Task processing method and device

Abstract

The embodiment of the invention provides a task processing method and device.The task processing method comprises the steps that in the process of processing a task accessed based on a program, firstly, a task access index is calculated according to task access data, and under the condition that the task access index meets an index detection condition, the task access index is detected; and performing abnormal access identification on the task according to the subordinate index of the task access index and the corresponding abnormal identification strategy to obtain an abnormal task node and an abnormal access category, and finally performing task updating processing according to the abnormal task node and the abnormal access category so as to realize updating processing on the task accessed based on the program.

Description

Technical Field

[0001] This document relates to the field of data processing technologies, and in particular, to a task processing method and apparatus. Background Art

[0002] With the development of Internet technologies, more and more users participate in tasks online, and more and more service providers guide users to participate in tasks online. For example, user information for participating in services is obtained by means of online information entry, avoiding the need for users to fill in information offline and go to a designated location to submit the information, saving the efficiency of users' task participation and also improving the efficiency and convenience of service providers in aggregating user information, providing convenience for both users and service providers. Summary of the Invention

[0003] One or more embodiments of this specification provide a task processing method, including: calculating a task access metric based on task access data for task access based on a program. If the task access metric meets the corresponding metric detection condition, reading subordinate metrics of the task access metric. Performing abnormal access identification on the task according to the subordinate metrics and an abnormal identification strategy corresponding to the task access metric to obtain abnormal task nodes and an abnormal access category. Performing task update processing according to the abnormal task nodes and the abnormal access category.

[0004] One or more embodiments of this specification provide a task processing apparatus, including: a task access metric calculation module configured to calculate a task access metric based on task access data for task access based on a program. If the task access metric meets the corresponding metric detection condition, running a subordinate metric reading module, the subordinate metric reading module being configured to read subordinate metrics of the task access metric. An abnormal access identification module configured to perform abnormal access identification on the task according to the subordinate metrics and an abnormal identification strategy corresponding to the task access metric to obtain abnormal task nodes and an abnormal access category. A task update processing module configured to perform task update processing according to the abnormal task nodes and the abnormal access category.

[0005] One or more embodiments of this specification provide a task processing device, including: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: calculate a task access metric based on task access data for task access based on a program. If the task access metric meets the corresponding metric detection condition, read subordinate metrics of the task access metric. Perform abnormal access identification on the task according to the subordinate metrics and an abnormal identification strategy corresponding to the task access metric to obtain abnormal task nodes and an abnormal access category. Perform task update processing according to the abnormal task nodes and the abnormal access category.

[0006] One or more embodiments of this specification provide a computer-readable storage medium for storing computer-executable instructions, and when the computer-executable instructions are executed, the following processes are implemented: calculating a task access metric based on task access data for accessing a task according to a program. If the task access metric meets the corresponding metric detection condition, reading the subordinate metric of the task access metric. Performing abnormal access recognition on the task according to the subordinate metric and the abnormal recognition strategy corresponding to the task access metric to obtain an abnormal task node and an abnormal access category. Performing task update processing according to the abnormal task node and the abnormal access category. Description of the Drawings

[0007] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings; Figure 1 It is a schematic diagram of an implementation environment of a task processing method provided by one or more embodiments of this specification; Figure 2 It is a processing flow chart of a task processing method provided by one or more embodiments of this specification; Figure 3 It is a schematic diagram of a task execution process provided by one or more embodiments of this specification; Figure 4 It is a processing flow chart of a task processing method applied to a task processing scenario based on a web program provided by one or more embodiments of this specification; Figure 5 It is a processing flow chart of a task processing method applied to a task processing scenario based on a web service provided by one or more embodiments of this specification; Figure 6 It is a processing flow chart of a task processing method applied to a task processing scenario based on an application program provided by one or more embodiments of this specification; Figure 7 It is a schematic diagram of an embodiment of a task processing device provided by one or more embodiments of this specification; Figure 8 It is a schematic diagram of the structure of a task processing device provided by one or more embodiments of this specification. Detailed Embodiments

[0008] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0009] The task processing method provided by one or more embodiments of this specification is applicable to the implementation environment of task access based on a program. Referring to Figure 1 , this implementation environment at least includes: a terminal device 101 and a server 102; Among them, the terminal device 101 is used to interact with the user, enabling the user to perform task access based on the program running on the terminal device 101. The terminal device 101 can specifically be a mobile phone, a personal computer, a tablet computer, an e-book reader, a device for information interaction based on VR (Virtual Reality), a vehicle-mounted terminal, an IoT device, a wearable intelligent device, a laptop computer, a desktop computer, and so on; The server 102 is used to calculate the task access metrics and subordinate metrics during the task access process of the terminal device 101 and perform task processing. The server 102 can be a single server, or a server cluster composed of several servers, or one or more cloud servers in a cloud computing platform; In addition, this implementation environment may further include a processing terminal 103, which is used to remind the abnormal task nodes and abnormal access categories obtained by the server 102 during task processing, enabling the processing user to perform task update processing according to the abnormal task nodes and abnormal access categories. The processing terminal 103 can specifically be a mobile phone, a personal computer, a tablet computer, an e-book reader, a device for information interaction based on VR (Virtual Reality), a vehicle-mounted terminal, an IoT device, a wearable intelligent device, a laptop computer, a desktop computer, and so on; In this implementation environment, the user accesses or executes tasks through the program running on the terminal device 101. The server 102 calculates the task access metrics based on the task access data generated during the process of accessing or executing tasks through the program running on the terminal device 101. If the task access metrics meet the corresponding metric detection conditions, the subordinate metrics of the task access metrics are read, and the task is identified for abnormal access according to the subordinate metrics and the abnormal identification strategy corresponding to the task access metrics, obtaining the abnormal task nodes and the abnormal access categories, and performing task update processing according to the abnormal task nodes and the abnormal access categories. In this way, starting from the task access metrics and the subordinate metrics, the abnormal task nodes and the abnormal access categories are determined, and then the task update processing is performed according to the abnormal task nodes and the abnormal access categories, realizing the effective update of the task.

[0010] It should be noted that considering that the task access data, subordinate metrics and other related data involved in this specification may, to a certain extent, belong to the privacy of the task provider, and the data related to the user may also, to a certain extent, belong to the privacy of the user. Therefore, to collect and process the task access data, subordinate metrics and other related data, the authorization of the task provider can be obtained before collecting the data, so that the operation of collecting and processing the data complies with relevant data management regulations. For example, data authorization can be carried out before the task provider publishes the task, or data authorization can be carried out when calculating the task access metrics, or data authorization can be carried out before the user accesses the task; the specific method of data authorization can be to send a data authorization reminder to the user or the task provider, and the user or the task provider can obtain the data collection authorization and processing authorization after confirming the reminder through an instruction. Or, the method of data authorization can also be to obtain the data collection and processing authorization by signing a data authorization agreement.

[0011] One or more embodiments of a task processing method provided in this specification are as follows: Refer to Figure 2 , the task processing method provided in this embodiment, the method specifically includes steps S202 to S208.

[0012] Step S202, calculate the task access metrics according to the task access data for task access based on the program.

