Multi-channel publicity management method and system for regional health improvement

Abstract

The present invention discloses a multi-channel publicity management method and system for regional health improvement, which relates to the field of multi-channel publicity management technology, and specifically includes the following steps: when the health publicity task is an operational task, the channels where the task operation chain cannot be completed are screened out from all channels matching the task, and marked as abnormal operation chain channels; the execution behavior information of each abnormal operation chain channel is obtained, and analyzed after acquisition, the execution path interruption degree of each abnormal operation chain channel is evaluated, and each abnormal operation chain channel is graded according to the evaluation results; a multi-channel distribution path is configured for the operational task according to the grading results, and the channel distribution method is dynamically regulated. The present invention solves the problem that the execution chain of operational health tasks is easily interrupted in multiple channels, realizes accurate distribution and dynamic regulation based on evaluation and grading, and improves the task completion rate and intervention effectiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-channel publicity management, and in particular to a multi-channel publicity management method and system for regional health improvement. Background Art

[0002] Multi-channel publicity management refers to the use of multiple information dissemination media (such as WeChat official accounts, short message services, mobile application notifications, community broadcasts, emails, online live broadcasts, physical bulletin boards, etc.) to coordinate the release and dissemination of specific content across different channels under a unified planning and scheduling mechanism, and continuously optimize content and channel strategies through data feedback. It is a systematic management approach. Multi-channel publicity management for regional health improvement, on this basis, focuses on improving the overall health literacy and health behavior intervention effects of residents in a specific geographical area. By combining multi-dimensional data such as regional population structure, health records, and communication preferences, it develops targeted, personalized, and differentiated health information push plans, and achieves accurate content reach and maximizes coverage, thereby raising public health awareness, promoting early intervention, and optimizing resource utilization efficiency. The reason why multi-channel publicity and management for regional health improvement is needed is that the traditional single publicity model can no longer meet the efficiency and refinement needs of modern public health governance. Against the background of health information explosion and user behavior differentiation, relying solely on a certain communication path often leads to problems such as scattered publicity effects, insufficient population coverage, and delayed health intervention. Only by establishing a comprehensive management method based on regional characteristics, data-driven as the core, and multi-channel linkage as a means can we truly realize the scientific, intelligent and efficient dissemination of health information, thereby promoting the continuous improvement and comprehensive improvement of regional health levels.

[0003] Existing multi-channel publicity and management technologies for regional health improvement usually integrate multiple information technology platforms and communication channels to build a health information dissemination network covering the regional population. Its management process generally includes five core links: "population identification and profiling, content generation and matching, channel selection and distribution, feedback collection and effect evaluation, strategy optimization and continuous iteration." First, the system integrates regional health databases, demographic information, and resident behavior data to create portraits of people with different health needs and classify the types of publicity targets; secondly, based on population characteristics and health goals, it intelligently generates or calls corresponding health publicity content, such as chronic disease prevention knowledge, vaccination reminders, mental health counseling, etc., and selects the most suitable communication materials through a label matching mechanism; then, based on user usage habits, channel characteristics, and content urgency, the system automatically dispatches multiple publicity carriers such as WeChat public accounts, short messages, APP push, community broadcasts, etc. to achieve content distribution and delivery; then, through the data collection module, it monitors user reception, click behavior, forwarding paths and other feedback information in real time to quantitatively evaluate the publicity effect; finally, based on the evaluation results, the system adjusts the publicity strategy and content structure, and continuously optimizes the information dissemination path and method, thus forming a closed-loop, dynamically optimized multi-channel health publicity management system.

[0004] The existing technology has the following deficiencies:

[0005] In regional health promotion campaigns, operational tasks (such as completing health questionnaires, registering for vaccine appointments, and submitting daily blood pressure data) not only require the information to be successfully pushed, but also rely on users completing subsequent operational processes such as jumps, form loading, and data submission within the receiving channel. During the execution of such tasks, if the selected channel has the ability to push content but lacks the function to support a complete operation chain, users will receive the task content but be unable to complete the operation. However, existing multi-channel promotion management technologies for regional health promotion cannot dynamically adjust the channel distribution method based on the degree of interruption in the execution path of operational health promotion tasks in the region when the task operation chain cannot be completed in a specific channel. This leads to interruptions in the task process and obstructions in user response behavior, which in turn causes the failure of intervention task execution, the loss of key data, and the difficulty in achieving regional health management goals.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a multi-channel publicity management method and system for regional health improvement to solve the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a multi-channel publicity and management method for regional health improvement, specifically comprising the following steps:

[0009] Identify the content structure of health promotion tasks, extract their structural feature information, and analyze whether the health promotion tasks are operational tasks based on the extracted structural feature information;

[0010] If the health promotion task is an operational task, select the channels where the task operation chain cannot be completed from all channels matching the task, and mark them as abnormal operation chain channels;

[0011] Obtain and analyze the execution behavior information of each abnormal operation chain channel, evaluate the degree of interruption of the execution path of each abnormal operation chain channel, and classify each abnormal operation chain channel based on the evaluation results;

[0012] Configure multi-channel distribution paths for operational tasks based on the classification results, and dynamically adjust the channel distribution method;

[0013] During the task execution process, the user's operation behavior feedback information in each channel is obtained, and the execution path interruption degree assessment result of the operation chain abnormal channel is updated based on the obtained operation behavior feedback information.

[0014] Preferably, when the health promotion task is an operational task, the channels where the task operation chain cannot be completed are screened out from all channels matching the task, and are marked as abnormal operation chain channels, specifically:

[0015] If the health promotion task is an operational task, identify all channels that match the health promotion task;

[0016] For each channel matching the health promotion task, call the execution data of the same type of tasks in the channel to determine whether there is at least one of the following uncompleted behavior records: jump failure, form loading failure, and task submission failure. If there is at least one uncompleted behavior record, filter the channel as the channel where the task operation chain cannot be completed;

[0017] The filtered channels where the task operation chain cannot be completed are marked as abnormal operation chain channels.

[0018] Preferably, the execution behavior information of each abnormal operation chain channel is obtained and analyzed after acquisition to evaluate the degree of interruption of the execution path of each abnormal operation chain channel, and each abnormal operation chain channel is graded according to the evaluation results, specifically including:

[0019] Obtain the execution behavior information of each abnormal channel of the operation chain and pre-process the obtained execution behavior information;

[0020] Extracting path stability information and operation coherence information from the pre-processed execution behavior information, and generating the path blocking index and operation interruption coefficient of each abnormal channel of the operation chain based on the extracted information;

[0021] Based on the generated path blocking index and operation interruption coefficient of each abnormal operation chain channel, an execution interruption evaluation model is constructed to generate the execution interruption index of each abnormal operation chain channel;

[0022] Determine the pre-set execution interruption index threshold range, and compare it with the generated execution interruption index of each operation chain abnormal channel after determination. Evaluate the execution path interruption degree of each operation chain abnormal channel based on the comparison results, and grade each operation chain abnormal channel based on the evaluation results.

