CN116431650A

CN116431650A - Object processing method and device

Info

Publication number: CN116431650A
Application number: CN202310316969.9A
Authority: CN
Inventors: 卢光汇; 刘慧慧; 倪静
Original assignee: Alibaba China Co Ltd
Current assignee: Alibaba China Co Ltd
Priority date: 2023-03-28
Filing date: 2023-03-28
Publication date: 2023-07-14

Abstract

The embodiment of the specification provides an object processing method and device, wherein the object processing method comprises the following steps: determining a first object to be detected corresponding to a first detection task; determining relevant parameters of the first object to be detected under the condition that the first object to be detected meets the task execution condition of the first detection task; determining a target data acquisition statement corresponding to the first object to be detected according to the related parameters of the first object to be detected; and acquiring the data corresponding to the first object to be detected by using the target data acquisition statement, and detecting the data corresponding to the first object to be detected to obtain a data detection result. Detection false alarm is avoided through pre-verification of an object to be detected, so that detection efficiency is improved, and data quality is guaranteed.

Description

Object processing method and device

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to an object processing method.

Background

Along with the rapid development of the internet, the application of data is gradually enriched, more and more applications and services are built based on the data, and only the data quality is ensured, the normal operation of the applications and services can be ensured. The data quality is evaluated from the dimensions of stability, consistency, timeliness, integrity, normalization, and accuracy of the data. The data quality is the basis of the validity and accuracy of the data analysis conclusion, the data with the quality not guaranteed cannot embody the service value, and the abnormal data can cause abnormal service when the data analysis is performed and the service strategy is formulated based on the abnormal data. How to implement quality detection of data so as to ensure the quality of the data is a non-negligible problem.

Disclosure of Invention

In view of this, the present embodiment provides an object processing method. One or more embodiments of the present disclosure relate to an object processing apparatus, another object processing method, another object processing apparatus, an object processing system, a commodity data detection method, and a computing device, so as to solve the technical drawbacks of the prior art.

According to a first aspect of embodiments of the present specification, there is provided an object processing method, including:

determining a first object to be detected corresponding to a first detection task;

determining relevant parameters of the first object to be detected under the condition that the first object to be detected meets the task execution condition of the first detection task;

determining a target data acquisition statement corresponding to the first object to be detected according to the related parameters of the first object to be detected;

and acquiring the data corresponding to the first object to be detected by using the target data acquisition statement, and detecting the data corresponding to the first object to be detected to obtain a data detection result.

According to a second aspect of embodiments of the present specification, there is provided an object processing apparatus comprising:

The first determining module is configured to determine a first object to be detected corresponding to the first detection task;

a second determining module configured to determine a relevant parameter of the first object to be detected, in a case where it is determined that the first object to be detected satisfies a task execution condition of the first detection task;

the third determining module is configured to determine a target data acquisition statement corresponding to the first object to be detected according to the related parameters of the first object to be detected;

the detection module is configured to acquire the data corresponding to the first object to be detected by using the target data acquisition statement, detect the data corresponding to the first object to be detected and acquire a data detection result.

According to a third aspect of embodiments of the present specification, there is provided an object processing method, applied to an end-side device, including:

detecting touch operation of a display interface, and displaying a task configuration interface through the display interface under the condition that the touch operation is determined to trigger a data detection request;

detecting touch operation aiming at a first control in the task configuration interface, determining corresponding task configuration information, and generating at least one initial detection task, wherein the first control is any one of at least one control on the task configuration interface;

And receiving a data detection result obtained based on the at least one initial detection task, and displaying the data detection result through the display interface.

According to a fourth aspect of embodiments of the present specification, there is provided an object processing apparatus applied to an end-side device, including:

the display module is configured to detect touch operation of the display interface, and display a task configuration interface through the display interface under the condition that the touch operation is determined to trigger a data detection request;

the generating module is configured to detect touch operation of a first control in the task configuration interface, determine corresponding task configuration information and generate at least one initial detection task, wherein the first control is any one of at least one control on the task configuration interface;

the receiving module is configured to receive the data detection result obtained based on the at least one initial detection task and display the data detection result through the display interface.

According to a fifth aspect of embodiments of the present specification, there is provided an object processing system comprising an object detection unit and a data storage unit, wherein,

the object detection unit is used for executing the object processing method, and the data storage unit is used for storing an object to be detected and data corresponding to the object to be detected.

According to a sixth aspect of embodiments of the present specification, there is provided a commodity data detection method, comprising:

determining a first to-be-detected index corresponding to a first detection task, wherein the first to-be-detected index is the to-be-detected index of a target commodity;

determining a relevant parameter of the first index to be detected under the condition that the first index to be detected meets the task execution condition of the first detection task;

determining a target data acquisition statement corresponding to the first index to be detected according to the related parameters of the first index to be detected;

and acquiring the data corresponding to the first to-be-detected index by using the target data acquisition statement, and detecting the data corresponding to the first to-be-detected index to obtain a data detection result.

According to a seventh aspect of embodiments of the present specification, there is provided a computing device comprising:

a memory and a processor;

the memory is configured to store computer-executable instructions that, when executed by the processor, perform the steps of the method described above.

According to an eighth aspect of embodiments of the present description, there is provided a computer-readable storage medium storing computer-executable instructions which, when executed by a processor, implement the steps of the above-described method.

According to a ninth aspect of the embodiments of the present specification, there is provided a computer program, wherein the computer program, when executed in a computer, causes the computer to perform the steps of the above method.

An embodiment of the present disclosure provides an object processing method, which determines a first object to be detected corresponding to a first detection task; determining relevant parameters of the first object to be detected under the condition that the first object to be detected meets the task execution condition of the first detection task; determining a target data acquisition statement corresponding to the first object to be detected according to the related parameters of the first object to be detected; and acquiring the data corresponding to the first object to be detected by using the target data acquisition statement, and detecting the data corresponding to the first object to be detected to obtain a data detection result.