[0013] In this embodiment, by performing task update processing on tasks that access tasks based on a program, the effectiveness of the tasks is improved, and thus the user's participation in the tasks and even in the program is also improved. Optionally, the program includes an application program and / or a web program. During the process of task access, task access can be performed based on the program page of the program. Optionally, the program page includes an application page of the application program and / or a web page of the web program, that is, a web page. In addition, the program can also include a system program. In the case where the program includes a system program, the program page can also be the system page of the system program. It should be noted that in addition to accessing tasks based on a program, tasks can also be accessed based on a service, such as accessing tasks based on a web service. That is, the program in this embodiment can also be a web-based product.

[0014] In practical applications, users, service providers, or institutions participate in or complete tasks by accessing or executing tasks through a program, thereby achieving corresponding purposes. The tasks in this embodiment include interaction or processing logic designed to achieve a purpose. For example, an information collection task for collecting information through a web-based product, or an information display task for displaying information through a web-based product. In addition, task configuration can also be performed according to actual needs, such as a management task for service management through a system program. During the process of task execution, in order to improve the perceived degree of task progress, at least one task node can be configured for the task. For example, if A—B—C—D—E is a task, then this task includes 5 task nodes, where A, B, C, D, and E are the task nodes of this task. Optionally, the task node can be a program page. Different task nodes of the task are entered by switching the program page. In addition, the task node can also be a control or an input item. For example, in the information collection task A—B—C—D—E, the A task node is a name input item, the B task node is a time input item, the C task node is an associated name input item, the D task node is an evaluation input item, and the E node is a supplementary information input item. Or, the task node can also be time. For example, in the information display task A—B—C—D—E, the A task node is to display information for 10 minutes, the B task node is to display information for 20 minutes, and so on.

[0015] In this embodiment, in order to improve the effectiveness of tasks for design or configuration, and thus enhance the perception of tasks, the quality of task access is detected through task access metrics, that is, whether the task needs to be updated is detected by whether the task access metrics meet the metric detection conditions; the task access metrics include metrics configured to evaluate the quality of task access. Optionally, the task access metrics include at least one of the following: the task conversion rate of users completing task execution through the program, the execution loss rate of users performing tasks through the program, and the average access duration of nodes for users to access tasks through the program.

[0016] In specific implementation, the tasks can be evaluated from three task access metrics: task conversion rate, execution loss rate, and average access duration of nodes. Among them, the conversion situation of tasks is evaluated through the task conversion rate, and the design rationality between task nodes, that is, between steps (one step from the previous task node to the next task node, for example, A - B), is evaluated through the execution loss rate, and the design rationality of a single task node, that is, a single step, is evaluated through the average access duration of nodes. It should be noted that whether it is the task conversion rate, or the execution loss rate and the average access duration of nodes, they are all for improving the task conversion rate. Since the task conversion rate only evaluates the task conversion situation and cannot evaluate the reasons for the low task conversion rate, in this embodiment, the execution loss rate and the average access duration of nodes are introduced to evaluate the reasons for task conversion; on the other hand, for some special tasks, the task conversion rate may be relatively high due to few alternative tasks, but there are often some problems with the tasks themselves. In order to achieve more comprehensive task updates, the execution loss rate and the average access duration of nodes are also introduced as task access metrics. In the specific execution process, the task access metrics are calculated based on the task access data for task access through the program, so as to obtain the task access metrics for evaluating the task conversion rate; since the task access metrics include the task conversion rate, the execution loss rate, and / or the average access duration of nodes, different target access data are used in the calculation process of different task access metrics. In an optional implementation manner provided in this embodiment, in the process of calculating the task access metrics based on the task access data for task access through the program, the following operations are performed: Read the target access data corresponding to the task access metrics from the task access data obtained by accessing tasks based on the program page. Calculate the metric values of the task access metrics according to the target access data.

[0017] That is, in the process of calculating the task access metric based on the task access data of task access according to a program, the metric value of the task access metric, that is, the metric parameter, is calculated according to the target access data of task access based on the program corresponding to the task access metric. It should be noted that in the process of calculating the metric value of the task access metric according to the task access data corresponding to the task access metric, the task access metric can be the metric name. For example, the task access metric is "task conversion rate" or "execution loss rate" or "average access duration of nodes"; and the task access metric calculated according to the task access data of task access based on the program can be the metric value of the task access metric; or it can also be understood that the task access metric includes the metric name and the metric value, that is, the task access metric is "task conversion rate 40%" or "execution loss rate 5%" or "average access duration of nodes t"; that is, the task access metric in this embodiment can be only the metric name, or can include the metric name and the metric parameter, and this embodiment does not make a limitation here. The above description has been made on the process of calculating the task access metric in the process of calculating the task access metric according to the task access data of task access based on the program by calculating the task access metric according to the corresponding target access data of task access based on the program. Optionally, the target access data includes at least one of the following: The number of first users of the first task node of the access task and the number of second users who complete the task; The number of deduplicated access nodes and the actual number of access nodes for task access; The task access duration and the number of access nodes for each task access.

[0018] Optionally, the number of second users includes the number of second users of the last task node who complete the task.

[0019] That is, the process of calculating the task access metric in the process of calculating the task access metric according to the task access data of task access based on the program includes: calculating the task conversion rate according to the number of first users of the first task node of the access task and the number of second users who complete the task, calculating the execution loss rate according to the number of deduplicated access nodes and the actual number of access nodes for task access, and / or calculating the average access duration of nodes according to the task access duration and the number of access nodes.

[0020] For example, for task A - B - C - D - E, the task conversion rate is calculated in the following way: Task conversion rate = number of users who complete node E / number of users who complete node A.

[0021] For another example, for task A - B - C - D - E, the execution loss rate L is calculated in the following way:

[0022] Wherein, N is the number of different nodes actually accessed by the user during the process of completing a task, that is, the number of deduplicated nodes, such as the number of different pages or steps; S is the total number of nodes accessed by the user during the process of completing a task, that is, the actual number of nodes, such as the total number of pages or steps accessed, including repeatedly accessed nodes. If multiple execution loss degrees are calculated, the 75th percentile can be determined.

[0023] For another example, for task A - B - C - D - E, calculate the average access duration of the nodes accessed by each task according to the task access duration and the number of accessed nodes of each task, that is, calculate the quotient of the task access duration and the number of accessed nodes of each task as the average access duration of the nodes accessed by each task, and then determine the 75th percentile of the average access duration of the nodes accessed by multiple tasks as the average access duration of the nodes of the task.

[0024] In specific implementation, after calculating the task access metrics based on the task access data of task access based on a program, in order to ensure the effectiveness of task update processing, configure metric detection conditions for the task access metrics. After calculating the task access metrics, detect whether the task access metrics meet the metric detection conditions. If not, no processing is required. If so, read the subordinate metrics of the task access metrics. Optionally, the metric detection conditions include at least one of the following: less than the conversion rate threshold, greater than the loss degree threshold, greater than the duration threshold. Specifically, if the task conversion rate is less than the conversion rate threshold, it is determined that the task conversion rate meets the metric detection conditions, that is, read the subordinate metrics of the task access metrics; if the execution loss degree is greater than the loss degree threshold, it is determined that the execution loss degree meets the metric detection conditions; if the average access duration of the nodes is greater than the duration threshold, it is determined that the average access duration of the nodes meets the metric detection conditions.