[0023] Preferably, the logic for obtaining the path blocking index of each abnormal channel of the operation chain is as follows:

[0024] Path stability information is extracted from the pre-processed execution behavior information, including the cumulative number of times that users triggered the jump component but failed to jump successfully in each operation chain abnormal channel during the historical task execution, and the ratio of the number of form loading failures to the total number of loading attempts, and marked them as and , Indicates the The cumulative number of times that users triggered the jump component but failed to jump successfully during the execution of historical tasks in the abnormal operation chain channel. Indicates the The ratio of the number of form load failures to the total number of load attempts in the historical task execution of the abnormal channel of the operation chain, , is a positive integer;

[0025] Calculate the path blocking index of abnormal channels in each operation chain The specific calculation method is: The ratio of the number of form load failures to the total number of load attempts in the historical task execution of the abnormal channel of the operation chain Square it and add it to the number 1 to get the first part; The cumulative number of times that users triggered the jump component but failed to jump successfully during the execution of historical tasks in the abnormal operation chain channel Add it to the number 1 and take the natural logarithm to get the second part; multiply the first part by the second part, and the product is the first part. Path blocking index of abnormal channels in the operation chain .

[0026] Preferably, the logic for obtaining the operation interruption coefficient of each abnormal channel of the operation chain is as follows:

[0027] The operation coherence information is extracted from the pre-processed execution behavior information, including the number of users who visited the task page but did not complete any key operations in each abnormal channel of the operation chain, the total number of users who visited the task page, and the cumulative number of times the user clicked submit but did not respond successfully, and marked them as 、 and , Indicates the The number of users who visited the task page but did not complete any key operations in the abnormal operation chain channel. Indicates the The total number of users who accessed the task page through abnormal channels in the operation chain, Indicates the The cumulative number of times users click submit but fail to respond successfully in abnormal channels of the operation chain. , is a positive integer;

[0028] Calculate the operation interruption coefficient of each abnormal channel of the operation chain The specific calculation formula is: The number of users who visited the task page but did not complete any key operations in the abnormal operation chain channel The total number of users who visited the task page Divide by to get the midway interruption rate; then divide the The cumulative number of times users clicked submit but failed to respond successfully in abnormal channels of the operation chain Add it to 1 and take the natural logarithm; multiply the midway interruption rate by the natural logarithm, and the product is the first Operation interruption coefficient of abnormal channels in the operation chain .

[0029] Preferably, based on the generated path blocking index of each abnormal channel of the operation chain and operation interruption coefficient Construct an execution interruption assessment model and generate the execution interruption index of each operation chain abnormal channel through weighted summation .

[0030] Preferably, a predetermined execution interruption index threshold interval is determined , and after determination, generate the execution interruption index of each operation chain abnormal channel Perform a comparison and evaluate the degree of interruption of the execution path of each abnormal operation chain channel based on the comparison results. Then, classify each abnormal operation chain channel based on the evaluation results. The specific comparison analysis and classification are as follows:

[0031] like ,The execution path interruption degree of the operation chain abnormal channel is low, and the operation chain abnormal channel is classified as a first-level channel;

[0032] like ,The execution path interruption degree of the operation chain abnormal channel is medium, and the operation chain abnormal channel is classified as a secondary channel;

[0033] like ,The execution path interruption degree of the operation chain exception channel is high, and the operation chain exception channel is divided into three levels.

[0034] Preferably, a multi-channel distribution path is configured for the operational task according to the classification result, specifically: the abnormal operation chain channel classified as the first-level channel is set as the main distribution channel for the operational task, which is used to carry the delivery of the task content first; the abnormal operation chain channel classified as the second-level channel is set as the backup distribution channel for the operational task, and when the distribution capacity of the first-level channel reaches the preset upper limit, the second-level channel is activated to supplement the task delivery, and when the first-level channel fails to deliver, the second-level channel is activated to perform the task delivery instead; the abnormal operation chain channel classified as the third-level channel is removed from the distribution path of the operational task, and the task delivery to the third-level channel is prohibited;

[0035] The channel distribution method is dynamically regulated. Specifically, during the task execution process, based on the task response status, channel feedback data and user behavior change information, the following dynamic regulation operations are performed respectively: increase the task distribution frequency of the first-level channel, reduce the task distribution frequency of the second-level channel, and stop the task delivery of the third-level channel, so as to realize real-time regulation and optimization of the task distribution path.

[0036] Preferably, a multi-channel publicity management system for regional health improvement includes a task type identification module, a channel anomaly identification module, a path interruption assessment module, a distribution path configuration module, and a feedback-driven update module;

[0037] The task type identification module identifies the content structure of the health promotion task, extracts its structural feature information, and analyzes whether the health promotion task is an operational task based on the extracted structural feature information;

[0038] The channel anomaly identification module, when the health promotion task is an operational task, screens out the channels where the task operation chain cannot be completed from all channels matching the task, and marks them as channels with abnormal operation chains;

[0039] The path interruption assessment module obtains and analyzes the execution behavior information of each abnormal operation chain channel, assesses the degree of execution path interruption of each abnormal operation chain channel, and classifies each abnormal operation chain channel based on the assessment results;

[0040] The distribution path configuration module configures multi-channel distribution paths for operational tasks based on the classification results and dynamically adjusts the channel distribution method;

[0041] The feedback-driven update module obtains the user's operation behavior feedback information in each channel during the task execution process, and updates the execution path interruption degree assessment results of the abnormal operation chain channel based on the obtained operation behavior feedback information.

[0042] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0043] 1. The present invention, by constructing a task type identification module and a channel anomaly identification module, can automatically complete the task content structure analysis and operation type determination in the early stage of health promotion task generation, and combine historical task execution data to identify channels where the task operation chain cannot be completed from all channels that match the task. This pre-placed structure identification and behavior tracing mechanism can exclude high-risk channels before the task is distributed, ensuring that operational tasks only enter distribution paths with complete execution capabilities, and fundamentally avoiding the problems of task chain breaks and operational process interruptions. Compared to the existing channel selection method based on static rules or manual experience, this solution greatly improves the accuracy and automation level of channel matching, and has strong task adaptation flexibility and deployment robustness.

[0044] 2. This invention introduces two calculable execution behavior parameters: the path blocking index and the operation interruption coefficient. It constructs an execution interruption index based on multiple categories of quantitative behavioral data and implements multi-level hierarchical management of channels by setting threshold intervals. This data-driven and mathematically modeled evaluation mechanism breaks through the traditional binary judgment model of "whether" the channel status is available and can provide a fine-grained assessment of the reliability and interruption risk of each channel in the task execution chain. At the same time, by grading the execution interruption index into primary, secondary, and tertiary channels, a multi-path redundant distribution structure for tasks can be further constructed, improving the security of task carrying while enhancing the flexibility of the distribution strategy, providing a clear strategic reference for the stable execution of tasks.

[0045] 3. The present invention also includes a set of information feedback mechanisms based on user operation behavior feedback. During the task execution process, the system continuously collects user response data, behavior trajectories, and task completion status on each channel, and uses them in real time to update the channel's execution path interruption evaluation results. Through feedback-driven model reconstruction and distribution path adjustment, the system can achieve adaptive optimization of the distribution strategy, thereby dynamically increasing the proportion of tasks in channels with excellent performance, compressing or eliminating high-risk channels, and realizing a closed-loop control process of "identification-evaluation-distribution-feedback-update". This mechanism significantly improves the system's operational intelligence level and environmental adaptability, and solves the problem of task failure caused by the difficulty in real-time perception of channel status changes in existing technologies, ensuring that regional health improvement tasks can continuously and stably achieve their expected goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0047] Figure 1 This is a flow chart of the multi-channel publicity management method and system for regional health improvement according to the present invention.