The method determines the detection task, checks the object to be detected, determines the related parameters of the object to be detected under the condition that the object to be detected meets the task execution condition, thereby determining the target data acquisition statement, realizing the acquisition of the data corresponding to the object to be detected, facilitating the detection of the data, avoiding the detection false alarm through the pre-check of the object to be detected, improving the detection efficiency, facilitating the subsequent adjustment or alarm of abnormal data according to the data detection result, and guaranteeing the quality of the data.

Drawings

Fig. 1 is a schematic application scenario diagram of an object processing method according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method of object handling provided in one embodiment of the present disclosure;

FIG. 3 is a process flow diagram of an object processing method according to one embodiment of the present disclosure;

FIG. 4 is a schematic diagram of an object processing apparatus according to an embodiment of the present disclosure;

FIG. 5 is a flow chart of another object handling method provided by one embodiment of the present disclosure;

FIG. 6 is a schematic view of a scenario of an object processing method according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of another object handling apparatus according to one embodiment of the present disclosure;

FIG. 8 is a schematic diagram of an object handling system according to one embodiment of the present disclosure;

FIG. 9 is a block diagram of another object handling system provided in one embodiment of the present disclosure;

FIG. 10 is a block diagram of a computing device provided in one embodiment of the present description.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many other forms than described herein and similarly generalized by those skilled in the art to whom this disclosure pertains without departing from the spirit of the disclosure and, therefore, this disclosure is not limited by the specific implementations disclosed below.

The terminology used in the one or more embodiments of the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the specification. As used in this specification, one or more embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that, although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first may also be referred to as a second, and similarly, a second may also be referred to as a first, without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) according to the embodiments of the present disclosure are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and provide corresponding operation entries for the user to select authorization or rejection.

In the present specification, an object processing method, which relates to an object processing apparatus, another object processing method, another object processing apparatus, an object processing system, a commodity data detecting method, a computing device, and the like, is provided, and the following embodiments are described in detail one by one.

In practical application, in order to detect data in a database, data is usually detected by submitting a plurality of SQL sentences, specifically, the SQL sentences for data acquisition, the SQL sentences for data detection and the SQL sentences for result output can be sequentially executed, and finally, a data detection result is obtained. However, when detecting a plurality of data tables in the database, if the condition that the data in the data tables is not updated timely occurs, the SQL statement is operated without being produced or updated by the partition or the data tables, the corresponding data cannot be acquired when the SQL statement is operated, and therefore false alarm is caused, and the false alarm rate is high.

For example, in the field of electronic commerce, for an enterprise, various index data of a commodity, such as data of production capacity, sales capacity and the like of the commodity, is generally stored in a database inside the enterprise, and whether a business process of the enterprise operates normally can be reflected through the data, so that analysis of business conditions of the enterprise is realized, for example, by analyzing sales capacity of a certain commodity in a period of time, a data basis can be provided for a strategy of determining whether to increase production capacity of the commodity or not later. Based on this, the quality of data stored in databases inside the enterprise should be guaranteed, for example, the data stored in the databases should be accurate, and for dynamic data of commodity index data, which changes every day, it is also necessary to guarantee the timeliness of data update. Therefore, the data in the database needs to be detected. For example, the detection rule may be determined according to the business requirement inside the enterprise, for example, in order to ensure that the business of the enterprise operates normally, the daily throughput of the commodity should be greater than 0, so that the normal sales of the commodity can be ensured, based on which the daily throughput of the commodity stored in the database may be detected, and in the case that the daily throughput is determined to be greater than 0, it is indicated that the daily throughput of the commodity meets the business requirement. Or, according to the business operation condition in the enterprise, the sales volume of a certain commodity is basically leveled in a period of time and does not decline, based on the condition, the current daily sales volume of the commodity is larger than or equal to the last daily sales volume, so that the business operation condition in the enterprise is met, based on the condition, the daily sales volume of the commodity stored in the database can be detected, and the data stored in the database is accurate under the condition that the current daily sales volume is always larger than or equal to the last daily sales volume. If the current daily sales volume is detected to be smaller than the previous daily sales volume, the method can further process according to the detection result, judge whether the data stored in the database is wrongly recorded or the business operation condition of the enterprise changes, and further determine the production and sales strategy based on the detection result.

However, currently, when detecting data in a database, for example, when detecting production data of a commodity, data is usually detected by submitting a plurality of SQL statements, for example, an SQL statement for acquiring the production data, an SQL statement for detecting the production data, and an SQL statement for outputting a detection result may be sequentially executed, and a data detection result is obtained according to the running results of the SQL statements. This results in a long time for detecting the production data of the commodity, and when there are a lot of commodities to be detected or a lot of tasks for detecting the commodity, it results in a lot of occupied detection resources and a reduction in detection efficiency. In addition, in the case of updating production data of a commodity in the database, the production data to be detected may be stored in two data tables respectively due to a system delay problem, the complete production data is not acquired from one data table, or other problems result in that the updated production data does not exist in the database, and in the process of detecting the production data, the production data needs to be acquired from the database first, and in this case, the updated production data does not exist in the database, the acquisition of the data fails, so that the detection of the production data fails, and early warning is performed at this time, but the early warning is not early warning because the data detection result is problematic, but because the data to be detected is not acquired, the early warning generates false alarm, and the false alarm rate is increased. Based on this, an effective solution is needed to solve the above-mentioned problems.

Referring to fig. 1, fig. 1 shows a schematic application scenario of an object processing method according to an embodiment of the present disclosure.