[0025] In addition, the calculation of task access metrics can also be performed in real time, and the detection of task access metrics can be performed according to a period, that is, the calculation of task access metrics is not continuous with the process of task processing. Step S202 can also be replaced by detecting the task access metrics calculated from the task access data of task access based on a program, and forming a new implementation manner with one or more other processing steps provided in this embodiment; optionally, the task access metrics are calculated and stored in advance.

[0026] It should be noted that when calculating task access metrics, any task access metric can be calculated, or at least one task access metric can be calculated. That is, step S202 can also be replaced by calculating at least one task access metric according to the task access data of task access based on a program, and forming a new implementation manner with one or more other processing steps provided in this embodiment.

[0027] Step S204, if the task access metric meets the corresponding metric detection condition, read the subordinate metrics of the task access metric.

[0028] In specific implementation, to improve the accuracy of task update processing starting from task access metrics, after calculating the task access metrics for task access based on a program, if the task access metric meets the corresponding metric detection condition, read the subordinate metrics of the task access metric.

[0029] In this embodiment, by configuring subordinate metrics for the task access metric, when the task access metric meets the corresponding metric detection condition, the abnormal task nodes and abnormal access categories that cause the task access metric to meet the metric detection condition are determined with the help of the subordinate metrics; Optionally, the subordinate metrics include at least one of the following: The number of user participations in each task node; The number of loop times, cross jumps, repeat times, and / or bounce times; The operation metrics and running metrics of each task node.

[0030] Specifically, the subordinate metric of the task conversion rate includes the number of user participations in each task node, the subordinate metric of the execution loss degree includes the number of loop times, cross jumps, repeat times, and / or bounce times, and the subordinate metric of the average access duration of a node includes the operation metrics, running metrics, and node operation loss metric of each task node.

[0031] Optionally, the node operation loss metric is:

[0032] where n is the number of deduplication operations in the corresponding node, and s is the actual total number of operations.

[0033] It should be noted that the above descriptions of the subordinate metrics for each task access metric are only exemplary, and the configuration of each task access metric and the configuration of the subordinate metrics of each task access metric can also be carried out according to actual needs, which are not limited in this embodiment.

[0034] In addition, step S204 can also be replaced by reading the subordinate metrics of the task access metric and forming a new implementation manner with one or more other processing steps provided in this embodiment.

[0035] It should be noted that the above steps S202 to S204 can also be replaced by obtaining task access metrics that meet the corresponding metric detection conditions, reading the subordinate metrics of the task access metrics, and forming a new implementation manner with one or more other processing steps provided in this embodiment; optionally, the task access metrics are calculated based on task access data for task access based on a program; or, steps S202 to S204 can also be replaced by, when it is detected that the task access metrics calculated based on task access data for task access based on a program meet the corresponding metric detection conditions, reading the subordinate metrics of the task access metrics, and forming a new implementation manner with one or more other processing steps provided in this embodiment; or, the data metrics can also be directly read, that is, steps S202 to S204 can also be replaced by obtaining the subordinate metrics of task access metrics that meet the corresponding metric detection conditions, and forming a new implementation manner with one or more other processing steps provided in this embodiment; optionally, the task access metrics are calculated based on task access data for task access based on a program.

[0036] It should also be noted that when the above step S202 is replaced by calculating at least one task access metric based on task access data for task access based on a program, step S204 can also be replaced by determining a target task access metric that meets the corresponding metric detection condition among the at least one task access metric and reading the subordinate metric of the target task access metric. Correspondingly, step S206 can also be replaced by performing abnormal access recognition on the task according to the subordinate metric of the target task access metric and the corresponding abnormal recognition strategy, obtaining abnormal task nodes and abnormal access categories, and forming a new implementation manner with step S208, or it can also be composed of the replaced steps S202, S204, and S206 to form a new implementation manner.

[0037] Step S206, perform abnormal access recognition on the task according to the subordinate metric and the abnormal recognition strategy corresponding to the task access metric, and obtain abnormal task nodes and abnormal access categories.

[0038] In this embodiment, in order to achieve accurate task update processing under different task access metrics, when the task access metric meets the corresponding metric detection condition, by identifying abnormal task nodes and abnormal access categories under the task access metric, accurate identification of task anomalies is achieved, and the convenience of task update processing is also improved.

[0039] The abnormal task node includes a task node where an abnormality occurs during the task access process; optionally, the abnormal task node includes at least one of the following: an application page with a user bounce rate greater than the bounce rate threshold during the process of task execution based on the application page of the application; a web page where the page loading metric and / or page operation metric fails the detection during the task access process based on the web page of the web program; a web page or web page that is repeatedly accessed during the process of task execution based on the web page or web page. In addition, the abnormal task node can also be an application page with a user bounce rate greater than the bounce rate threshold during the process of task execution based on the web page; besides the program page, the abnormal task node can also be a control or input item. For example, an input item with a user bounce rate greater than the bounce rate threshold during the process of task execution based on the application page of the application. This embodiment will not elaborate here. It should be noted that the above application program and web program can be replaced with each other; optionally, the abnormal task node can also include at least one of the following: an interaction page with a user bounce rate greater than the bounce rate threshold during the process of task execution based on the interaction page of the web-based product, an interaction page where the page loading metric and / or page operation metric fails the detection during the task access process based on the interaction page of the web-based product, an interaction page that is repeatedly accessed, horizontally jumped, looped, and / or bounced during the process of task execution based on the interaction page of the web-based product.

[0040] The abnormality that occurs during the task access process may be caused by abnormalities in interaction dimensions such as node bounce and node repetition, or may be caused by abnormalities in the node operation dimension. Therefore, by identifying the category of abnormal access, the cause of the abnormality can be obtained, thereby improving the accuracy of abnormal access identification; that is, the category of abnormal access is used to represent the cause of the abnormality.

[0041] Since the determination of abnormal task nodes and abnormal access categories starts from task access metrics, in order to improve the effectiveness and accuracy of abnormal task nodes and abnormal access categories under the obtained task access metrics, corresponding abnormal identification methods, that is, abnormal identification strategies, can be configured for different task access metrics to achieve the identification of abnormal task nodes and abnormal access categories under the task access metrics; in this embodiment, the abnormal identification strategy corresponding to the task access metric includes the method of identifying abnormal task nodes and abnormal access categories under the task access metric.

[0042] In specific implementation, the task is identified for abnormal access according to the abnormal identification strategy corresponding to the subordinate indicator and the task access indicator, and the abnormal task node and the abnormal access category are obtained. In the first optional implementation manner provided in this embodiment, in the process of identifying the task for abnormal access according to the abnormal identification strategy corresponding to the subordinate indicator and the task access indicator to obtain the abnormal task node and the abnormal access category, first, according to the abnormal determination strategy corresponding to the subordinate indicator and the task access indicator, the abnormal task node and the associated indicator are determined among at least one task node, and then the abnormal access category is determined according to the associated indicator.

[0043] Optionally, the abnormal determination strategy includes: calculating the node abnormal indicator of each task node according to the subordinate indicator of the task access indicator, and determining the abnormal task node among at least one task node according to the node abnormal indicator, and reading the associated indicator of the abnormal task node under the task access indicator; or determining the target subordinate indicator according to the subordinate indicator of the task access indicator and the benchmark threshold, reading the associated indicator of the target subordinate indicator, and determining the abnormal task node according to the associated indicator.