[0048] Figure 2 This is a method mind map of the multi-channel publicity management method and system for regional health improvement of the present invention.

[0049] Figure 3 This is a module diagram of the multi-channel publicity management method and system for regional health improvement of the present invention. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0051] The present invention provides Figure 1 and Figure 2 The multi-channel publicity and management method for regional health improvement shown in the figure includes the following steps:

[0052] Identify the content structure of health promotion tasks, extract their structural feature information, and analyze whether the health promotion tasks are operational tasks based on the extracted structural feature information;

[0053] The software system can perform structured analysis of the original content template or task data structure of the health promotion task to achieve content structure recognition. Specifically, it includes: automatically reading the description fields, embedded components, interactive element labels, nested levels, control types, etc. of the task content, and identifying the number of paragraphs, text fields, image embeddings, types of interactive controls (such as forms, buttons, jump links), whether there is jump logic, and other structural features contained in the task based on the document object model (DOM), JSON structure tree or database field mapping. The software system automatically extracts the above structural elements by setting field extraction rules or based on the template rule engine, and classifies and integrates them into unified structural feature information for subsequent task classification and analysis.

[0054] After extracting structural feature information, the software system can analyze whether the task contains components that require user interaction based on a set of preset judgment logic rules, thereby determining whether the task is an operational task. Specific implementation methods include: determining whether the extracted structural feature information contains features such as input controls (such as form fields), jump behavior identifiers (such as links, QR codes, jump instructions), and behavior return instructions (such as submit buttons, feedback events); if any one or more interactive elements meet the preset judgment threshold (such as "contains form fields" or "contains jump paths"), the task is marked as an operational task; conversely, tasks that only contain static text or images without interactive behavior are identified as reading tasks. This process can be implemented through a rule engine, conditional decision tree, or content classification model.

[0055] Identifying the content structure and determining whether the health promotion task is an operational task is a key prerequisite for implementing a multi-channel intelligent distribution strategy. This is because only by clarifying whether the task depends on the user's subsequent interactive operations in the channel (such as filling in, jumping, submitting, etc.) can we further determine whether the task is dependent on the channel's interactive capabilities, thereby affecting the channel selection strategy. If the task is identified as an operational task, a channel with the ability to carry a complete operational chain must be used subsequently; otherwise, any channel that supports information delivery can be used. If the task type is not accurately identified, the interactive task may be distributed to a channel that does not support the operational chain, resulting in an execution interruption problem of "task content delivered but unable to be completed", which seriously affects the effect of health intervention. Therefore, this step is the logical starting point for the execution of a series of strategies such as subsequent channel matching, anomaly identification, and evaluation and grading.

[0056] If the health promotion task is an operational task, select the channels where the task operation chain cannot be completed from all channels matching the task, and mark them as abnormal operation chain channels;

[0057] In this embodiment, when the health promotion task is an operational task, the channels where the task operation chain cannot be completed are screened out from all channels matching the task, and are marked as abnormal operation chain channels. Specifically,

[0058] If the health promotion task is an operational task, identify all channels that match the health promotion task;

[0059] The determination of all channels that match the health promotion task can be achieved through the preset channel matching rules and task feature associations in the software system, combined with the channel capability database. Specifically, after the system identifies a health promotion task as an operational task, it will extract the structural feature information corresponding to the task, such as whether it contains interactive elements such as jump actions, form fields, and data submission controls, and compare these elements with the support capabilities of each channel recorded in the channel capability library. The channel capability library pre-stores parameters such as the operation type, content format, interaction method, and feedback capability supported by each channel. The system uses a rule engine or conditional filtering logic to screen out channels that have all the operational elements required to carry out the task, and classifies such channels into a set of channels that match the operational task. This process can be automatically completed through methods such as label mapping and capability Boolean matrix comparison to ensure the accuracy of channel selection and the feasibility of task execution.

[0060] For each channel matching the health promotion task, call the execution data of the same type of tasks in the channel to determine whether there is at least one of the following uncompleted behavior records: jump failure, form loading failure, and task submission failure. If there is at least one uncompleted behavior record, filter the channel as the channel where the task operation chain cannot be completed;

[0061] By establishing a task execution record database and channel task association index in the software system, it is possible to call the execution data of the same type of tasks that have been historically executed in each channel that matches the health promotion task. Specifically, after identifying a certain operational task, the system will extract the task type identifier of the task and generate a task type label based on the structural feature information of the task. Then, based on the task type label, the system retrieves all historical task instances corresponding to the type label in the execution record database, and further filters out the execution records of this type of task in the target channel through the channel identification index. These records include data on dimensions such as task delivery time, user access path, operation node status, interactive component loading results, and final submission status. The system can use a combination of database interface, task type mapping table and channel label to batch retrieve the complete execution data set of the task type on the specified channel, providing data support for subsequent behavior judgment.

[0062] The identification of unfinished behaviors can be achieved by automatically analyzing the status fields of key operation nodes in each historical execution record. Specifically, the system will structure and deconstruct each retrieved task execution record, and extract the execution status values ​​of the jump behavior node, form loading node, and data submission node respectively. These status values ​​are usually stored in the form of Boolean, timestamp, or execution code. For example: the failure of the jump to be triggered can be manifested as the jump behavior field being empty or the status being marked as "not executed"; the incomplete form loading can be manifested as the page not being fully loaded, the resource request failing, or the component loading timeout; the incomplete task submission can be manifested by the failure to trigger the submission instruction, the failure to return a response, or the failure to record a successful entry in the database. The system sets a set of standardized judgment conditions and performs logical judgments on the status of the above three key nodes one by one. If an unfinished state is identified at any node, the record is marked as abnormal, and the corresponding channel is marked as an abnormal operation chain channel, realizing automatic screening and abnormal channel calibration.

[0063] The filtered channels where the task operation chain cannot be completed are marked as abnormal operation chain channels.

[0064] The channels where the task operation chain cannot be completed can be calibrated by building a channel status identification mechanism in the software system and automatically updating the channel labels based on the screening results. Specifically, after the system completes the analysis of the historical execution data of each channel, it will determine that there are channels with behaviors such as jump failure, incomplete form loading, or incomplete task submission, and centrally process them as screening results. The system maintains a status label table for each channel, which contains fields such as channel identification, current adaptation status, and operation chain integrity status. When a channel is identified as a channel where the task operation chain cannot be completed, the system automatically updates the "operation chain integrity status" field in its status label table to "abnormal", or adds the channel to the operation chain abnormal channel list data structure. This calibration process can be triggered by the task analysis module and written to the database or cache system for direct call in the subsequent execution behavior information acquisition and evaluation phase, thereby completing the channel abnormality calibration operation based on the analysis results.