Cloud-side device 102 and database 104 are included in fig. 1. Cloud-side device 102 may be configured to perform an object processing method to enable detection of data stored in database 104.

In specific implementation, in a case where it is determined that an object to be detected stored in the database 104 is updated, the cloud-side device 102 determines at least one detection task corresponding to the object to be detected, and determines an object to be detected corresponding to any one detection task. And determining relevant parameters of the object to be detected under the condition that the object to be detected meets the task execution condition of the detection task. And determining a target data acquisition statement for acquiring the data of the object to be detected according to the related parameters, acquiring the data of the object to be detected by using the target data acquisition statement, and checking whether the data meets the detection rule corresponding to the detection task or not to obtain a data detection result.

As shown in fig. 1, the database 104 stores the number of times (objects to be detected) of the merchant accessing the e-commerce platform, specifically, the number of times of the merchant a accessing for 3 months and 1 day is 18 times, the number of times of the merchant a accessing for 3 months and 2 days is 50 times, and the like, and when determining that the number of times of the merchant a accessing is updated, the cloud side device 102 determines that the detection task 1 corresponding to the number of times of the merchant a accessing is greater than 0, and the detection rule corresponding to the detection task is: the number of times the merchant is accessed every day is required to be larger than 0, and the detection rule can be preset according to the service requirement of the e-commerce platform. The detection task 2 is to detect whether the accessed times of the current day is larger than the accessed times of the previous day, and the detection rule corresponding to the detection task is as follows: merchants need to be accessed a greater number of times per day than the previous day. For the detection task 1, in the case where it is determined that the updated number of times of day of access is stored in the database 104, it is determined that the relevant parameter corresponding to the number of times of access is 3 months and 4 days, and according to the relevant parameter, a target data acquisition statement 1 for acquiring the number of times of day of access of 3 months and 4 days is determined, and the number of times of day of access of 3 months and 4 days is 15 from the database 104 by using the target data acquisition statement 1, where the number of times of day of access satisfies a detection rule that the number of times of day of access of a merchant needs to be greater than 0, and the data detection result may be passing detection. For the detection task 2, in the case where it is determined that the updated number of times of day access and the number of times of day access associated with the updated number of times of day access are stored in the database 104, the relevant parameter corresponding to the number of times of day access is acquired as 3 months 4 days, the relevant parameter corresponding to the number of times of day access is acquired as 3 months 3 days, the target data acquisition statement 2 for acquiring the number of times of day access of 3 months 4 days and the number of times of day access of 3 months 3 days is determined based on the relevant parameter, the number of times of day access of 3 months 4 days is acquired as 15 times by using the target data acquisition statement 2, the number of times of day access of 3 months 3 days is 20 times, and the detection rule that the number of times of day access of the merchant needs to be greater than the number of times of day access of the previous day is not satisfied, the data detection result is not passed. The service personnel can be displayed and alerted through the terminal equipment.

Through the pre-verification of the object to be detected, the execution can be continued under the condition that the rule running condition is met, and the situation that data to be detected cannot be found when the detection task runs is avoided, so that the false alarm rate is reduced.

Referring to fig. 2, fig. 2 shows a flowchart of an object processing method according to an embodiment of the present disclosure, which specifically includes the following steps.

Step 202: and determining a first object to be detected corresponding to the first detection task.

Specifically, the object processing method provided in the embodiment of the present disclosure may be applied to cloud-side devices. The cloud-side device may be communicatively coupled to a data storage unit, such as an enterprise database, that is to be tested, so as to test the data stored in the data storage unit using the object handling method.

The object processing method provided in the embodiments of the present disclosure may be used for detecting a database of an enterprise, for example, for a manufacturing enterprise, the database is generally established to record data information of a product, for example, the data information may be throughput, sales volume, etc. of the product, and the production condition of the product is detected by detecting the data information of the product, so as to facilitate the specification of a subsequent product production policy. The object processing method can also be used for an e-commerce platform, a plurality of merchants possibly exist to enter the e-commerce platform, commodity management is carried out through the e-commerce platform, the e-commerce platform can also establish a database to record management information of each merchant, and the management information can be information such as sales amount, accessed times and the like of the merchant. The business condition of the merchant where the electronic commerce platform resides can be evaluated through detection of the business information of the merchant. It will be appreciated that the object processing method may also be used in any scenario where there is a need for detection of a database, and the description is not limited herein.

Specifically, the first object to be detected corresponding to the first detection task may be determined under the condition that the update of the object to be detected is determined. The first object to be detected corresponding to the first detection task can also be determined according to the service requirement. The first object to be detected corresponding to the first detection task may also be determined according to a preset time interval, which is not limited in this embodiment of the present disclosure.

The first detection task may be understood as any one of at least one detection task corresponding to the object to be detected. The object to be detected may be understood as data having a detection requirement, such as data of a throughput of the commodity, sales of the merchant, and the like. The at least one detection task corresponding to the object to be detected may be understood as detection content for detecting the object to be detected, may be understood as detection task for detecting a fluctuation degree of the object to be detected, for example, when the object to be detected is a production amount of a commodity, the at least one detection task corresponding to the object to be detected may include detecting whether the production amount on the same day is greater than 0, detecting whether the production amount on the same day is greater than a sales amount on the previous day, detecting whether a fluctuation rate between the production amount on the same day and the production amount generated in a history period does not exceed a preset threshold, and the like. Updating the object to be detected can be understood as adding the data of the object to be detected or changing the data of the object to be detected. For example, the commodity production amount update may be to add the commodity production amount on the 3 month 10 day basis, or may be to change the commodity production amount on the 3 month 9 day basis. The detection task may be determined according to a preset detection rule, for example, the detection rule preset according to the service requirement is that the daily throughput is greater than 0, and then the detection task determined according to the detection rule is that whether the daily throughput data in the detection table is greater than 0. In practical applications, the object to be detected may be offline data stored in a database, for example.