[0044] For example, calculate the node bounce rate of each task node (i.e., each program page) according to the subordinate indicator of the task conversion rate, determine the abnormal task node with a node bounce rate higher than the bounce rate threshold among at least one task node according to the node bounce rate of each task node, and read the operation indicator and the running indicator of each abnormal task node as the associated indicator according to the task conversion rate.

[0045] For another example, determine the target subordinate indicator according to the subordinate indicator of the execution loss degree and the benchmark threshold, read the associated indicator of the target subordinate indicator, and determine the abnormal task node according to the associated indicator.

[0046] On the basis of determining the abnormal task node and the associated indicator, the abnormal access category can be determined according to the associated indicator. Specifically, the indicator category to which the abnormal associated indicator in the associated indicator belongs can be used as the abnormal access category. For example, the associated indicators include the average operation duration of the node, the average number of operations of the node under the operation category, and the number of node js (JavaScript) exceptions, the number of node interface exceptions, and the number of node white screen exceptions under the running category. If the average number of operations of the node is large, it is determined that the abnormal access category is the operation category to which the average number of operations of the node belongs; if the number of node interface exceptions is large, it is determined that the abnormal access category is the running category to which the number of node interface exceptions belongs; in addition, the running category can also include the interface performance score and the operation performance score. The interface performance score can be determined according to the interface response time. The longer the interface response time, the lower the interface performance score, and the shorter the interface response time, the higher the interface performance score; the operation performance score can be calculated according to the number of operations. The more the number of operations, the lower the operation performance score, and the fewer the number of operations, the higher the operation performance score.

[0047] Alternatively, the associated metrics can also be classified by metric category to obtain the category-associated metrics for each metric category, and the abnormal access category can be determined based on the category-associated metrics for each metric category. Specifically, when obtaining the category-associated metrics for each metric category, the abnormal access category can be determined based on the metric value distribution of the category-associated metrics for each metric category. The specific processing method of the category-associated metrics in this embodiment is similar to the processing method of the following category-subordinate metrics, and will not be elaborated herein. In addition, the associated metric can be directly used as the abnormal access category. For example, if the target subordinate metric is the number of repetitions, and the associated metric of the read target subordinate metric is the repeated node, then the repetition is directly used as the abnormal access category.

[0048] In another alternative embodiment provided by this embodiment, when performing abnormal access recognition on a task according to the abnormal recognition strategy corresponding to the subordinate metric and the task access metric to obtain the abnormal task node and the abnormal access category, the subordinate metrics of the task access metric can be first classified by metric category to obtain the category-subordinate metrics for each metric category, and then the abnormal task node and the abnormal access category can be determined based on the category-subordinate metrics for each metric category.

[0049] Specifically, the subordinate metrics of the task access metric can be first classified by metric category to obtain the category-subordinate metrics for each metric category, then the abnormal access category can be determined in at least one metric category according to the metric value distribution of the category-subordinate metrics for each metric category, and then the abnormal task node can be determined according to the metric values of the category-subordinate metrics of each task node under the abnormal access category.

[0050] For example, the subordinate metrics of the task access metric are classified by metric category to obtain the category-subordinate metrics under the operation category and the category-subordinate metrics under the running category. Then, according to the metric value distribution of the category-subordinate metrics under the operation category and the metric value distribution of the category-subordinate metrics under the running category, the abnormal access category is determined in the operation category and the running distribution, and then the task node corresponding to the category-subordinate metric with a metric value greater than the value threshold under the abnormal access category is determined as the abnormal task node.

[0051] In the specific execution process, the task access metric includes the task conversion rate, the execution loss rate, and / or the average access duration of nodes. The following will specifically describe the abnormal access recognition process under these three task access metrics.

[0052] In the first alternative embodiment provided by this embodiment, when performing abnormal recognition access on a task according to the abnormal recognition strategy corresponding to the subordinate metric and the task access metric to obtain the abnormal task node and the abnormal access category, the following operations are performed: Calculate the node anomaly index for each task node according to the subordinate index of the task access index, and determine the abnormal task node among at least one task node according to the node anomaly index; Read the associated index of the abnormal task node under the task access index, and determine the abnormal access category according to the associated index.

[0053] Optionally, the task access index includes the task conversion rate at which the user completes task execution through the program; the subordinate index of the task conversion rate includes the number of user participations in each task node.

[0054] Specifically, calculate the user bounce rate of each task node according to the number of user participations in adjacent task nodes, and determine the bounce node with a user bounce rate greater than the bounce rate threshold as the abnormal task node. Read the operation index and running index of the bounce node, and perform index detection on the operation index (index under the operation category) and the running index (index under the running category). Determine the abnormal access category according to the index detection result. Optionally, the adjacent task nodes include each task node and the next task node.

[0055] For example, the index relationship of the task access index of the task conversion rate is shown in Table 1:

[0056] Table 1 Among them, the average node operation duration can calculate the average node operation duration of each node access according to the total duration of each node access of the corresponding node and the total number of operations of the corresponding node, and then determine the 75th percentile of the average node operation duration of multiple node accesses as the average node access duration of the corresponding node; For tasks A - B - C - D - E, when it is detected that the task conversion rate is less than the conversion rate threshold, read the number of user participations in each task node, and take (|Number of user participations in the next task node - Number of user participations in the task node| / Number of user participations in the task node) * 100% as the user bounce rate of the task node. After detection, the user bounce rates of task node B and task node C are greater than the bounce rate threshold. Then, determine task node B, task node C, and task node D, which are involved in calculating the user bounce rates of task node B and task node C, as abnormal task nodes, and respectively read the average node operation duration, average node operation number, number of node js exceptions, number of node interface exceptions, and number of node white screen exceptions of task node B, task node C, and task node D as associated indexes; On the basis of obtaining the associated metrics, determine the abnormal access category according to the metric value distribution of the category subordinate metrics under each metric category. Specifically, if the category subordinate metrics under the operation category are large or the number of category subordinate metrics greater than the numerical threshold is large, then determine the operation category as the abnormal access category, and / or, if the category subordinate metrics under the running category are large or the number of category subordinate metrics greater than the numerical threshold is large, then determine the running category as the abnormal access category.

[0057] It should be noted that the above subordinate metrics and associated metrics for task access metrics are merely exemplary, and the specific subordinate metrics and associated metrics can be configured according to actual requirements and actual scenarios. For example, in the case of task access based on web - end products, the running metrics may include the number of Node.js exceptions, the number of node interface exceptions, and the number of node white - screen exceptions. However, in the case of task access based on system programs, the running metrics may only include the number of node interface exceptions and the number of node white - screen exceptions. This embodiment does not make a limitation here.

[0058] In the second alternative implementation provided by this embodiment, during the process of obtaining abnormal task nodes and abnormal access categories by performing abnormal identification access on tasks according to the abnormal identification strategy corresponding to the subordinate metrics and task access metrics, the following operations are performed: Determine the target subordinate metrics according to the subordinate metrics of the task access metrics and the benchmark threshold, and read the associated metrics of the target subordinate metrics; Determine the abnormal task nodes and abnormal access categories according to the associated metrics.