[0065] When a health promotion task is an operational task, it is necessary to screen out channels from all matching channels where the task operation chain fails and label them as abnormal operation chain channels. This is because operational tasks not only require information to be pushed to the user terminal, but also require the user to complete a series of interactive operations within the receiving channel, such as jumping to a page, filling out a form, and submitting data. If a channel has delivery capabilities but lacks complete interactive support capabilities, the task operation chain will be interrupted, preventing the user from completing the task. This situation will not only cause the task to fail, but also lead to the loss of regional health management data and the inability to evaluate the effectiveness of the intervention. Therefore, identifying and labeling such channels in advance can exclude or lower their priority in the subsequent distribution path configuration, thereby avoiding the delivery of tasks to channels with insufficient execution capabilities. This ensures the executable nature of operational tasks and the effective implementation of intervention measures, which is a fundamental step in achieving multi-channel precision delivery and improving the completion rate of health tasks.

[0066] Obtain and analyze the execution behavior information of each abnormal operation chain channel, evaluate the degree of interruption of the execution path of each abnormal operation chain channel, and classify each abnormal operation chain channel based on the evaluation results;

[0067] In this embodiment, the execution behavior information of each abnormal operation chain channel is obtained and analyzed after acquisition to evaluate the degree of interruption of the execution path of each abnormal operation chain channel. The abnormal operation chain channel is graded according to the evaluation results, specifically including:

[0068] Obtain the execution behavior information of each abnormal channel of the operation chain and pre-process the obtained execution behavior information;

[0069] The software system can be used to access the task execution log database, user behavior tracking system and channel interaction record module, and combine the channel identifier and task number to automatically obtain the execution behavior information of each abnormal channel in the operation chain. The specific implementation method is: the system first reads each channel identifier in the abnormal channel set of the operation chain, and extracts the relevant behavior data generated by the channel during the historical task execution process. The data range includes user jump behavior records, component loading status logs, operation button trigger events, form input behavior and submission action response codes, etc. The system quickly locates the data distribution blocks of the channel in the process of executing the same type of operational tasks by constructing a channel-task mapping index, and automatically retrieves all relevant behavior records based on data tags. The acquired execution behavior information is input into the processing flow in the form of structured data for subsequent analysis and modeling.

[0070] The purpose of preprocessing the acquired execution behavior information is to improve the accuracy and stability of subsequent information extraction and parameter calculation, and to avoid the influence of data anomalies, inconsistent formats or missing fields on the model judgment results. The preprocessing process includes the following three types of operations: First, fill in or remove empty values ​​and missing fields in the behavior records, such as deleting jump records with missing status marks; second, unify and standardize the formats of data fields from different formats, such as converting Boolean loading status and enumeration submission response codes into unified success / failure identification labels; third, identify and correct outliers, such as normalizing or truncating a single extreme high value in the number of jump failures. The entire preprocessing process is automatically completed by the data cleaning module in the software system, combining field rules, data integrity verification logic and standardized rule tables to ensure that the input information is computable and logically consistent, providing a reliable data foundation for the subsequent extraction of path stability information and operation continuity information.

[0071] Extracting path stability information and operation coherence information from the pre-processed execution behavior information, and generating the path blocking index and operation interruption coefficient of each abnormal channel of the operation chain based on the extracted information;

[0072] The software system's built-in structured behavior data parsing module, combined with preset indicator mapping rules, can automatically extract path stability and operational consistency information from preprocessed execution behavior information. Specifically, the system first identifies and categorizes the fields of each execution behavior record. Based on task type, channel identifier, and behavior event label, it isolates data fields related to jump behavior, form loading behavior, submission behavior, and user operation behavior. For path stability information, the system extracts the jump failure count field and the form loading status field. A complete data subset is formed by counting the total number of jump failure events and calculating the load failure rate. For operational consistency information, the system extracts the user interruption identification field, the access user identification field, and the submission failure event field. Using a user path integrity analysis algorithm, the number of users interrupted during the process is determined, and the total number of access users and the number of submission failures are counted. Based on a field feature label matching mechanism, the system encapsulates this extracted data into two structured data sets in a predefined format. These are output as path stability information and operational consistency information, respectively, for use in the subsequent parameter calculation model. The entire extraction process is automatically completed by the software, which has data-driven, structure-matching and semantic classification capabilities to ensure that the extracted information is accurate, quantifiable and can be used for modeling and analysis.

[0073] Based on the generated path blocking index and operation interruption coefficient of each abnormal operation chain channel, an execution interruption evaluation model is constructed to generate the execution interruption index of each abnormal operation chain channel;

[0074] Determine the pre-set execution interruption index threshold range, and compare it with the generated execution interruption index of each operation chain abnormal channel after determination. Evaluate the execution path interruption degree of each operation chain abnormal channel based on the comparison results, and grade each operation chain abnormal channel based on the evaluation results.

[0075] The software system can automatically set and dynamically adjust the execution interruption index threshold range based on historical task execution results data and statistical modeling methods, combining multi-dimensional assessment objectives. Specifically, the system first extracts the evaluated execution interruption index results from historical task data. Combined with target indicators for each channel, such as task completion rate, user interaction success rate, and feedback effectiveness, it establishes a correlation between the execution interruption index and actual task completion performance. The system then statistically categorizes the historical execution interruption index using cluster analysis or quantile distribution methods, for example, by quartiles or K-means clustering to rank channel performance. Based on this, the system automatically generates three interval boundary values: low, medium, and high interruption intervals, which serve as the current execution interruption index threshold range. As new data continues to flow in, the system can periodically retrain the model or dynamically update the threshold boundaries to adapt to different data stages. The entire determination process relies on data-driven analytical modeling logic and is automatically executed by the software, ensuring that the grading intervals are scientifically sound, responsive to actual differences, and adjustable.

[0076] In this embodiment, the logic for obtaining the path blocking index of each abnormal channel of the operation chain is as follows:

[0077] Path stability information is extracted from the pre-processed execution behavior information, including the cumulative number of times that users triggered the jump component but failed to jump successfully in each operation chain abnormal channel during the historical task execution, and the ratio of the number of form loading failures to the total number of loading attempts, and marked them as and , Indicates the The cumulative number of times that users triggered the jump component but failed to jump successfully during the execution of historical tasks in the abnormal operation chain channel. Indicates the The ratio of the number of form load failures to the total number of load attempts in the historical task execution of the abnormal channel of the operation chain, , is a positive integer;

[0078] The user behavior tracking module and task execution log analysis engine integrated into the software system can be combined with channel identification and task record fields to achieve automatic acquisition of two types of data: "the cumulative number of times users triggered the jump component but failed to jump successfully in historical task executions in each abnormal operation chain channel" and "the ratio of the number of form load failures to the total number of load attempts." Specifically, the system will first identify the event records of users triggering the jump component during each task execution from the log data based on the behavioral definition of the operational task, and further compare the execution status field of the jump event (such as the status code, response tag, or jump result tag) to determine whether the jump is successful. For events marked as failed, the system will mark them as "unsuccessful jump" and accumulate them by channel dimension to obtain the number of jump failures corresponding to each abnormal operation chain channel. At the same time, during the page loading phase, the system will track fields such as resource request status, DOM rendering completion, or component initialization response results during the form module loading process. For loading failure events (such as loading timeout, resource missing, component inactivation, etc.), the system will record them as "loading failure events". By counting the total number of form loading attempts during the task execution process of this channel, the loading failure rate can be calculated. , which is the ratio of the number of form load failures to the total number of load attempts for that channel. The entire process is automated by the software system through event tag matching, field rule parsing, and channel behavior aggregation, without manual intervention. This ensures accurate data sources and clear structure, directly supporting the subsequent extraction of path stability information and calculation of the path blocking index.

