestamp, version number, and data source identifier. The structured data, consistency checksum, and metadata are integrated into a JSON-formatted checkpoint file to ensure rapid parsing and reading. Finally, checkpoint consistency verification is performed. After encapsulation, the structured data in the checkpoint file is reread, strings are concatenated according to the same rules, and a hash value is calculated. This hash value is then compared with the integrity checksum in the file. If they match, the checkpoint is considered successfully generated and stored on a non-volatile storage medium. If they do not match, the checkpoint is discarded, and the snapshot collection and encapsulation process is retried until a checkpoint that passes consistency verification is generated, ensuring the checkpoint data is complete and untampered.

[0118] Based on execution feedback and preset rules, the strategy parameters are dynamically adjusted, which can flexibly adapt to the node resource status, execution progress deviation and various abnormal situations, avoiding low execution efficiency or task interruption caused by rigid strategies; and improving the adaptability of long-cycle data collection tasks to complex environmental changes.

[0119] In a preferred embodiment of the present invention, step 5 above, if the task is abnormally terminated, selects a recovery mode based on the most recent checkpoint and persistent state; after the task is completed normally, the experience triplet corresponding to this task is written back to the experience database, and the experience in the database is updated by exponential decay weighting and archiving, which may include:

[0120] In this embodiment of the invention, step 550 involves loading the most recently successfully generated task checkpoint and all related persistent task states from persistent storage when an abnormal termination of the data acquisition task is detected. This constitutes the basic dataset for task recovery. Specifically, this includes: abnormal termination detection, real-time monitoring of the data acquisition task's running status, and determining abnormal termination when network interruption, node hardware failure, target site protection blocking, software errors, or other situations prevent the task from continuing. The data source and scope are determined, and persistent storage is a non-volatile storage medium specifically used to store task states and checkpoint data. Two types of core data need to be extracted from it: one is the most recently successfully generated task checkpoint, and the other is the data related to the current abnormal termination. All persistent task states related to the task are loaded, along with task checkpoints. Each checkpoint contains a complete snapshot of the task state, including snapshots of the queue to be collected, the collected set, policy parameters, retry counts, and generation timestamps. Persistent task states are also loaded, containing key information throughout the task's lifecycle, including task ID, target profile summary, list of collected resources, queue to be collected, snapshots of executed policy parameters, retry counts, intermediate result indexes, and event logs. A basic dataset is then constructed by summarizing and integrating the loaded task checkpoints and persistent task states to ensure no conflicts or omissions, forming a complete and comprehensive basic dataset.

[0121] Step 551: Perform integrity verification on the basic dataset and, based on the specific anomaly types that triggered task abort, select the appropriate task recovery mode according to preset rules. This includes a full-state rollback recovery mode based on the most recent checkpoint and an incremental recovery mode combining checkpoints and incremental logs. Specifically, this includes: integrity verification of the basic dataset; completeness verification; checking each key field and core content in the basic dataset one by one to confirm that all snapshots of the task checkpoints and all information of the persistent task state are complete and without omissions; calculating the checksum of each core data block in the basic dataset and comparing it with the checksum recorded during storage; if the comparison matches, the data is determined to be undamaged; if the comparison does not match, the data is determined to be damaged and needs to be reloaded or a backup data should be used; anomaly type identification and classification; analyzing the triggering reasons for the abnormal task abort; and clarifying... Specific anomaly types, common types include network interruption, node failure, protection blockage, parameter error, etc. The rules clearly define the correspondence between different anomaly types and recovery modes. At the same time, the final mode is determined by combining the integrity verification results of the basic dataset. The full-state rollback recovery mode is selected when the anomaly type is a reversible temporary anomaly such as network interruption or temporary software error, and the basic dataset is complete and undamaged. This mode is suitable for scenarios where the task status was stable before the interruption and the checkpoint data can fully reflect the latest execution status. The incremental recovery mode is selected when the anomaly type is a node failure, long-term interruption, etc., which results in incremental data not being synchronized to the checkpoint, or when the checkpoint is found not to contain the latest execution record after the basic dataset verification. This mode requires supplementing and updating the status by combining the checkpoint and incremental logs to ensure that the task progress is unbiased after recovery.