[0059] Optionally, the task access metrics include: the execution loss degree of a user performing a task through a program; the subordinate metrics of the execution loss degree include the number of loops, the number of cross - jumps, the number of repetitions, and / or the number of drop - offs. For example, as Figure 3 shown, for task A - B - C - D - E, if the actual execution is: ABCBA, it means that the loop is executed 1 time, the repetition is executed 2 times, and A and B are repeated respectively; if the actual execution is ABABAB, it means that the cross - jump is executed 3 times, the repetition is executed 3 times, and A, B, and C are repeated respectively; if the actual execution is ABDC, it means that the drop - off is executed 1 time.

[0060] Specifically, determine the target subordinate metrics whose metric values in the subordinate metrics of the execution loss degree are greater than the benchmark threshold, read the associated metrics of the target subordinate metrics, determine the abnormal task nodes according to the metric values of the associated metrics, and determine the abnormal access category according to the metric names of the associated metrics.

[0061] For example, the metric relationship of the execution loss degree, which is a task access metric, is shown in Table 2:

[0062] Table 2 For task A - B - C - D - E, after determining the target subordinate indicators greater than 0 among the subordinate indicators of the execution loss degree, the abnormal task nodes and abnormal access categories are determined according to the associated indicators of the target subordinate indicators; if the target subordinate indicator is the number of loops, the loop nodes in the loop record can be determined as abnormal task nodes, and the loop category is used as the abnormal access category; the same applies to other subordinate indicators, which will not be elaborated here in this embodiment.

[0063] The following combination Figure 3 and Table 3 are used to give examples of the processes of loop, cross - jump, repetition, and dropout.

[0064]

[0065] Table 3 In the third alternative implementation manner provided in this embodiment, in the process of performing abnormal recognition access on tasks according to the abnormal recognition strategy corresponding to the subordinate indicators and task access indicators to obtain abnormal task nodes and abnormal access categories, the following operations are performed: Classify the subordinate indicators of the task access indicators to obtain the category subordinate indicators of each indicator category; Determine the abnormal task nodes and abnormal access categories according to the category subordinate indicators under each indicator category.

[0066] Optionally, the task access indicators include the average access duration of nodes for users to access tasks through the program; the subordinate indicators of the average access duration of nodes include the operation indicators, running indicators, and node operation loss indicators of each task node.

[0067] Furthermore, in order to improve the accuracy and effectiveness of the abnormal task nodes and abnormal access categories determined based on the category subordinate indicators, in an alternative implementation manner provided in this embodiment, in the process of determining the abnormal task nodes and abnormal access categories according to the category subordinate indicators under each indicator category, first, according to the index value distribution of the category subordinate indicators of each indicator category, the target category is determined as the abnormal access category in at least one indicator category, and the abnormal task nodes are determined in at least one task node according to the index values of the category subordinate indicators of each task node under the target category.

[0068] Specifically, after classifying the subordinate indicators of the task access indicators to obtain the category subordinate indicators of each indicator category, the indicator category with larger category subordinate indicators or a larger number of category subordinate indicators greater than the numerical threshold is determined as the target category, and the task nodes corresponding to the category subordinate indicators greater than the numerical threshold under the target category are determined as the abnormal task nodes.

[0069] For example, the index relationship of the task access indicator of the average access duration of nodes is shown in Table 4:

[0070] Table 4 For task A - B - C - D - E, after obtaining the category subordinate indicators under the category operation category, operation category, and lost category by classifying the subordinate indicators, if the category subordinate indicators under the operation category are larger or the number of category subordinate indicators greater than the numerical threshold is larger, then determine the operation category as the abnormal access category, and determine the task nodes corresponding to the category subordinate indicators greater than the numerical threshold under the operation category as abnormal task nodes; If the category subordinate indicators under the operation category are larger or the number of category subordinate indicators greater than the numerical threshold is larger, then determine the operation category as the abnormal access category, and determine the task nodes corresponding to the category subordinate indicators greater than the numerical threshold under the operation category as abnormal task nodes; If the category subordinate indicators under the lost category are larger or the number of category subordinate indicators greater than the numerical threshold is larger, then determine the lost category as the abnormal access category, and determine the task nodes corresponding to the category subordinate indicators greater than the numerical threshold under the lost category as abnormal task nodes.

[0071] It should be noted that the task access indicators, subordinate indicators, and associated indicators in this embodiment can all be calculated based on the task access data. They can be calculated and stored in real - time according to the task access data, or calculated during task processing. This embodiment does not make any limitations here.

[0072] It should also be noted that the above provides different methods for obtaining abnormal task nodes and abnormal access categories. Different methods for obtaining abnormal task nodes and abnormal access categories can be combined and replaced with each other. For example, during the process of determining the abnormal access category based on the associated indicator, the abnormal access category can be determined according to the numerical distribution of the indicator values under the three indicator categories of the operation category, operation category, and lost category. The specific process is similar to the above, and this embodiment does not make any limitations here.

[0073] In addition, steps S204 to S206 can also be replaced with: if the task access indicator meets the corresponding indicator detection condition, perform abnormal access recognition on the task according to the subordinate indicator of the task access indicator and the corresponding abnormal recognition strategy to obtain abnormal task nodes and abnormal access categories, and form a new implementation method with step S202 and / or step S208 provided in this embodiment.

[0074] In this embodiment, for the task conversion rate and execution loss, the abnormal task nodes and abnormal access categories are determined by determining the subordinate indicators and then determining the associated indicators. For the average node access time, the abnormal task nodes and abnormal access categories are determined by determining the category subordinate indicators of each indicator category. It should be noted that the specific subordinate indicators, associated indicators and category subordinate indicators can be determined based on the program. For example, according to the abnormality determination strategy corresponding to the subordinate indicators and task access indicators, the abnormal task nodes and the associated indicators under the program are determined in at least one task node, and the abnormal access category is determined according to the associated indicators; specifically, if the program is a web product or an application (local application), the determined associated indicators and category subordinate indicators include related indicators of the number of js exceptions, such as the number of node js exceptions; if the program is a system program, the determined associated indicators, category associated indicators and category subordinate indicators may not include related indicators of the number of js exceptions; in addition, due to different programs, other associated indicators and category subordinate indicators may be different, which can be configured according to the actual scenario, and this embodiment is not limited here.

[0075] Step S208: performing task update processing according to the abnormal task node and the abnormal access category.

[0076] During specific implementation, after obtaining the abnormal task node and abnormal access category, task update processing is performed according to the abnormal task node and abnormal access category. In order to improve the processing user's perception of the task update processing, this embodiment provides an optional implementation method. In the process of performing task update processing according to the abnormal task node and abnormal access category, abnormal reminder data is first generated according to the abnormal access category, and then a task update reminder is performed according to the abnormal task node and abnormal reminder data.

[0077] Specifically, the abnormal reminder data corresponding to the abnormal access category can be configured in advance. For example, the abnormal reminder data corresponding to the operation category can be configured as follows: the operation is complicated, causing the user to not know how to operate; the abnormal reminder data corresponding to the running category can be configured as follows: a node abnormality occurs; the abnormal reminder data corresponding to the loop category, the horizontal jump category, and the repetition category can be configured as follows: the input of the subsequent node needs to depend on the content of the previous node, or the previous input item is very complicated and needs to be confirmed repeatedly. The dependent input items can be designed on one page to reduce the number of confirmations; the abnormal reminder data corresponding to the jump category can be configured as follows: the task design is complex; the abnormal reminder data corresponding to the lost category can be configured as follows: the user is easily lost when participating; Based on this, after obtaining the abnormal access category, the abnormal reminder data corresponding to the abnormal access category is first read, and then a task update reminder containing the abnormal task node and abnormal reminder data is generated and sent to the processing user. Optionally, the processing user performs task update processing according to the task update reminder.