[0079] Calculate the path blocking index of abnormal channels in each operation chain The specific calculation method is: The ratio of the number of form load failures to the total number of load attempts in the historical task execution of the abnormal channel of the operation chain Square it and add it to the number 1 to get the first part; The cumulative number of times that users triggered the jump component but failed to jump successfully during the execution of historical tasks in the abnormal operation chain channel Add it to the number 1 and take the natural logarithm to get the second part; multiply the first part by the second part, and the product is the first part. Path blocking index of abnormal channels in the operation chain ;

[0080] The specific calculation formula is as follows:

[0081]

[0082] Where, For the The path blocking index of abnormal channels in the operation chain.

[0083] Path blocking index The calculation formula takes into account two key dimensions: form loading failure rate and jump failure number, to measure the The severity of the interruption of the abnormal channel of the operation chain in the execution of the structure path. Among them, the number of jump failures It indicates the cumulative frequency of users failing to complete the jump operation during the task, which is the core indicator for measuring the frequency of structural chain interruption. The natural logarithm function is used to calculate the frequency of the interruption. On the one hand, it can maintain sensitive response under low failure counts, and on the other hand, it can avoid abnormal amplification caused by high failure values ​​and achieve smooth control of exponential convergence. It represents the proportion of system components that fail to load successfully after the user reaches the task page. Its square term Designed to amplify the nonlinear risk brought by high failure rate, that is, when the loading failure rate is close to 1, the sensitivity of the indicator is significantly improved. As a weighting factor, it ensures that this term is non-zero when the failure rate is zero, while also smoothly integrating the impact of the two dimensions. By multiplying these two components, the formula captures the combined risks of high-frequency failures and high loading failure rates, forming a comprehensive measure of path stability risk. This design is mathematically differentiable and exhibits positive output, making it suitable for weighted calculations and threshold comparisons in subsequent models, and exhibits good algorithmic stability and interpretability.

[0084] No. Path blocking index of abnormal channels in the operation chain The numerical value of directly reflects the strength of the structural path interruption risk of the channel during task execution, and is therefore highly correlated with "Assessing the degree of execution path interruption of each abnormal operation chain channel". Specifically, the larger the path blocking index, the higher the frequency or severity of jump failures and form loading failures in historical tasks in the channel, which means that the path stability is worse, the possibility of users completing the task operation chain in the channel is lower, and the execution path is more likely to be interrupted. On the contrary, if A smaller value indicates that the channel's overall performance during the jump and loading phases is stable, the execution chain is more continuous, and the path interruption level is low. Therefore, by calculating and comparing the path blocking index of each channel, we can quantitatively assess its structural interruption risk, thereby providing a key parameter basis for comprehensively determining the degree of interruption in the channel's execution path.

[0085] In this embodiment, the logic for obtaining the operation interruption coefficient of each abnormal channel of the operation chain is as follows:

[0086] The operation coherence information is extracted from the pre-processed execution behavior information, including the number of users who visited the task page but did not complete any key operations in each abnormal channel of the operation chain, the total number of users who visited the task page, and the cumulative number of times the user clicked submit but did not respond successfully, and marked them as 、 and , Indicates the The number of users who visited the task page but did not complete any key operations in the abnormal operation chain channel. Indicates the The total number of users who accessed the task page through abnormal channels in the operation chain, Indicates the The cumulative number of times users click submit but fail to respond successfully in abnormal channels of the operation chain. , is a positive integer;

[0087] The software system can automatically obtain and count three types of data: "number of users who visited the task page but did not complete any key operations", "total number of users who visited the task page", and "the cumulative number of users who clicked submit but did not respond successfully" through the user behavior tracking engine, task execution log database and event flow analysis module integrated in the software system, combined with channel identification and task identification. Specifically, the system will first identify all users who visited the task page through the first task within a specified time period according to the task page access event log. The user behavior records of the abnormal channels that enter the task page in the operation chain are collected, and the list of users who visited the task page is extracted based on the user's unique identifier. The total number of users is counted to form the "total number of users who visited the task page" The system then further analyzes whether these users have performed key action events predefined by the system, such as redirection, form filling, submitting, etc. For users who have not triggered any key action, the system marks them as "interrupted users" and accumulates the number of people to obtain the "number of users who visited the task page but did not complete any key action". At the same time, the system will capture all submit button trigger events in the task interaction behavior log, and determine whether the submission is successful by analyzing the response status field (such as server return code, submission result flag), and accumulate the records marked as failed to obtain the "cumulative number of times the user clicked submit but failed to respond successfully". The entire process is achieved through the software system, which implements logical association and aggregated statistics based on event timestamps, user session IDs, and operation type tags. This eliminates the need for human intervention, ensures data statistical accuracy, and forms structured behavioral data to provide data support for the calculation of subsequent operation interruption coefficients.

[0088] Calculate the operation interruption coefficient of each abnormal channel of the operation chain The specific calculation formula is: The number of users who visited the task page but did not complete any key operations in the abnormal operation chain channel The total number of users who visited the task page Divide by to get the midway interruption rate; then divide the The cumulative number of times users clicked submit but failed to respond successfully in abnormal channels of the operation chain Add it to 1 and take the natural logarithm; multiply the midway interruption rate by the natural logarithm, and the product is the first Operation interruption coefficient of abnormal channels in the operation chain ;

[0089] The specific calculation formula is as follows:

[0090]

[0091] Where, For the The operation interruption coefficient of abnormal channels in the operation chain.

[0092] Operation interruption factor The calculation formula is intended to comprehensively reflect the The risk level of user behavior interruption during task execution in an abnormal channel of the operation chain. The first term in the formula The mid-way interruption rate is the ratio of the number of users who visited the task page but did not complete any key operations to the total number of users who visited the task page. It is used to measure the proportion of users who did not perform any substantive operations after entering the task. The higher the ratio, the greater the possibility of user churn or abandonment of the operation. The number of submission failures The model uses a natural logarithm function to weight submission failure events using nonlinear amplification, providing a stronger risk response when failure frequencies are high while suppressing fluctuations caused by low failure frequencies. Multiplying these two factors means that the model not only considers users' tendency to fail to complete tasks (behavioral abandonment) but also considers triggering events that interrupt the path to completion (such as submission failure). This creates a comprehensive interruption coefficient that incorporates both the "potential interruption probability" and the "impact of failure at key nodes" to accurately reflect the risk level of interruption in the operational behavior path. This design makes the operation interruption coefficient distinguishable and interpretable across different channels, and facilitates its integration with the path blocking index in subsequent evaluation models for comprehensive judgment.