[0122] Step 552: According to the selected recovery mode, using the basic dataset, reconstruct the task execution context on the central task manager and related physical acquisition nodes, and resume task execution. Specifically, this includes: Full-state rollback recovery mode execution: Overwrite the corresponding status fields of the current task in the central task manager with the snapshots of the pending collection queue, the collected set, the policy parameters, and the retry count from the most recent checkpoint in the basic dataset, ensuring complete consistency between the task status in the central task manager and the checkpoint record. Send a status synchronization command to all physical acquisition nodes associated with the task, distributing the policy parameter snapshots and execution context information from the checkpoint to each acquisition node. After receiving the information, the acquisition nodes load the corresponding execution environment and parameter configuration. Once the central task manager and all acquisition nodes have synchronized their states, the central task manager sends a resume execution command, and the task continues from the next step recorded at the checkpoint, i.e., the first subtask not executed after the checkpoint. Incremental recovery mode execution: Load the basic state, first loading the most recent checkpoint... The complete state of each point is loaded into the central task manager, initializing core states such as the queue to be collected, the collected set, and policy parameters. Incremental logs are parsed, extracting incremental event logs from the checkpoint generation to the task termination from the basic dataset. The logs contain records of completed subtasks, failure retry records, and policy adjustment records within this time period. The incremental logs are parsed one by one in chronological order. Based on the parsed incremental logs, the task state of the central task manager is updated, completed subtasks are added to the collected set, and corresponding subtasks are removed from the queue to be collected. The retry counts of relevant subtasks are corrected according to the failure retry records. The current policy parameters are updated to the latest state according to the policy adjustment records. Intermediate result indexes and event logs in the incremental logs are supplemented. The integrated and updated complete task state is sent to the relevant physical collection nodes. Each node loads the corresponding execution context and the latest parameter configuration. After the central task manager confirms that all nodes are ready, it sends a resumption execution command, and the task continues execution from the latest integrated progress.

[0123] Step 553: After the data collection task is determined to be completed normally, extract the actual scene features during the task execution process, the final data collection strategy adopted, and the final execution result to construct a new experience triplet. Specifically, this includes: determining normal task completion; checking the execution status of all sub-tasks to confirm that all sub-tasks have been completed; verifying the data collection results to ensure they meet the preset output requirements; confirming that there are no unresolved abnormal events during task execution and that the final status is completed, thus determining normal task completion; extracting actual scene features; and extracting the true features of the target object during the task execution process according to the feature dimensions of the structured target profile, specifically including the target type, the actual verified target... The target type is determined by the actual type; if the target type is found to be inconsistent with the initial profile during execution, the actual type shall prevail. Protection features include the types and trigger frequencies of protection measures actually encountered. Interaction features include the interactive operations that need to be completed during execution. Response features include the average response latency, return code distribution, and redirection mode during the actual collection process. Content update features include the observed target content update frequency and incremental content characteristics. The final collection strategy is extracted, and all parameters and values ​​of the collection strategy that ultimately took effect during this task execution are collected, specifically including the request rate (maximum number of requests per unit time or minimum request interval during actual execution), the concurrency strategy (the actual number of parallel subtasks allowed), and proxy / Outbound strategy: The actual proxy pool used, outbound IP switching rules, or anonymous network channel; browser emulation strategy: whether browser rendering is enabled, the UA identifier used, fingerprint configuration, and headless / headed mode selection; retry and backoff strategy: the maximum number of retries after failure, the backoff interval growth rule (linear / exponential), and the initial backoff time; blocking handling strategy: the specific actions taken when encountering blocking, extracting the final execution result, and summarizing the complete execution effect indicators of this task, specifically including the task completion status, clearly recording it as normal completion; time cost: the total time consumed from task start to completion; intelligence effective hit rate: the proportion of content related to the task theme in the collected results; repeated collection index. The metrics include: percentage of duplicate URLs or content; resource consumption metrics, such as total bandwidth used, CPU usage time, peak memory usage, and proxy fees during task execution; blocking / banning status, indicating whether blocking or banning signals from the target site were triggered, and if so, recording the specific signal type; failure reasons, recording "none" if there were no failures, and recording the specific reasons for failures if individual subtasks failed; and constructing a new experience triplet, using the extracted actual scenario features as the first element, the final acquisition strategy as the second element, and the final execution result as the third element, combining them in sequence to form a structurally complete new experience triplet, ensuring that the information of each element in the triplet is completely consistent with the current task execution status.