[0078] In addition to performing task update processing based on abnormal task nodes and abnormal reminder data, task update reminders can also be generated based on abnormal task nodes, abnormal reminder data and related indicators to enable processing users to more intuitively perceive the cause of the abnormality.

[0079] It should be noted that, when obtaining the abnormal task node and abnormal access category under the task access indicator of execution loss, if the abnormal access category is the first access category, the abnormal reminder data corresponding to the abnormal access category is directly read, and a task update reminder is generated based on the abnormal task node and abnormal reminder data; optionally, the first access category includes a loop category, a lateral jump category, and a repeat category; If the abnormal access category is the second access category, it means that in some cases, the task can be completed without executing a certain task node, which means that the task design is too complex or there are some special circumstances. In this embodiment, in order to make the content of the task update reminder more specific and comprehensive, the task update reminder can be generated based on the detection results of the access user detection and / or access scenario detection and the abnormal task node; in an optional implementation manner provided by this embodiment, during the task update process, if the abnormal access category is the missed category, the task update process can be performed in the following manner: detecting whether the access scenario corresponding to the second access category is the same access scenario; If yes, perform similarity and difference detection on the page data of the abnormal task node and the page data of the adjacent task nodes of the abnormal task node, and generate a task update reminder according to the detection result and the abnormal task node; If not, abnormal jump missing reminder data is generated, and a task update reminder is generated based on the abnormal jump missing reminder data and the abnormal task node.

[0080] Specifically, if the abnormal access category is a missed category and the access scenario in which the missed category abnormal access occurs is the same access scenario, then detect whether the page data of the abnormal task node and the page data of the adjacent task node are the same, and generate a task update reminder based on the abnormal reminder data and abnormal task node corresponding to the detection result.

[0081] For example, if the access scenarios in which abnormal accesses with missed categories occur are all information collection scenarios based on web-based products, then it is detected whether the page data of the abnormal task node is the same as the page data of the previous task node, and / or whether the page data of the abnormal task node is the same as the page data of the next task node. If there is an adjacent task node with the same page data as the abnormal task node, a task update reminder including the abnormal task node and the duplicate task node is generated; if not, a task update reminder including the abnormal task node and the missed category is generated.

[0082] In addition to access scenario detection, user detection can also be accessed. Specifically, it is detected whether the abnormal access users corresponding to the second access category are of the same type to obtain a user detection result. A task update reminder is generated according to the user detection result and the abnormal task node. For example, a task update reminder including the user bounce data and abnormal task nodes of the same type or a task update reminder including the user bounce data and abnormal task nodes of different types is generated. Optionally, the user bounce data includes specific user types.

[0083] It should be noted that in this embodiment, the effectiveness of task update processing can be improved by adjusting the subordinate indicators of each task access indicator. Specifically, if it is detected that all task access indicators meet the indicator detection conditions within a preset period, a reminder for adjusting the subordinate indicators of the task access indicator is generated and sent to the processing user to continuously improve the effectiveness of task access data calculation, thereby improving the effectiveness of task update processing.

[0084] In specific implementation, after obtaining the target task through the above-mentioned method for task update processing, the target task can also be recommended to the service provider to perform program configuration based on the target task, improving the program configuration efficiency and also improving the usage rate of the target task. Optionally, the target task obtained after task update processing is acquired and recommended to at least one service provider to perform program configuration based on the target task. In addition, task development or task configuration can be carried out based on the target task. In addition, the task data of the target task can be used as training samples to train a large language model to obtain a task generation model. After the task generation prompt text is input into the task generation model subsequently, the task generation model can generate the target task according to the task generation prompt text. The large language model includes LLM (Large Language Model). In addition, the large language model in this embodiment can also be a pre-trained natural language model. The large language model can adopt a foundation model or a pre-trained model. Specifically, the architecture of the large language model can be a neural network architecture with a large number of parameters, a Transform architecture or other architectures. In the specific execution process, the large language model can directly adopt a foundation model or a pre-trained model, or can fine-tune (Supervised Fine-Tuning, SFT) the foundation model or the pre-trained model for task generation tasks based on the foundation model or the pre-trained model, and thus a large language model capable of performing task generation tasks can be obtained.

[0085] In addition, steps S206 to S208 can also be replaced by identifying abnormal access to the task according to the abnormal identification strategy corresponding to the subordinate index and the task access index, obtaining the abnormal task node and the abnormal access category, so as to perform task update processing or perform task update processing based on the abnormal task node and the abnormal access category, and form a new implementation manner with one or more other processing steps provided in this embodiment.

[0086] In summary, one or more task processing methods provided in this embodiment calculate task access metrics based on task access data for task access on a web-based product. If the task access metrics meet the corresponding metric detection conditions, abnormal access to the task is identified according to the subordinate metrics of the task access metrics and the corresponding abnormal identification strategy, obtaining the abnormal task node and the abnormal access category, generating a task update reminder based on the abnormal task node and the abnormal access category, so as to perform task update processing according to the task update reminder; In this process, by introducing three task access metrics, namely task conversion rate, execution loss rate, and average access duration of nodes, the task quality of task access on a web-based product is detected from three dimensions: the reason for user departure, the expected task access process and the actual task access process, and whether the task node design is reasonable, improving the effectiveness and comprehensiveness of task quality detection; after obtaining that each task access metric does not meet the corresponding metric detection conditions, that is, each task access metric meets the expected target task, the target task can also be recommended or the web-based product can be designed based on the target task, thereby improving the convenience and efficiency of similar web-based product design.

[0087] The following takes the application of a task processing method provided in this embodiment in a task processing scenario based on a web program as an example to further illustrate the task processing method provided in this embodiment, as Figure 4 shown, the task processing method applied to a task processing scenario based on a web program specifically includes the following steps.

[0088] Step S402, calculate the task conversion rate according to the task access data for task access on the web page based on the web program.

[0089] Optionally, the web page of the web program includes a web page.

[0090] Step S404, if the task conversion rate is less than the conversion rate threshold, read the number of user participations in each web task page.

[0091] Step S406, calculate the user bounce rate of each web task page according to the number of user participations in each web task page and the number of user participations in the adjacent web task page.

[0092] Step S408: Determine the abnormal web task pages with the user bounce rate greater than the bounce rate threshold in at least one web task page, and read the associated metrics of the abnormal web task pages.

[0093] Step S410: Classify the associated metrics to obtain the category-associated metrics for each metric category.

[0094] Step S412: Determine the abnormal access category based on the category-associated metrics for each metric category, and read the abnormal reminder data corresponding to the abnormal access category.

[0095] Step S414: Generate a task update reminder including the abnormal web task pages and the abnormal reminder data for task update.