[0093] No. Operation interruption coefficient of abnormal channels in the operation chain The numerical value of directly reflects the possibility of task interruption in the user behavior path of this channel, and is therefore closely related to "assessing the degree of interruption in the execution path of each abnormal operation chain channel." The larger the operation interruption coefficient, the higher the risk that a higher proportion of users in this channel have not completed any key operations after visiting the task page, or that the submission action frequently fails. This indicates that the user exits the operation process midway or ultimately fails to complete the task. The behavior path is discontinuous, and the integrity of the operation chain is poor, which makes the task execution path easily interrupted. On the contrary, if the operation interruption coefficient is small, it means that most users can successfully execute the operation process, the behavioral path of task execution has good coherence and closed-loop characteristics, and the degree of path interruption is low. Therefore, the size of the operation interruption coefficient can be used as an important parameter to measure the degree of interruption in the execution path at the behavioral level, and provide a quantitative basis for the evaluation and grading of channel adaptation capabilities.

[0094] In this embodiment, based on the generated path blocking index of each abnormal channel of the operation chain and operation interruption coefficient Construct an execution interruption assessment model and generate the execution interruption index of each operation chain abnormal channel through weighted summation , the specific calculation formula is as follows:

[0095]

[0096] Where, For the The execution interruption index of abnormal channels in the operation chain, and are the path blocking indexes of abnormal channels in each operation chain and operation interruption coefficient The non-zero weight coefficient of .

[0097] In order to generate the execution interruption index of each abnormal channel of the operation chain, the software system obtains the path blocking index Operation interruption coefficient Based on this, an execution interruption assessment model is constructed and a weighted sum method is used to generate the first Execution interruption index for each channel The model uses a preset weighting coefficient and Regulate the influence of the two indexes respectively and meet the 、 as well as The constraints of , ensure that both dimensions make actual contributions to the final evaluation value. Among them, the weight coefficient Indicates the relative importance of path blocking to the risk of channel interruption, usually used to emphasize the impact of structural levels (such as jump failure, form loading failure) on task interruption; Indicates the weight of the operation interruption behavior on the risk assessment, which is used to characterize the risk of intervention failure caused by the inconsistency of user operation behavior. The system can set and dynamically adjust these two weight coefficients based on the historical task completion rate, model training feedback results or strategy configuration requirements. The weighted calculation is performed through the formula The output execution interruption index can be used as a core reference indicator for channel adaptability assessment and task path distribution control. The entire process is automatically executed by the system logic module and is configurable, updateable, and scalable.

[0098] In this embodiment, the predetermined execution interruption index threshold interval is determined. , and after determination, generate the execution interruption index of each operation chain abnormal channel Perform a comparison and evaluate the degree of interruption of the execution path of each abnormal operation chain channel based on the comparison results. Then, classify each abnormal operation chain channel based on the evaluation results. The specific comparison analysis and classification are as follows:

[0099] like ,The execution path interruption degree of the operation chain abnormal channel is low, and the operation chain abnormal channel is classified as a first-level channel;

[0100] This situation shows that the channel has good structural path stability and user operation consistency in historical tasks. In other words, users have a high success rate in completing key interactive operations such as jumping, loading, and submitting in this channel, the task operation chain is closed-loop complete, and the probability of behavior interruption is low. In actual health task promotion and intervention, using such channels to carry operational tasks can significantly improve the task completion rate, data return rate, and the stability of intervention effects. Therefore, the first-level channel is usually configured as the core delivery channel in the channel distribution strategy, especially for high-priority, high-response health action instructions or questionnaire data collection tasks.

[0101] like ,The execution path interruption degree of the operation chain abnormal channel is medium, and the operation chain abnormal channel is classified as a secondary channel;

[0102] This situation means that there is a certain degree of path instability or risk of user behavior interruption during task execution in this channel. Specifically, some users may encounter delays in the jump link, failures in form loading, or some failure records in the submission stage. Although the channel still has a certain task carrying capacity overall, the probability of interruption is relatively increased in scenarios with peak loads, complex content, or long user operation paths. In regional health promotion tasks, secondary channels can be used as supplementary channels. They are suitable for promotional tasks with high fault tolerance and appropriately extended response times, or as strategic diversion configurations when primary channel resources are insufficient.

[0103] like ,The execution path interruption degree of the operation chain exception channel is high, and the operation chain exception channel is divided into three levels.

[0104] This situation means that the channel has significant problems in both the structural chain and user operation behavior dimensions. This type of channel is often accompanied by frequent jump failures, form loading exceptions, and unsuccessful submissions. The user is likely to be interrupted in the operation chain, and the task completion rate is extremely low. In the practice of multi-channel publicity for regional health improvement, if the third-level channel is used for operational tasks, it is very likely to cause task delivery failure, data loss, and failure to achieve intervention goals. Therefore, this type of channel is usually not configured as a distribution channel for operational tasks. It can be used as a one-way notification channel for static content or reading information, or it can be reincorporated into the dynamic task carrying system after its capabilities are improved through subsequent optimization measures.

[0105] Configure multi-channel distribution paths for operational tasks based on the classification results, and dynamically adjust the channel distribution method;

[0106] In this embodiment, a multi-channel distribution path is configured for the operational task based on the classification results, specifically: the abnormal operation chain channel classified as the first-level channel is set as the main distribution channel for the operational task, which is used to give priority to the delivery of task content; the abnormal operation chain channel classified as the second-level channel is set as the backup distribution channel for the operational task, and when the distribution capacity of the first-level channel reaches the preset upper limit, the second-level channel is activated to supplement the task delivery. When the first-level channel fails to deliver, the second-level channel is activated to replace the task delivery; the abnormal operation chain channel classified as the third-level channel is removed from the distribution path of the operational task, and the task delivery to the third-level channel is prohibited;

[0107] This process can be automated by building a "channel level mapping module" and a "task distribution strategy engine" into the software system. After calculating the path blocking index and operation interruption coefficient for each abnormal operation chain channel, generating an execution interruption index based on this, and completing the grading process, the system maps each channel's grading label (level one, level two, level three) to its corresponding task distribution status. For channels marked as level one, the system includes them in the list of primary task delivery channels and prioritizes them for push during task scheduling. For level two channels, the system adds them to the backup channels and binds them to conditional triggers. When it detects that the distribution volume of a level one channel has reached a preset threshold, or when a failure response occurs during actual task scheduling (such as a delivery failure, timeout, or abnormal user feedback), the corresponding level two channel is automatically enabled for supplemental delivery or alternative execution. For level three channels, the system removes them from the task delivery strategy table and performs exclusionary filtering during strategy execution to ensure that tasks are not distributed to such channels. The entire process makes decisions based on channel status tags, task load status, feedback data, and scheduling rule logic flow. The task scheduling engine automatically controls according to the priority strategy, avoiding manual intervention and ensuring the efficiency and controllability of path configuration.