The first object to be detected may include an updated object to be detected and an associated object associated with the object to be detected, and the first object to be detected corresponding to the first detection task may be understood as data to be detected involved in the first detection task, for example, for a detection task that requires a current day throughput greater than 0, where the object to be detected corresponding to the detection task is the current day throughput. For the detection task with the current day throughput being larger than the previous day sales, the objects to be detected corresponding to the detection task are the current day throughput (updated objects to be detected) and the previous day sales (associated objects).

Based on this, in the case of determining that the data having the detection requirement is newly added or changed, a first detection task for detecting the data may be determined, and a first object to be detected corresponding to the first detection task may be determined.

For example, when detecting data in the database, in order to ensure that the commodity business of the enterprise normally operates, the commodity production amount should be larger than the sales amount of the commodity, so that normal sales of the commodity can be realized, and influence on the business is avoided.

In practical application, the database stores a plurality of data tables, and the first object to be detected may be stored in different partitions in the same data table or may be stored in different data tables. A partition may be understood as a collection of data updated in a data table. For example, the commodity throughput may be stored in data table a and the commodity sales may be stored in data table B. As shown in tables 1 and 2. The production amount of the commodity and the update time of the production amount are recorded in table 1, for example, the production amount of the commodity updated at 3 months 1 day is 100, the production amount of the commodity updated at 3 months 3 days is 150, etc., and the sales amount of the commodity and the update time of the sales amount are recorded in table 2, for example, the sales amount of the commodity updated at 3 months 1 day is 100, the sales amount of the commodity updated at 3 months 2 days is 80, etc. Accordingly, in the case that the commodity throughput update in table 1 is detected, it is determined that the detection task "detects whether the current day throughput is greater than the previous day sales", the updated object to be detected in the first object to be detected is the commodity throughput, the related parameter of the commodity throughput is determined for 3 months and 3 days according to the commodity sales determined by the detection task, and the related parameter of the commodity sales is determined for 3 months and 2 days, so that the target data acquisition statement is conveniently determined according to the related parameter, and the data value of the commodity throughput updated for 3 months and 3 days is obtained for 150,3 months and the data value 80 of the commodity sales updated for 2 days.

TABLE 1

Update time	Commodity throughput
		3 months 1 day	100
3 months and 3 days	150

TABLE 2

Update time	Commodity sales
		3 months 1 day	100
3 months and 2 days	80

In practical applications, a detecting person usually detects data stored in a database according to a detection requirement, or detects data stored in the database at intervals of a preset time period, however, this manner may lead to untimely detection of the data, possibly resulting in omission of data detection, such as performing data analysis by using updated data, which is abnormal, but the inaccuracy of data analysis is caused due to non-performing of data detection, thereby affecting subsequent services.

Based on the data in the database can be monitored, and when the update is monitored, the data is monitored, and the specific implementation mode is as follows:

determining at least one detection task corresponding to the updated object to be detected, including:

under the condition that the timestamp update corresponding to the object to be detected is determined, determining the update of the object to be detected; or alternatively

Under the condition of receiving a data update notification corresponding to an object to be detected, determining that the object to be detected is updated;

and determining at least one detection task corresponding to the updated object to be detected.

The timestamp corresponding to the object to be detected can be understood as a timestamp of data stored in the database, and when the data is updated, the timestamp corresponding to the object to be detected also changes, and based on the timestamp, whether the object to be detected is updated or not can be judged according to the timestamp. The data update notification may be understood as a data update notification transmitted by a database storing the object to be detected.

Based on this, it is possible to determine the update of the object to be detected and determine at least one detection task corresponding to the updated object to be detected, in the case of determining the update of the timestamp corresponding to the object to be detected or in the case of receiving the notification of the data update sent by the database.

In practical application, the updating time corresponding to the updated data partition in the corresponding data table on the data map can be monitored to be consistent with the updating time in the database, if not, the updating of the data in the data table is indicated, and then the subsequent detection step is executed. Specifically, a data update time delay can be set, and if the update time corresponding to the data partition is within the range of the data update time delay, the data update is indicated.

In conclusion, timeliness of data detection is achieved by monitoring updating of objects to be detected in the database, omission of data detection is facilitated, accuracy and consistency of data stored in the database are further guaranteed, and accordingly data quality is guaranteed.

In practical application, under the condition that more services exist at the same time point, and more data are updated, a large number of data detection tasks are submitted at the same time point, a data detection queue is occupied, and under the condition that the target project space queue resource is full, project space cannot be switched, standby manual operation is not available, a large number of quality inspection nodes and manual service flows can be created, high consumption is caused, and the number of concurrent detection tasks is uncontrollable. Continuing to explain by taking the aforementioned e-commerce field as an example, for index data of commodities stored in the enterprise database, since the commodities produced and sold by the enterprise are more, according to service requirements, detection tasks are different for different commodities, for example, for daily commodities or consumables with always higher sales volume, the detection tasks can be daily sales volume is not less than the average sales volume in a historical time period, so that for the commodities, the detection of the production volume is also important, and the detection tasks can also be daily production volume is greater than the average sales volume in the historical time period, so that normal sales of the commodities is ensured, and normal operation of the sales service of the commodities of the enterprise is ensured. Based on this, each commodity can correspond to a plurality of detection tasks, which results in a larger number of data detection tasks. In addition, for data updating stored in the database, index data of all commodities stored in the database are generally updated at a certain fixed time point in a unified way, and one-time input can ensure updating efficiency, but in this way, a plurality of data detection tasks possibly exist at the same time point, and at present, the plurality of data detection tasks are directly submitted to a data detection queue, so that resource consumption is high, the number of the data detection tasks cannot be controlled, and the detection efficiency is reduced.