[0124] Step 554: Vectorize the new experience triples to generate corresponding experience vectors, which are then written into the experience database as new records. Read all experience vectors and their corresponding timestamps from the experience database. Based on the time elapsed since each experience and the preset decay coefficient, recalculate the time decay weight for each experience vector. Specifically, this includes: processing the actual scene features, final acquisition strategy, and final execution results in the new experience triples according to standardization rules consistent with historical experience triples, eliminating differences in data sources and dimensions; converting the standardized actual scene features into fixed-dimensional scene feature vectors; converting the parameter values ​​of the final acquisition strategy into fixed-dimensional strategy vectors; and converting the various indicators of the final execution results into fixed-dimensional result vectors. Following a preset order, concatenate the three vectors sequentially to form a complete, fixed-dimensional experience vector, ensuring that the... The vector format is consistent with existing experience vectors in the experience library. Newly generated experience vectors are treated as new records, associated with the task completion timestamp, and stored in the main data area of ​​the experience library. Simultaneously, the semantic retrieval index of the experience library is updated, incorporating the feature information of the new experience vector into the index structure to ensure that subsequent tasks can match this experience through semantic retrieval. All existing experience vectors in the experience library are traversed, and the task completion timestamp corresponding to each experience vector is read one by one (i.e., the task completion time when the experience was generated). The current system time is obtained, and the task completion timestamp of each experience vector is subtracted from the current system time to obtain the time elapsed since the beginning of the experience. The time unit is uniformly converted to days, and the system's preset decay coefficient is obtained. Simultaneously, the initial weight of each experience is determined, with a preset fixed value, such as 1.0. The initial weight of all newly generated experiences is consistent. The time decay weight of each experience vector is calculated according to a preset formula. The specific calculation method is as follows: ,in Time decay weight, which is the final weight value after a certain period of time; These are the initial weights, the base weights when time is 0; is a natural constant, a commonly used irrational number in mathematics, and its approximate value is 2.7182818; is the attenuation coefficient, a constant greater than 0, used to control the rate of weight attenuation; The value is the time elapsed since the beginning of time. It is a non-negative value representing the time span from the initial moment to the current moment. The final weight is obtained by multiplying the initial weight by the negative power of the natural constant e. The value of the negative power is obtained by multiplying the decay coefficient by the time elapsed since the beginning of time.

[0125] Step 555: Based on the recalculated time decay weight, filter all experience vectors in the experience library, transferring experience vectors with weights lower than the preset archiving threshold to the archive library or performing security cleanup. Specifically, this includes: determining the filtering criteria: obtaining the system's preset archiving threshold, a fixed weight threshold, such as 0.05. This threshold is used to classify the retention status of experience vectors. The recalculated time decay weight of each experience vector in the experience library is compared one by one with the preset archiving threshold, distinguishing between two types of experience vectors: one with a weight lower than the archiving threshold, and the other with a weight higher than or equal to the archiving threshold. For experience vectors with weights lower than the archiving threshold, if the system is preset to archive them, the experience vector and its associated timestamp are extracted from the main experience library. The original experience triplet information is stored in a separate archive repository. The archive repository is physically isolated from the main experience repository, retaining the complete data of the experience but not participating in regular semantic retrieval, and can only be called in special scenarios. At the same time, all records and index information of the experience vector in the main experience repository are deleted. For experience vectors with a weight lower than the archiving threshold, if the system is configured to clean up, the experience vector and all its associated data, including timestamps, original experience triplets, index records, check codes, etc., are completely deleted to release the storage resources of the main experience repository and ensure that there is no redundant data in the main experience repository. For experience vectors with a weight higher than or equal to the archiving threshold, their storage location and index information in the main experience repository are retained to maintain their searchable status and ensure that the collection task can normally match and call these effective experiences.