[0108] The need to configure multi-channel distribution paths for operational tasks based on channel grading stems from fundamental differences in the executable nature of task operational chains across different channels. Operational tasks typically involve multiple interactive steps, such as redirects, form completion, and data submission, placing high demands on the channel's execution capabilities. Indiscriminately distributing tasks equally across all channels could result in tasks being delivered to channels with insufficient execution capabilities, leading to operational disruptions, data loss, or user churn, impacting overall task completion rates and the effectiveness of health interventions. Through tiered configuration, the system prioritizes task delivery to primary channels with stable historical performance and consistent behavior, improving task delivery reliability. Secondary channels with moderate performance are conditionally included as candidates for participation, enhancing system resilience and fault tolerance. Completely eliminating high-risk tertiary channels effectively avoids task failures caused by structural execution failures. This tiered path configuration approach enables dynamic resource matching and precise distribution, improving task execution efficiency and success rates, and is a foundational element for intelligent, closed-loop health promotion task management.

[0109] The channel distribution method is dynamically regulated. Specifically, during the task execution process, based on the task response status, channel feedback data and user behavior change information, the following dynamic regulation operations are performed respectively: increase the task distribution frequency of the first-level channel, reduce the task distribution frequency of the second-level channel, and stop the task delivery of the third-level channel, so as to realize real-time regulation and optimization of the task distribution path.

[0110] Dynamic channel distribution can be achieved by integrating a "task monitoring engine," a "channel response analysis module," and an "adaptive distribution strategy scheduling component" into the software system. During task execution, the system continuously monitors each channel's task response status (e.g., delivery success, timeouts, user feedback), channel feedback data (e.g., delivery confirmation rate, user click-through rate, task completion rate), and user behavior changes (e.g., interaction path completeness and interruption rate fluctuations). All monitoring data is aggregated in real time into the scheduling component's strategy decision engine, which automatically adjusts the task distribution frequency for each channel based on predefined dynamic control rules. For example, if a primary channel's response success rate consistently exceeds a preset threshold, the system increases the distribution ratio for that channel; if a secondary channel experiences task execution fluctuations (e.g., abnormal feedback, declining click-through rate), the system reduces its participation frequency; and if a tertiary channel experiences multiple consecutive structural failures, the system dynamically freezes it, removing it from participating in task distribution. All control instructions are driven by the system's real-time data, logical judgment, and rules engine. The process is cyclical and automated, forming a closed-loop system for continuous optimization of task distribution paths.

[0111] In regional health improvement tasks, the effectiveness of task distribution will be affected by multiple factors such as channel operation status, user activity, and changes in the communication environment. Therefore, a single static path configuration is difficult to adapt to fluctuations in execution effects in the long term. Without dynamic regulation capabilities, even if the initial distribution path is reasonable, changes in user behavior or a decrease in channel stability may lead to decreased task execution efficiency, reduced user completion rates, or task freezes and failures. By introducing a dynamic regulation mechanism during the task execution phase, the system can adjust the distribution strategy in a timely manner based on actual delivery feedback and behavioral data, thereby maximizing the use of well-performing channel resources and avoiding further wasting resources and data traffic on high-risk channels. This mechanism not only improves the adaptability and responsiveness of task distribution, but also enhances the system's fault-tolerant correction capabilities for execution deviations through a feedback control model, achieving precise, multi-round, feedback-loop-driven distribution optimization, and ensuring that the completion rate of operational tasks and the health intervention effects are stable and controllable in the long term.

[0112] During the task execution process, the user's operation behavior feedback information in each channel is obtained, and based on the obtained operation behavior feedback information, the execution path interruption degree assessment results of the abnormal operation chain channel are updated to adjust the channel distribution method of subsequent tasks.

[0113] This process can be automated and closed-looped by configuring a "user behavior feedback collection module," "execution path dynamic evaluation engine," and "distribution strategy adjustment controller" within the software system. First, during task execution, the system uses a user-side embedded behavior tracking mechanism to collect real-time user behavior data from each task step across various channels, including jump trigger status, form load completion, input activity, and submission response results. All data is tagged with user ID, task ID, and channel ID and uploaded to the feedback analysis module in real time. The system then cleans, de-dupes, and normalizes this feedback data before matching it to the corresponding channel with an abnormal operation chain, generating the latest round of "channel operation behavior feedback information." The system then compares this feedback information with the channel's original path blocking index and operation interruption coefficient, triggering an incremental update mechanism within the path interruption assessment model to recalculate the execution interruption index for each abnormal operation chain channel. The updated index is immediately used in the distribution controller's grading decisions, adjusting the channel's distribution weight, role, and inclusion in the primary distribution path in subsequent task executions, achieving dynamic adaptive optimization.

[0114] Incorporating user behavior feedback and dynamically updating the degree of execution path interruption are core mechanisms for ensuring the long-term, effective operation of a multi-channel task delivery system. On the one hand, the execution quality of operational tasks is not static; different channels may exhibit dynamic fluctuations over time or for different task types. For example, changes in user interaction willingness, channel technology updates, and client anomalies can all affect task completion rates. On the other hand, static evaluation indices calculated solely based on historical behavior cannot accurately reflect the true state of current task execution, easily leading to distorted path evaluation and delayed delivery strategies. By collecting user operational feedback in real time and using it to update the evaluation model, the system can promptly identify changes in channel operational status, quickly respond to execution deviations, and form refined, dynamic control logic at the distribution control layer. This mechanism enables task delivery to be driven by real-time feedback rather than static predictions, significantly improving delivery success rates, user completion rates, and the efficiency of overall health intervention strategy execution. It is a key capability for achieving closed-loop management, intelligent optimization, and precise operations for regional health improvement.

[0115] like Figure 3 The multi-channel publicity and management system for regional health promotion shown includes a task type identification module, a channel anomaly identification module, a path interruption assessment module, a distribution path configuration module, and a feedback-driven update module;

[0116] The task type identification module identifies the content structure of the health promotion task, extracts its structural feature information, and analyzes whether the health promotion task is an operational task based on the extracted structural feature information;

[0117] The channel anomaly identification module, when the health promotion task is an operational task, screens out the channels where the task operation chain cannot be completed from all channels matching the task, and marks them as channels with abnormal operation chains;

[0118] The path interruption assessment module obtains and analyzes the execution behavior information of each abnormal operation chain channel, assesses the degree of execution path interruption of each abnormal operation chain channel, and classifies each abnormal operation chain channel based on the assessment results;

[0119] The distribution path configuration module configures multi-channel distribution paths for operational tasks based on the classification results and dynamically adjusts the channel distribution method;

[0120] The feedback-driven update module obtains the user's operation behavior feedback information in each channel during the task execution process, and updates the execution path interruption degree assessment results of the abnormal operation chain channel based on the obtained operation behavior feedback information.