Based on the method, the maximum concurrent execution task number can be set for the execution space of the detection tasks, so that the control of the detection task number is realized, and the specific implementation mode is as follows: before determining the first object to be detected corresponding to the first detection task, the method further comprises:

determining an execution space for executing the first detection task;

and executing the step of determining the first object to be detected corresponding to the first detection task under the condition that the number of the detection tasks executed in the execution space is determined to not reach a preset number threshold.

The executing the first detection task may be understood as executing a data acquisition statement corresponding to the first detection task, for example, the first detection task is to detect that the current day throughput is greater than the previous day sales, and then the data acquisition statement corresponding to the first detection task may be understood as an SQL statement for acquiring the current day throughput and the previous day sales. SQL, known as Structured Query Language, is a database language with multiple functions such as data manipulation and data definition. An execution space may be understood as a library for executing SQL statements. The preset number threshold may be understood as the maximum number of tasks that the execution space can concurrently execute. For example, in the case that the preset number threshold is 50, it is indicated that 50 detection tasks can be concurrently executed in the execution space.

Based on the above, an execution space for executing the detection task may be determined, and the number of detection tasks currently executed by the execution space may be determined, where the step of determining the first object to be detected corresponding to the first detection task is continued when the number of detection tasks currently executed by the execution space does not reach the maximum number of tasks that can be concurrently executed by the execution space.

For example, a preset number threshold of execution spaces for executing the first detection task is determined as a, the number of detection tasks executed in the execution spaces is b, and if b is smaller than a, it is indicated that the detection tasks can also be executed in the execution spaces, and at this time, the first object to be detected corresponding to the first detection task is continuously executed.

Correspondingly, if the number of the detection tasks executed in the execution space reaches a preset number threshold, it is indicated that the detection tasks cannot be executed any more in the execution space, and the execution is continued after the execution of any one detection task in the execution space is completed, and the first detection task can be added to the task queue to be executed for waiting, which is specifically implemented as follows:

after determining the execution space for executing the first detection task, the method further comprises:

Under the condition that the number of detection tasks executed in the execution space reaches a preset number threshold, adding the first detection task to a task queue to be executed;

and under the condition that the execution completion of any one detection task executed in the execution space is determined, adding the detection task in the task queue to be executed into the execution space and executing according to the priority information of the detection task in the task queue to be executed.

The task queue to be executed is understood to be a queue for placing detection tasks. The priority information of the detection task may be used to represent the importance of the detection task.

Specifically, when the number of detection tasks executed in the execution space reaches the maximum number of tasks that can be executed concurrently in the execution space, the first detection task may be added to the task queue to be executed first. And under the condition that the execution completion of any detection task executed in the execution space is determined, adding the detection task with the highest priority to the execution space according to the priority information of the detection task in the task queue to be executed, and executing.

Along with the above example, when the number b of detection tasks executed in the execution space is equal to the preset number threshold a, it is indicated that the first detection task cannot be executed in the execution space any more, the first detection task is added to the task queue to be executed, when any one of the b detection tasks executed in the execution space is executed, b-1 detection tasks are executed in the execution space at this time, b-1 is smaller than a, and then according to the priority information of the detection tasks in the task queue to be executed, the detection task with the highest priority is added to the execution space and executed, and so on until all detection tasks in the task queue to be executed are added to the execution space.

In addition, when the first detection task is added to the task queue to be executed, the position of the first detection task in the task queue to be executed may be determined according to the priority information of the first detection task. For example, the task queue to be executed includes a task 1 (with priority 3), a task 2 (with priority 1) and a task 3 (with priority 1), according to the priority 2 of the first detection task 4, the first detection task 4 may be added between the task 1 and the task 2 according to the priority order, and in the process of adding the detection task in the task queue to be executed to the execution space, the task 1 is added to the execution space according to the priority order, and then the first detection task 4 is added to the execution space. It may be understood that, in the case where the priorities of the detection tasks in the task queue to be executed are the same, it may be determined that one of the detection tasks is added to the execution space at random, or the detection tasks added to the execution space may be determined according to the addition order of the detection tasks to the task queue to be executed.

Specifically, for a plurality of data detection tasks submitted to a plurality of commodities, under the condition that the plurality of data detection tasks are submitted to an execution space, the number and the sequence of the data detection tasks submitted to the execution space are controlled through a preset number threshold value set by the execution space (namely the maximum number of tasks which can be executed by the execution space) and the priority set for each data detection task, so that a large amount of resources are prevented from being occupied, the orderly execution of the data detection tasks is ensured, and the data detection tasks are further promoted.

In summary, by controlling the number of tasks concurrently executed in the execution space, the current is limited for the detection tasks added to the execution space, and under the condition that the number exceeds the allowed maximum number of tasks, the tasks are submitted after the front-end tasks executed in the execution space are completed, so that the phenomena of task backlog, resource contention and the like caused by submitting a large number of tasks at the same time are avoided, other online tasks executed in the execution space are not influenced, the influence on data detection is avoided, and the detection efficiency is further improved.

In practical application, when detecting data, a large number of detection tasks are usually executed concurrently, no priority is set for the detection tasks, and due to the manual service flow, the adjustment of the task priority cannot be realized, and when the execution space resources are tense, the resources cannot be obtained, and meanwhile, the queue resources are occupied, so that deadlock is caused, and mutual influence is amplified. Continuing with the foregoing e-commerce field as an example, for index data of a commodity stored in the enterprise database, a plurality of data detection tasks may exist for each commodity. In practical application, priority is not set for each data detection task in advance, so that when tasks are executed, all data detection tasks are executed concurrently, priority cannot be set for each data detection task in the execution process, when resources of an execution space are tense, a plurality of data detection tasks executed concurrently can only wait, queue resources are occupied in the waiting process, mutual influence is caused, the data detection tasks which are already submitted to the execution space cannot be executed, newly generated data detection tasks which are not submitted to the execution space cannot be submitted to the execution space, and the data detection efficiency is reduced.