[0126] When a data collection task is abnormally interrupted, the task execution site is reconstructed based on the complete basic dataset and the appropriate recovery mode, ensuring that the task can be quickly resumed from the most recent effective progress, avoiding duplicate data collection and resource waste caused by interruption, and ensuring the continuity and stability of long-cycle data collection tasks.

[0127] like Figure 2 As shown, embodiments of the present invention also provide an adaptive data collection system for intelligence agents based on long memory and long-term scheduling, comprising:

[0128] The module is used to receive intelligence gathering tasks, parse task parameters to build a target profile, extract and standardize target features, and form a structured target profile.

[0129] The generation module is used to abstract past similar tasks into experience triples based on structured target profiles, and then vectorize and encode them into an experience database. It also constructs a semantic retrieval index to implement hierarchical storage management. Through semantic retrieval, it matches similar experiences from the experience database and generates an adaptive collection strategy by combining similarity and past success rate.

[0130] The distribution module decomposes tasks into multiple subtasks according to an adaptive acquisition strategy. Before distributing them to physical execution nodes, it assesses the actuator bearing capacity of subtasks involving torsional loads, calculates the maximum tolerable torque threshold under the current working conditions, and compares it with the drive load required by the task. If the load exceeds the safe range, it automatically adjusts the intensity of the acquisition action, reduces the operating frequency, and allocates the task to a node with higher bearing capacity. After verification, the task status is serialized and persistently stored, and the compliant subtasks are asynchronously distributed to each acquisition node for execution.

[0131] The update module dynamically updates task status and strategy parameters based on event-driven task scheduling reported by the collection nodes, and periodically generates task checkpoints.

[0132] The write-back module is used to select the recovery mode based on the most recent checkpoint and persistent status if the task is abnormally terminated; after the task is completed normally, the experience triplet corresponding to this task is written back to the experience library, and the experience in the library is updated by exponential decay weighting and archiving.

[0133] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0134] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling, characterized in that, The method includes: Step 1: Receive intelligence gathering tasks, analyze task parameters to construct target profiles, extract and standardize target features to form structured target profiles; Step 2: Based on the structured target profile, similar past tasks are abstracted into experience triples and vectorized and stored in the experience database. A semantic retrieval index is constructed and hierarchical storage management is implemented. Similar experiences are matched from the experience database through semantic retrieval, and an adaptive collection strategy is generated by combining similarity and past success rate. Step 3: Decompose the task into multiple sub-tasks according to the adaptive acquisition strategy. Before distributing them to physical execution nodes, assess the actuator bearing capacity of sub-tasks involving torsional loads, calculate the maximum tolerable torque threshold under the current working condition, and compare it with the drive load required by the task. If it exceeds the safe range, automatically adjust the intensity of acquisition actions, reduce the operating frequency, and allocate it to a node with higher bearing capacity. After verification, serialize and persistently store the task status, and asynchronously distribute compliant sub-tasks to each acquisition node for execution. Step 4: Based on the event-driven task scheduling reported by the collection nodes, dynamically update the task status and strategy parameters, and periodically generate task checkpoints; Step 5: If the task is abnormally terminated, select the recovery mode based on the most recent checkpoint and persistent status; after the task is completed normally, write the experience triplet corresponding to this task back to the experience library, and perform exponential decay weighting and archiving updates on the experience in the library.

2. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 1, characterized in that, The experience triple includes target characteristics, strategy selection, and execution effect.

3. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 2, characterized in that, Receive intelligence gathering tasks, analyze task parameters to construct target profiles, extract and standardize target features, and form structured target profiles, including: Receive and parse the raw mission parameters of the intelligence gathering mission to obtain a set of mission elements consisting of target identifiers, key attribute description text, and mission constraints. Based on the task element set, the static attribute features, temporal dynamic behavior features, and contextual features related to the external environment of the target are extracted simultaneously using preset feature recognition rules. Static attribute features, time-series dynamic behavior features, and context-related features are processed by applying preset standardization rules and normalization algorithms corresponding to each feature type to eliminate numerical differences caused by different data sources and units. Standardized feature data is used as input and filled into a preset multi-dimensional profile template. The template engine then organizes and integrates the data in a structured manner to generate a complete structured target profile that represents the intelligence features of the target object.

4. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 3, characterized in that, Based on structured target profiles, similar past tasks are abstracted into experience triples and vectorized and stored in the experience database. A semantic retrieval index is constructed and hierarchical storage management is implemented. By semantically retrieving similar experiences from an experience base and combining similarity with past success rates, an adaptive data collection strategy is generated, including: Based on the dimensions of structured target profiles, previously completed tasks are abstracted into experience triples. The task scenario description and collection strategy in the experience triples are vectorized and encoded using a pre-trained semantic model to obtain task scenario semantic vectors and collection strategy semantic vectors, which are then correlated with the recorded success rate index calculated based on the corresponding execution results. Vectorized experience data associated with success rate indicators are stored in the experience database; cluster analysis is performed based on the distribution of semantic vectors of all task scenarios in the experience database, and a hierarchical semantic index structure is constructed based on the clustering results to achieve hierarchical storage and management of experience data. The structured target profile of the current task is converted into a query semantic vector; the query semantic vector is used as input, and a semantic similarity retrieval is performed in the experience base with the help of a hierarchical semantic index to match and return the K prior experience triples that are most similar to the current task scenario; For the K prior experience triples obtained by retrieval and matching, the semantic similarity between the task scenario semantic vector and the current query semantic vector in each experience triple is extracted as a weight, and the recorded success rate index associated with each experience triple is obtained. Based on the semantic similarity weight and the existing efficiency performance, the collection strategies recorded in the K experience triples are weighted and fused to generate an adaptive collection strategy suitable for the current task.

5. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 4, characterized in that, Based on the adaptive acquisition strategy, the task is decomposed into multiple sub-tasks. Before being distributed to the physical execution nodes, the actuator bearing capacity is assessed for sub-tasks involving torsional loads. The maximum tolerable torque threshold under the current working condition is calculated and compared with the drive load required by the task, including: Based on the action sequence and resource requirements planned in the adaptive acquisition strategy, the total task to be executed is divided into a series of logically independent sub-tasks that can be executed sequentially or in parallel. For a set of subtasks, identify the specific subtasks that require output torque to complete the acquisition action, and extract the drive load parameters required to execute the subtasks based on the action instructions of the specific subtasks, including the peak torque value and continuous torque value required to be output. Based on the resource mapping relationship of the adaptive acquisition strategy, determine the physical execution node to be allocated to a specific subtask, and obtain the mechanical physical parameters and current real-time operating status parameters of the target execution node. Based on the physical parameters of the mechanism and the current real-time operating condition parameters, a comprehensive analysis is conducted through preset torque bearing capacity calculation rules to evaluate and calculate the dynamic maximum tolerable torque threshold of the target execution node under the current specific operating condition. The drive load parameters required for a specific subtask are compared with the dynamic maximum tolerable torque threshold to determine whether the load demand is within the safe bearing range of the mechanism.

6. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 5, characterized in that, The physical parameters of the mechanism include at least the rated torque of the transmission mechanism, the safety factor, and the material strength characteristics. The current real-time operating condition parameters include at least the wear condition of the mechanism, the ambient temperature, and the lubrication condition.

7. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 6, characterized in that, If the data collection intensity is exceeded, the operation frequency will be automatically adjusted, and the data will be allocated to a node with higher capacity. After verification, the task status is serialized and persistently stored, and the compliance subtasks are asynchronously distributed to each data collection node for execution, including: When the real-time required drive load exceeds the maximum tolerable torque threshold, the ratio of the reduction of the collected action intensity parameters and execution frequency parameters is dynamically calculated based on the ratio of the real-time drive load to the maximum tolerable torque threshold, and the adjusted intensity parameters and execution frequency parameters are generated according to the reduction ratio. Using the adjusted intensity parameters and execution frequency parameters as input, the new requirements of the acquisition task on the physical node carrying capacity are calculated; based on the new requirements, physical execution nodes with matching carrying capacity are dynamically selected from the node resource pool, and the selected nodes are reassigned and bound to the sub-tasks to be executed. For all subtasks that have been reassigned and bound, perform a final compliance check on the node allocation scheme and parameter configuration; and serialize the complete task status information for compliance check. The transformed task status information is written to a non-volatile storage medium to complete persistent storage. The compliant subtasks and their corresponding configuration parameters that have been persistently stored are asynchronously distributed to the corresponding physical acquisition nodes and execution instructions are sent to the nodes to trigger the execution of the acquisition tasks.

8. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 7, characterized in that, Event-driven task scheduling based on data collection node reports dynamically updates task status and policy parameters, and periodically generates task checkpoints, including: It continuously receives execution event data streams actively reported by each physical acquisition node, and obtains a raw event set including task progress updates, task status changes, and runtime exception alarms; The original event set is parsed and classified. The status information of the corresponding subtasks in the central task manager is updated according to the parsing results. The updated status of the total task is generated by aggregating the status information of all subtasks. Based on the updated task status and the execution feedback information in the original event set, the scheduling strategy parameters to be adjusted for the subtask are dynamically calculated according to the preset strategy adjustment rules. Based on scheduling policy parameters, current task status, and execution context, when the preset time period expires, a task status snapshot including the complete task status, context, and latest policy parameters at the current moment is triggered and obtained; the task status snapshot is formatted and encapsulated to generate a task checkpoint including complete consistency information.

9. The adaptive data acquisition method for intelligence agents based on long memory and long-term scheduling according to claim 8, characterized in that, If the task is aborted abnormally, the recovery mode is selected based on the most recent checkpoint and persistent state; after the task is completed normally, the experience triplet corresponding to this task is written back to the experience repository, and the experience in the repository is updated with exponential decay weighting and archiving, including: When an abnormal abort of the data collection task is detected, the most recently successfully generated task checkpoint and all related persistent task states are loaded from persistent storage to form the basic dataset for task recovery. The integrity of the basic dataset is verified, and the appropriate task recovery mode is selected according to the specific anomaly type that triggered the task abort, based on preset rules. This includes a full state rollback recovery mode based on the most recent checkpoint, and an incremental recovery mode that combines checkpoints and incremental logs. Based on the selected recovery mode, using the basic dataset, the task execution scene is reconstructed on the central task manager and relevant physical acquisition nodes, and task execution is restored; Once the data collection task is determined to be completed normally, extract the actual scene features during the execution of this task, the final data collection strategy adopted, and the final execution result to construct a new experience triplet. The new experience triples are vectorized and encoded to generate corresponding experience vectors, which are then written into the experience database as new records. All experience vectors and their corresponding timestamps are read from the experience database, and the time decay weight is recalculated for each experience vector based on the time elapsed since the beginning of each experience and the preset decay coefficient. Based on the recalculated time decay weights, all experience vectors in the experience library are filtered, and experience vectors with weights lower than the preset archiving threshold are transferred to the archive library or subjected to security cleanup.

10. An adaptive data acquisition system for intelligence agents based on long memory and long-term scheduling, wherein the system implements the method as described in any one of claims 1 to 9, characterized in that, include: The module is used to receive intelligence gathering tasks, parse task parameters to build a target profile, extract and standardize target features, and form a structured target profile. The generation module is used to abstract past similar tasks into experience triples based on structured target profiles, and then vectorize and encode them into an experience database. It also constructs a semantic retrieval index to implement hierarchical storage management. Through semantic retrieval, it matches similar experiences from the experience database and generates an adaptive collection strategy by combining similarity and past success rate. The distribution module is used to decompose the task into multiple sub-tasks according to the adaptive acquisition strategy. Before distributing to the physical execution node, it evaluates the actuator bearing capacity of the sub-tasks involving torsional loads, calculates the maximum tolerable torque threshold under the current working condition, and compares it with the drive load required by the task. If the data collection intensity is exceeded, the operation frequency will be automatically adjusted, and the data will be allocated to a node with higher capacity. After verification, the task status is serialized and persistently stored, and the compliance subtasks are asynchronously distributed to each collection node for execution. The update module dynamically updates task status and strategy parameters based on event-driven task scheduling reported by the collection nodes, and periodically generates task checkpoints. The write-back module is used to select the recovery mode based on the most recent checkpoint and persistent status if the task is abnormally terminated; after the task is completed normally, the experience triplet corresponding to this task is written back to the experience library, and the experience in the library is updated by exponential decay weighting and archiving.

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