[0096] It should be noted that any one step or any combination of multiple steps from Step S402 to Step S414 can be combined with any one step or any combination of multiple steps from the above Step S202 to Step S208 according to the needs of implementation and deployment to form a new implementation method; in addition, according to the actual deployment needs, any one or any combination of technical features from Step S402 to Step S414 can be combined with any one or more technical features provided by the above Step S202 to Step S208 to form a new implementation method; or, any one or any combination of technical features from Step S402 to Step S414 can also be replaced by any one or more technical features provided by the above Step S202 to Step S208 according to the actual deployment needs to form a new implementation method, which will not be elaborated here one by one.

[0097] The following takes the application of a task processing method provided in this embodiment in a task processing scenario based on web services as an example to further illustrate the task processing method provided in this embodiment. As Figure 5 shown, the task processing method applied to a task processing scenario based on web services specifically includes the following steps.

[0098] Step S502: Calculate the execution loss degree based on the task access data for task access on the service page based on web services.

[0099] Optionally, the service page of the web service includes a web page.

[0100] Step S504: If the execution loss degree is greater than the loss degree threshold, read the subordinate metrics of the execution loss degree calculated based on the task access data.

[0101] Step S506: Determine the target subordinate metrics based on the subordinate metrics and the benchmark threshold, and read the associated metrics of the target subordinate metrics.

[0102] Optionally, the reference threshold is 0.

[0103] Step S508: Determine the abnormal task service page of the task according to the index parameter of the associated index, and determine the abnormal access category according to the index name of the associated index.

[0104] Step S510: Generate reminder data according to the reminder data generation policy corresponding to the abnormal access category to obtain abnormal reminder data.

[0105] Optionally, the reminder data generation policy includes directly reading the abnormal reminder data corresponding to the abnormal access category corresponding to the first access category, and / or performing access scenario detection corresponding to the second access category to determine the abnormal reminder data corresponding to the detection result.

[0106] Step S512: Generate a task update reminder including the abnormal reminder data and the abnormal task service page for task update.

[0107] It should be noted that any one step or any combination of steps from Step S502 to Step S512 can be combined with any one step or any combination of the above steps S202 to S208 to form a new implementation according to the needs of implementation and deployment; in addition, according to the actual deployment needs, any one or any combination of technical features in Step S502 to Step S512 can be combined with any one or more technical features provided by the above steps S202 to S208 to form a new implementation; or, any one or any combination of technical features in Step S502 to Step S512 can also be replaced by any one or more technical features provided by the above steps S202 to S208 according to the actual deployment needs to form a new implementation, which will not be elaborated here one by one.

[0108] The following takes the application of a task processing method provided in this embodiment in a task processing scenario based on an application as an example to further illustrate the task processing method provided in this embodiment. As Figure 6 shown, the task processing method applied to a task processing scenario based on an application specifically includes the following steps.

[0109] Step S602: Calculate the average page access duration according to the task access data for task access based on the application page of the application.

[0110] Step S604: If the average page access duration is greater than the duration threshold, read the subordinate index of the average page access duration calculated according to the task access data.

[0111] Step S606: Classify the subordinate indexes to obtain the category subordinate indexes of each index category.

[0112] Step S608: Determine the abnormal access category and the abnormal task application page according to the category subordinate indicators under each indicator category, and read the abnormal reminder data corresponding to the abnormal access category.

[0113] Step S610: Generate a task update reminder including the abnormal task application page and the abnormal reminder data for task update.

[0114] It should be noted that any one step or any combination of steps from Step S602 to Step S610 can be combined with any one step or any combination of steps from the above-mentioned Step S202 to Step S208 according to the needs of implementation and deployment to form a new implementation method; in addition, according to the actual deployment needs, any one or any combination of technical features in Step S602 to Step S610 can be combined with any one or more technical features provided by the above-mentioned Step S202 to Step S208 to form a new implementation method; or, any one or any combination of technical features in Step S602 to Step S610 can also be replaced by any one or more technical features provided by the above-mentioned Step S202 to Step S208 according to the actual deployment needs to form a new implementation method, which will not be elaborated here one by one.

[0115] An embodiment of a task processing device provided in this specification is as follows: In the above embodiment, a task processing method is provided. Correspondingly, a task processing device is also provided, which will be described below with reference to the accompanying drawings.

[0116] Refer to Figure 7 , which shows a schematic diagram of an embodiment of a task processing device provided in this embodiment.

[0117] Since the device embodiment corresponds to the method embodiment, the description is relatively simple. For the relevant parts, please refer to the corresponding description of the method embodiment provided above. The device embodiments described below are only illustrative.

[0118] This embodiment provides a task processing device, which includes: A task access index calculation module 702, configured to calculate a task access index according to task access data for task access based on a program; If the task access index meets the corresponding index detection condition, then run a subordinate index reading module 704, and the subordinate index reading module 704 is configured to read the subordinate index of the task access index; An abnormal access recognition module 706, configured to perform abnormal access recognition on a task according to the subordinate index and an abnormal recognition policy corresponding to the task access index, and obtain an abnormal task node and an abnormal access category; The task update processing module 708 is configured to perform task update processing according to the abnormal task node and the abnormal access category.

[0119] An embodiment of a task processing device provided in this specification is as follows: Corresponding to the above-described task processing method, based on the same technical concept, one or more embodiments of this specification further provide a task processing device, and this task processing device is used to execute the task processing method provided above. Figure 8 It is a schematic structural diagram of a task processing device provided by one or more embodiments of this specification.

[0120] A task processing device provided in this embodiment includes: As Figure 8 shown, the task processing device may have relatively large differences due to configuration or performance, and may include one or more processors 801 and a memory 802. One or more application programs or data may be stored in the memory 802. Among them, the memory 802 may be short-term storage or persistent storage. The application programs stored in the memory 802 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the task processing device. Further, the processor 801 may be set to communicate with the memory 802 and execute a series of computer-executable instructions in the memory 802 on the task processing device. The task processing device may further include one or more power supplies 803, one or more wired or wireless network interfaces 804, one or more input / output interfaces 805, one or more keyboards 806, etc.

[0121] In a specific embodiment, the task processing device includes a memory and one or more programs, where one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the task processing device, and is configured to be executed by one or more processors. The one or more programs include the following computer-executable instructions: Calculate a task access metric based on task access data for task access based on a program; If the task access metric meets the corresponding metric detection condition, read the subordinate metric of the task access metric; Perform abnormal access identification on the task according to the subordinate metric and the abnormal identification policy corresponding to the task access metric to obtain an abnormal task node and an abnormal access category; Perform task update processing according to the abnormal task node and the abnormal access category.

[0122] An embodiment of a computer-readable storage medium provided in this specification is as follows: Corresponding to the above-described task processing method, based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0123] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, and when the computer-executable instructions are executed, the following processes are implemented: Calculate a task access metric based on task access data for accessing a task based on a program; If the task access metric meets the corresponding metric detection condition, read the subordinate metric of the task access metric; Perform abnormal access identification on the task according to the subordinate metric and the abnormal identification strategy corresponding to the task access metric, and obtain an abnormal task node and an abnormal access category; Perform task update processing according to the abnormal task node and the abnormal access category.

[0124] It should be noted that the embodiment of a computer-readable storage medium in this specification and the embodiment of a task processing method in this specification are based on the same inventive concept. Therefore, for the specific implementation of this embodiment, reference may be made to the implementation of the foregoing corresponding method, and repeated parts will not be elaborated.