Based on the method, priority information and the like of the detection task can be pre-configured before data detection, the configuration of the detection task is customized, the flexibility of data detection is ensured, and the specific implementation mode is as follows:

before determining the first object to be detected corresponding to the first detection task, the method further comprises:

receiving a task input instruction sent by a terminal side device, and determining at least one initial detection task;

receiving a priority configuration instruction which is sent by the end side equipment and aims at the at least one initial detection task, and configuring priority information for the at least one initial detection task;

and determining at least one detection task corresponding to the updated object to be detected from the at least one initial detection task.

Specifically, the detection task and the priority information of the detection task may be preconfigured by receiving an instruction of the terminal side device. The method comprises the steps of receiving a task input instruction sent by end side equipment, determining at least one initial detection task according to task content carried in the task input instruction, receiving a priority configuration instruction aiming at each initial detection task and sent by the end side equipment, and configuring priority information for each initial detection task according to priority information carried in the priority configuration instruction. After determining the at least one initial detection task and the priority information of each initial detection task, at least one detection rule corresponding to the updated object to be detected may be determined from the at least one initial detection task.

In practical application, before detecting index data of commodities stored in an enterprise database, a detector can configure detection tasks according to business requirements of an enterprise, and configure priority information of each detection task to divide priority of each detection task, and in the process of executing subsequent data detection tasks, each data detection task can be orderly executed according to preconfigured priority information.

In summary, by determining the initial detection task and the priority information of the initial detection task in advance, the priority of each detection task can be divided, when the execution space resources are tense, the detection task with higher priority can be processed preferentially according to the priority information, the implementation of the detection task is ensured, the definition of the detection task is realized, the flexibility of the detection task is ensured, and different service requirements can be met.

In practical application, the false alarm rate for cross-table detection or fluctuation detection is higher, because data to be detected are stored in different data tables, when the data are detected, the data in the different data tables need to be acquired, however, the problem that the corresponding data cannot be acquired from the tables although the data are updated due to update delay or failure is possibly caused, and depending partitions or tables cannot be output to update to operate detection, so that false alarm is caused, and the detection of the data is affected. For example, in order to ensure that the commodity sales service normally operates, the daily throughput of a commodity is generally greater than the daily sales volume of a previous commodity, so that the commodity can be normally operated, and the condition that the commodity is not available is avoided. Under the condition that the production volume of the commodity is updated, the production volume data of the commodity can be obtained from the data table 1 at the moment, but the sales volume of the commodity stored in the data table 2 may not be updated, so that the sales volume data of the commodity cannot be obtained, at the moment, the data detection task cannot normally operate, early warning is carried out, the early warning is carried out because the data cannot be obtained, but not the early warning is carried out because the data does not meet the service requirement, and the early warning can produce false alarm, so that the data detection is influenced.

Based on the above, the task execution condition of the initial detection task can be preconfigured according to the service requirement, and whether the task execution condition needs to be started or not is preconfigured, and the specific implementation manner is as follows:

and receiving a task execution condition configuration instruction which is sent by the end side equipment and aims at the at least one initial detection task, and configuring task execution condition information for the at least one initial detection task.

The task execution condition may be understood as a condition that the object to be detected needs to satisfy. For example, for a detection task to detect whether the current day throughput is greater than the previous day sales, the task execution condition corresponding to the detection task may be set to store the current day throughput and the previous day sales in the data table, and the initial detection task may be executed only if the data to be detected in the data table satisfies the condition. Alternatively, the task execution condition may be, for example, a condition that both the current partition at time t and the partition at time t-1 exist or that both the data table a and the data table b exist are satisfied.

Based on this, the task execution condition information can be configured for each initial detection task according to the task execution condition configuration instruction for each initial detection task sent by the end-side device.

In addition, whether the task execution condition check needs to be started or not can be determined according to the instruction sent by the end side device.

Specifically, before detecting the data stored in the database, the inspector may configure task execution condition information for each data detection task, for example, for the task of detecting commodity throughput and sales, the inspector may configure task execution condition information for the task as "whether commodity throughput and sales need to be pre-checked are both stored in the database" and start the task execution condition check. Therefore, when the task is executed subsequently, the task execution condition check is performed first to determine whether the commodity production quantity and sales quantity to be detected are stored in the database, and the task is executed only when the task execution condition check is passed, so that early warning caused by the fact that index data to be detected cannot be acquired is avoided, and generation of false alarms is avoided.

In summary, by starting the pre-task execution condition check, it can be ensured that the object to be detected meets the task execution condition, so that the data detection operation cannot report errors, the false alarm rate is reduced, and the flexibility of task configuration detection can be realized by setting whether the task execution condition check needs to be started or not.

Step 204: and determining relevant parameters of the first object to be detected under the condition that the first object to be detected meets the task execution condition of the first detection task.

Specifically, after determining the first object to be detected corresponding to the first detection task, it may be determined whether the first object to be detected meets the task execution condition of the first detection task, and if yes, the relevant parameters of the first object to be detected are determined.

The relevant parameter of the first object to be detected may be understood as a parameter indicating a storage location of the first object to be detected in the database, for example, the relevant parameter of the first object to be detected may be a time parameter, a storage location parameter, etc. of the first object to be detected. The task execution condition may be understood as a condition for the first object to be detected. For example, for a data detection task that detects commodity throughput and sales, the task execution conditions may be that both commodity throughput and sales are stored in a database. In other words, only if the commodity production amount and sales amount are stored in the database, the production amount data and sales amount data can be obtained from the database, and the subsequent data detection task can be completed.