[0121] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0122] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0123] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0128] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A multi-channel publicity and management method for regional health improvement, characterized by: The specific steps include: Identify the content structure of health promotion tasks, extract their structural feature information, and analyze whether the health promotion tasks are operational tasks based on the extracted structural feature information; If the health promotion task is an operational task, select the channels where the task operation chain cannot be completed from all channels matching the task, and mark them as abnormal operation chain channels; Obtain and analyze the execution behavior information of each abnormal operation chain channel, evaluate the degree of interruption of the execution path of each abnormal operation chain channel, and classify each abnormal operation chain channel based on the evaluation results; Specifically include: Obtain the execution behavior information of each abnormal channel of the operation chain and pre-process the obtained execution behavior information; Extracting path stability information and operation coherence information from the pre-processed execution behavior information, and generating the path blocking index and operation interruption coefficient of each abnormal channel of the operation chain based on the extracted information; The logic for obtaining the path blocking index of each abnormal channel of the operation chain is as follows: Path stability information is extracted from the pre-processed execution behavior information, including the cumulative number of times that users triggered the jump component but failed to jump successfully in each operation chain abnormal channel during the historical task execution, and the ratio of the number of form loading failures to the total number of loading attempts, and marked them as and , Indicates the The cumulative number of times that users triggered the jump component but failed to jump successfully during the execution of historical tasks in the abnormal operation chain channel. Indicates the The ratio of the number of form load failures to the total number of load attempts in the historical task execution of the abnormal channel of the operation chain, , is a positive integer; Calculate the path blocking index of abnormal channels in each operation chain The specific calculation method is: The ratio of the number of form load failures to the total number of load attempts in the historical task execution of the abnormal channel of the operation chain Square it and add it to the number 1 to get the first part; The cumulative number of times that users triggered the jump component but failed to jump successfully during the execution of historical tasks in the abnormal operation chain channel Add it to the number 1 and take the natural logarithm to get the second part; multiply the first part by the second part, and the product is the first part. Path blocking index of abnormal channels in the operation chain ; The logic for obtaining the operation interruption coefficient of each abnormal channel of the operation chain is as follows: The operation coherence information is extracted from the pre-processed execution behavior information, including the number of users who visited the task page but did not complete any key operations in each abnormal channel of the operation chain, the total number of users who visited the task page, and the cumulative number of times the user clicked submit but did not respond successfully, and marked them as 、 and , Indicates the The number of users who visited the task page but did not complete any key operations in the abnormal operation chain channel. Indicates the The total number of users who accessed the task page through abnormal channels in the operation chain, Indicates the The cumulative number of times users click submit but fail to respond successfully in abnormal channels of the operation chain. , is a positive integer; Calculate the operation interruption coefficient of each abnormal channel of the operation chain The specific calculation formula is: The number of users who visited the task page but did not complete any key operations in the abnormal operation chain channel The total number of users who visited the task page Divide by to get the midway interruption rate; then divide the The cumulative number of times users clicked submit but failed to respond successfully in abnormal channels of the operation chain Add it to 1 and take the natural logarithm; multiply the midway interruption rate by the natural logarithm, and the product is the first Operation interruption coefficient of abnormal channels in the operation chain ; Based on the generated path blocking index and operation interruption coefficient of each abnormal operation chain channel, an execution interruption evaluation model is constructed to generate the execution interruption index of each abnormal operation chain channel; Determine a pre-set execution interruption index threshold range, and compare it with the generated execution interruption index of each abnormal operation chain channel after determination. Based on the comparison results, evaluate the execution path interruption degree of each abnormal operation chain channel, and classify each abnormal operation chain channel according to the evaluation results; Configure multi-channel distribution paths for operational tasks based on the classification results, and dynamically adjust the channel distribution method; During the task execution process, the user's operation behavior feedback information in each channel is obtained, and the execution path interruption degree assessment result of the operation chain abnormal channel is updated based on the obtained operation behavior feedback information.

2. The multi-channel publicity and management method for regional health improvement according to claim 1 is characterized in that: When the health promotion task is an operational task, select the channels where the task operation chain cannot be completed from all channels matching the task and mark them as abnormal operation chain channels. Specifically: If the health promotion task is an operational task, identify all channels that match the health promotion task; For each channel matching the health promotion task, call the execution data of the same type of tasks in the channel to determine whether there is at least one of the following uncompleted behavior records: jump failure, form loading failure, and task submission failure. If there is at least one uncompleted behavior record, filter the channel as the channel where the task operation chain cannot be completed; The filtered channels where the task operation chain cannot be completed are marked as abnormal operation chain channels.

3. The multi-channel publicity and management method for regional health improvement according to claim 2 is characterized in that: Path blocking index based on the generated abnormal channels of each operation chain and operation interruption coefficient Construct an execution interruption assessment model and generate the execution interruption index of each operation chain abnormal channel through weighted summation .

4. The multi-channel publicity and management method for regional health improvement according to claim 3 is characterized in that: Determine the pre-set execution interruption index threshold range , and after determination, generate the execution interruption index of each operation chain abnormal channel Perform a comparison and evaluate the degree of interruption of the execution path of each abnormal operation chain channel based on the comparison results. Then, classify each abnormal operation chain channel based on the evaluation results. The specific comparison analysis and classification are as follows: like ,The execution path interruption degree of the operation chain abnormal channel is low, and the operation chain abnormal channel is classified as a first-level channel; like ,The execution path interruption degree of the operation chain abnormal channel is medium, and the operation chain abnormal channel is classified as a secondary channel; like ,The execution path interruption degree of the operation chain exception channel is high, and the operation chain exception channel is divided into three levels.

5. The multi-channel publicity and management method for regional health improvement according to claim 4 is characterized in that: Configure multi-channel distribution paths for operational tasks based on the grading results, specifically: set the abnormal operation chain channel classified as the first-level channel as the main distribution channel for the operational task, which is used to give priority to the delivery of task content; set the abnormal operation chain channel classified as the second-level channel as the backup distribution channel for the operational task, and activate the second-level channel to supplement the task delivery when the distribution capacity of the first-level channel reaches the preset upper limit. When the first-level channel fails to deliver, activate the second-level channel to perform the task delivery instead; remove the abnormal operation chain channel classified as the third-level channel from the distribution path of the operational task, and prohibit the delivery of tasks to the third-level channel; The channel distribution method is dynamically regulated. Specifically, during the task execution process, based on the task response status, channel feedback data and user behavior change information, the following dynamic regulation operations are performed respectively: increase the task distribution frequency of the first-level channel, reduce the task distribution frequency of the second-level channel, and stop the task delivery of the third-level channel, so as to realize real-time regulation and optimization of the task distribution path.

6. A multi-channel publicity and management system for regional health promotion, used to implement the multi-channel publicity and management method for regional health promotion as described in any one of claims 1 to 5, characterized in that: It includes a task type identification module, a channel anomaly identification module, a path interruption assessment module, a distribution path configuration module, and a feedback-driven update module; The task type identification module identifies the content structure of the health promotion task, extracts its structural feature information, and analyzes whether the health promotion task is an operational task based on the extracted structural feature information; The channel anomaly identification module, when the health promotion task is an operational task, screens out the channels where the task operation chain cannot be completed from all channels matching the task, and marks them as channels with abnormal operation chains; The path interruption assessment module obtains and analyzes the execution behavior information of each abnormal operation chain channel, assesses the degree of execution path interruption of each abnormal operation chain channel, and classifies each abnormal operation chain channel based on the assessment results; The distribution path configuration module configures multi-channel distribution paths for operational tasks based on the classification results and dynamically adjusts the channel distribution method; The feedback-driven update module obtains the user's operation behavior feedback information in each channel during the task execution process, and updates the execution path interruption degree assessment results of the abnormal operation chain channel based on the obtained operation behavior feedback information.

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