[0125] An embodiment of a computer program product provided in this specification is as follows: Corresponding to the above-described task processing method, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0126] A computer program product includes a computer program / instructions, and when the computer program / instructions are executed by a processor, the following steps are implemented: Calculate a task access metric based on task access data for accessing a task based on a program; If the task access metric meets the corresponding metric detection condition, read the subordinate metric of the task access metric; Perform abnormal access identification on the task according to the subordinate metric and the abnormal identification strategy corresponding to the task access metric, and obtain an abnormal task node and an abnormal access category; Perform task update processing according to the abnormal task node and the abnormal access category.

[0127] It should be noted that the embodiments of a computer program product in this specification and the embodiments of a task processing method in this specification are based on the same inventive concept. Therefore, for the specific implementation of this embodiment, reference may be made to the implementation of the corresponding method described above, and repeated parts will not be elaborated.

[0128] The embodiments in this specification are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. For example, the device embodiment, the equipment embodiment, and the computer-readable storage medium embodiment are all similar to the method embodiment, so the description is relatively simple. Please refer to the corresponding parts of the method embodiment for the relevant content in the device embodiment, the equipment embodiment, and the computer-readable storage medium embodiment.

[0129] The specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compilers used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a hardware description language (HDL), and there is not just one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into an integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0131] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0132] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0133] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0134] Those skilled in the art should understand that one or more embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this specification can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0135] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0136] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0138] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0139] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0140] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0141] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising at least one ..." does not exclude the presence of additional identical elements in the process, method, commodity, or apparatus comprising the element.

[0142] One or more embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data classes. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0143] The foregoing description is merely an example of the present invention and is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims herein.

Claims

1. A task processing method, comprising: Calculating a task access metric based on task access data for task access based on a program; If the task access metric meets a corresponding metric detection condition, reading a subordinate metric of the task access metric; Performing abnormal access recognition on the task according to the subordinate metric and an abnormal recognition strategy corresponding to the task access metric to obtain an abnormal task node and an abnormal access category; Performing task update processing according to the abnormal task node and the abnormal access category.

2. The task processing method according to claim 1, wherein the performing abnormal access recognition on the task according to the subordinate metric and an abnormal recognition strategy corresponding to the task access metric to obtain an abnormal task node and an abnormal access category comprises: Determining the abnormal task node and associated metrics in at least one task node according to the subordinate metric and an abnormal determination strategy corresponding to the task access metric; Determining the abnormal access category according to the associated metrics.

3. The task processing method according to claim 1, wherein the calculating a task access metric based on task access data for task access based on a program comprises: Reading target access data corresponding to the task access metric from task access data obtained by task access based on a program page; Calculating a metric value of the task access metric according to the target access data.

4. The task processing method according to claim 1, wherein the performing task update processing according to the abnormal task node and the abnormal access category comprises: Generating abnormal reminder data according to the abnormal access category; Performing a task update reminder according to the abnormal task node and the abnormal reminder data.

5. The task processing method according to claim 1, wherein the performing abnormal access recognition on the task according to the subordinate metric and an abnormal recognition strategy corresponding to the task access metric to obtain an abnormal task node and an abnormal access category comprises: Calculating a node abnormal metric for each task node according to the subordinate metric of the task access metric, and determining the abnormal task node in at least one task node according to the node abnormal metric; Reading an associated metric of the abnormal task node under the task access metric, and determining the abnormal access category according to the associated metric; wherein the task access metric includes: a task conversion rate at which a user completes task execution through the program.

6. The task processing method according to claim 1, wherein the performing abnormal access recognition on the task according to the subordinate metric and an abnormal recognition strategy corresponding to the task access metric to obtain an abnormal task node and an abnormal access category comprises: Determining a target subordinate metric according to the subordinate metric of the task access metric and a benchmark threshold, and reading an associated metric of the target subordinate metric; Determining the abnormal task node and the abnormal access category according to the associated metric; wherein the task access metric includes: an execution loss degree at which a user performs task execution through the program.

7. The task processing method according to claim 1, wherein the task update processing comprises: If the abnormal access category is the first access category, read the abnormal reminder data corresponding to the abnormal access category; Generate a task update reminder according to the abnormal task node and the abnormal reminder data.

8. The task processing method according to claim 7, wherein the task update processing further includes: If the abnormal access category is the second access category, detect whether the access scenario corresponding to the second access category is the same access scenario; If so, perform a difference detection on the page data of the abnormal task node and the page data of the adjacent task nodes of the abnormal task node, and generate a task update reminder according to the detection result and the abnormal task node.

9. The task processing method according to claim 1, wherein the abnormal access recognition of the task according to the abnormal recognition strategy corresponding to the subordinate index and the task access index to obtain an abnormal task node and an abnormal access category includes: Classify the subordinate indexes of the task access index to obtain the category subordinate indexes of each index category; Determine the abnormal task node and the abnormal access category according to the category subordinate indexes under each index category; Wherein, the task access index includes: the average access duration of the node for the user to perform task access through the program.

10. The task processing method according to claim 9, wherein the determining the abnormal task node and the abnormal access category according to the category subordinate indexes under each index category includes: Determine a target category in at least one index category according to the index value distribution of the category subordinate indexes of each index category, and use the target category as the abnormal access category; Determine the abnormal task node in at least one task node according to the index values of the category subordinate indexes of each task node under the target category.

11. The task processing method according to claim 1, wherein the program includes an application program and / or a web program; the task is accessed based on the program page of the program.

12. The task processing method according to claim 1, wherein the abnormal task node includes at least one of the following: An application page with a user bounce rate greater than the bounce rate threshold during the process of performing a task based on the application page of the application program; A web page that fails the page loading index and / or the page operation index detection during the process of accessing a task based on the web page of the web program; A web page that is repeatedly accessed during the process of performing a task based on the web page.

13. The task processing method according to claim 1, after the step of performing task update processing according to the abnormal task node and the abnormal access category, further includes: Obtain the target task obtained after the task update processing; Recommend the target task to at least one service provider for program configuration based on the target task.

14. A task processing device, comprising: A task access index calculation module configured to calculate a task access index according to task access data for performing task access based on a program; If the task access metric meets the corresponding metric detection condition, run the subordinate metric reading module, which is configured to read the subordinate metrics of the task access metric; The abnormal access identification module is configured to perform abnormal access identification on the task according to the subordinate metric and the abnormal identification strategy corresponding to the task access metric, and obtain the abnormal task node and the abnormal access category; The task update processing module is configured to perform task update processing according to the abnormal task node and the abnormal access category.

15. A task processing device, comprising: A processor; And a memory configured to store computer-executable instructions, which when executed cause the processor to: Calculate a task access metric based on task access data for task access based on a program; If the task access metric meets the corresponding metric detection condition, read the subordinate metrics of the task access metric; Perform abnormal access identification on the task according to the subordinate metric and the abnormal identification strategy corresponding to the task access metric, and obtain the abnormal task node and the abnormal access category; Perform task update processing according to the abnormal task node and the abnormal access category.

16. A computer-readable storage medium for storing computer-executable instructions, which when executed implement the steps of the method according to claim 1.

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