In implementation, when determining that the first object to be detected meets the task execution condition of the first detection task, determining the relevant parameters of the first object to be detected includes:

judging whether the first object to be detected meets the task execution condition of the first detection task or not;

and determining relevant parameters of the first object to be detected under the condition that the first object to be detected meets the task execution condition of the first detection task.

And adding the first detection task to a task queue to be executed under the condition that the first detection task is determined not to meet the task execution condition according to the first object to be detected.

Specifically, whether the first object to be detected meets the task execution condition of the first detection task can be judged, if yes, relevant parameters of the storage position of the first object to be detected in the database are determined, and if not, the first detection task is added to the task queue to be executed.

In practical application, because data index is generated when the data in the database is updated, whether the data is updated or not can be determined according to the data index, and based on the data index, whether the data is updated or not can be determined by checking the data index of the database, and whether the object to be detected exists or not can be checked.

For example, for the first detection task to detect whether the current daily throughput of the commodity is greater than the previous daily sales, in order to ensure the normal operation of the first detection task, it is necessary to ensure that both the current daily throughput and the previous daily sales of the commodity are stored in the database, so as to achieve the acquisition of the current daily throughput data and the previous daily sales data. The first object to be detected is the current day production and the previous day sales, the task execution condition of the first detection task is that the current day production and the previous day sales are stored in the database, and under the condition that the current day production and the previous day sales are stored in the database, the time parameter and the data table information parameter corresponding to the current day production are determined, and the time parameter and the data table information parameter corresponding to the previous day sales are determined, so that the follow-up data acquisition is facilitated. And under the condition that the current first object to be detected does not meet the task execution condition, adding the first detection task to a task queue to be executed, and then checking again.

In summary, by starting the pre-verification, whether the first object to be detected meets the task execution rule is judged, and the running effectiveness of the detection task is ensured, so that the false alarm rate is reduced.

In addition, when determining the relevant parameters of the first object to be detected, whether the acquisition of the pre-parameters needs to be started or not can be determined according to a pre-configured instruction whether the acquisition of the pre-parameters is needed or not. When the configuration is that the pre-parameter acquisition is needed, acquiring relevant parameters of a first object to be detected, for example, the last two partitions of the data table can be acquired, and the situation that the partitions are not produced according to a scheduling period due to overlong running time is avoided, so that rule running failure is caused. Under the condition that the acquisition of the front-end parameters is not required to be started, the relevant parameters of the first object to be detected can be determined directly according to the detection rules corresponding to the first detection task. For example, in the case where the first detection task is to detect whether the throughput of 3 months and 2 days is greater than 0, then the time parameter of the first object to be detected is 3 months and 2 days.

Step 206: and determining a target data acquisition statement corresponding to the first object to be detected according to the related parameters of the first object to be detected.

Specifically, after determining the relevant parameter of the first object to be detected, a target data acquisition statement corresponding to the first object to be detected may be determined according to the relevant parameter.

The target data acquisition statement may be understood as an SQL statement for acquiring data of an object to be detected. And the data of the object to be detected can be obtained by operating the target data acquisition statement.

In specific implementation, an initial SQL statement for data acquisition can be determined, then relevant parameters of an object to be detected to be acquired are added into the initial SQL statement, and the obtained target SQL statement can acquire the data of the object to be detected from a data table corresponding to the relevant parameters, and the specific implementation mode is as follows:

the determining, according to the related parameters of the first object to be detected, a target data acquisition statement corresponding to the first object to be detected includes:

determining an initial data acquisition statement;

and adding the relevant parameters of the first object to be detected to the initial data acquisition statement to obtain a target data acquisition statement corresponding to the first object to be detected.

The initial data acquisition statement may be understood as an SQL statement for data acquisition, and the target data acquisition statement may be understood as an SQL statement for acquiring data of an object to be detected.

Specifically, an initial SQL statement obtained by user data may be obtained from an SQL statement library, and relevant parameters of a first object to be detected are added to the initial SQL statement to obtain a target SQL statement for obtaining data of the first object to be detected.

In addition, for the SQL statement of data acquisition, the SQL statement of data detection and the SQL statement of data detection result output which need to be submitted sequentially in the data detection process, the data acquisition can be realized by only using a single target SQL statement of data acquisition as described above, the SQL statement of data acquisition, the SQL statement of data detection and the SQL statement of data detection result output can be aggregated to obtain an aggregated target statement, and the target statement is operated to obtain the data detection result.

In summary, by determining the target data acquisition statement according to the relevant parameters of the first object to be detected, when the subsequent target data acquisition statement runs, the data of the first object to be detected can be acquired, and the data acquisition is realized only by using the SQL statement of data acquisition, so that the subsequent detection of the acquired data is facilitated, and the detection efficiency is improved.

Step 208: and acquiring the data corresponding to the first object to be detected by using the target data acquisition statement, and detecting the data corresponding to the first object to be detected to obtain a data detection result.

Specifically, after the target data acquisition statement is obtained, the data corresponding to the first object to be detected may be acquired by using the target data acquisition statement, and whether the data corresponding to the first object to be detected satisfies the first detection rule may be determined, so as to obtain a data detection result. And the data detection result can be sent to the end-side equipment, and the end-side equipment displays the data detection result to service personnel.

In practical application, the detection of data is realized by using a plurality of detection SQL sentences, for example, for a detection task, a plurality of SQL sentences are sequentially operated, including data acquisition SQL sentences, judgment SQL sentences, result output SQL sentences and the like, that is, a quality inspection task can acquire data detection results only by submitting a plurality of SQL sentences, queue information occupying more execution space is easy to deadlock, and the execution process in task execution is unsuccessful due to the fact that process resources are not released when the SQL sentences are submitted, so that the failure of data detection is caused. In addition, because the SQL sentences are sequentially operated, the follow-up SQL sentence execution can be performed only when the front SQL sentence operation is needed to be successfully operated, and the execution time of the detection task is easy to be prolonged under the condition of insufficient resources until the whole detection task operation is finished.

Based on this, only the target data acquisition statement may be executed, and the data corresponding to the first object to be detected may be acquired according to the execution result, where the specific implementation manner is as follows:

the obtaining the data corresponding to the first object to be detected by using the target data obtaining statement includes:

And adding the target data acquisition statement into the execution space and executing, and acquiring data corresponding to the first object to be detected according to an execution result.

Specifically, the target data acquisition statement may be executed in the execution space, and the data corresponding to the first object to be detected is acquired according to the execution result.

In addition, the target data acquisition statement can be obtained by aggregating a plurality of detection SQL statements, so that only a single SQL statement is utilized in the subsequent data detection.

In sum, by only using a single target data acquisition statement to acquire data corresponding to the first object to be detected, the number of SQL statements can be reduced, deadlock is avoided, and therefore time consumption for detecting task execution is reduced. Task detail data can be adjusted to be acquired according to service requirements, so that the task number is reduced.

In summary, the method determines the detection task, checks the object to be detected, determines the relevant parameters of the object to be detected under the condition that the object to be detected meets the task execution condition, thereby determining the target data acquisition statement, realizing the acquisition of the data corresponding to the object to be detected, facilitating the detection of the data, avoiding the detection false alarm through the pre-check of the object to be detected, improving the detection efficiency, facilitating the subsequent adjustment or alarm of abnormal data according to the data detection result, and ensuring the quality of the data.

The following describes an object processing method provided in the present specification by taking an application of the object processing method to data detection as an example with reference to fig. 3. Fig. 3 is a flowchart illustrating a processing procedure of an object processing method according to an embodiment of the present disclosure, where the object processing method is applied to cloud-side devices, and specifically includes the following steps.

Step 302: and under the condition that the object to be detected is updated, determining a first object to be detected corresponding to the first detection task.

Specifically, in the case of determining the update of the sales volume of the product stored in the enterprise database in the product data detection scenario of the enterprise production, it may be determined that the first detection task "detects whether the sales volume of the product on the same day is not less than the average value of the sales volume of the product on the same day in 7 days of history", and the first object to be detected corresponding to the first detection task is the sales volume of the product on the same day and the sales volume of the product on the same day in 7 days of history. The first detection task may be preconfigured according to a business requirement of an enterprise, according to a historical sales business condition of the enterprise, the commodity is used as a daily consumable, sales volume is continuous and stable within a period of time, and a situation that sales volume is reduced does not occur, based on the fact, in order to detect sales conditions of the commodity, the sales volume condition of the commodity stored in the database needs to be detected, the first detection task may be configured to detect whether sales volume of the commodity on the current day is not less than an average value of sales volume of the commodity on the current day in 7 days.

Step 304: and adding the first detection task to a task queue to be executed according to the priority information of the first detection task.

Specifically, before data detection, a service person may configure task information of a detection task through an end-side device, and configure priority information of the detection task. Based on this, the first detection task may be added to the task queue to be executed according to priority information of the first detection task configured in advance.

Step 306: submitting the detection task with the priority reaching a preset priority threshold according to the priority information of the detection task in the task queue to be executed.

Specifically, in the embodiment of the present disclosure, an example is described in which the priority of the first detection task reaches the preset priority threshold, and then the detection task submitted here with the priority reaching the preset priority threshold is the first detection task.

Specifically, the priority information preconfigured for the first detection task is 1, which indicates that the priority is highest, and the first detection task with the priority information of 1 is submitted.

Step 308: an execution space for executing the first detection task is determined.

Step 310: and judging whether the number of the detection tasks executed in the execution space reaches a preset number threshold. If yes, go to step 304; if not, go to step 312.

Specifically, when the number of detection tasks executed in the execution space does not reach the preset number threshold, the first detection task may be added to the execution space, so as to implement subsequent execution. In the case that the number of detection tasks executed in the execution space reaches a preset number threshold, the first detection task may be added to the task queue to be executed to wait for execution.

It will be appreciated that step 304 and step 306 may not be performed before checking the number of detection tasks performed in the execution space, that is, after determining the first detection task and the first object to be detected in step 302, the number of detection tasks performed in the execution space may be checked directly, and if the number reaches the preset number threshold, the first detection task may be added to the task queue to wait for execution according to the priority information of the first detection task.

Step 312: and judging whether the first object to be detected meets the task execution condition of the first detection task. If yes, go to step 314. If not, go to step 304.

Specifically, before executing step 312, it may be determined, according to the configuration information of the first detection task, whether the first detection task is started for pre-verification, if so, step 312 is executed, and if not, step 314 is directly executed.

The configuration information of the first detection task may be checked or input by the service personnel through the terminal device. In one embodiment of the present disclosure, if the preamble check is turned on, the configuration information may be denoted by 1. If the preamble check is not enabled, the configuration information may be represented by 0.

Under the condition of starting the pre-verification, the service personnel can configure task execution condition information of the first detection task through the terminal side equipment. For example, for the first detection task "detecting whether the sales of commodity on the same day is not less than the average value of sales of commodity on the same day for 7 days of history", the task execution condition information of the first detection task may be "the sales of commodity on the same day is stored in the enterprise database and the sales of commodity on the same day for 7 days of history is also stored in the enterprise database". Specifically, whether the commodity sales of the first object to be detected on the same day and the commodity sales of the first object to be detected on each day within 7 days of the history are stored in the enterprise database can be checked, and if so, the first object to be detected meets the task execution condition of the first detection task.

Step 314: and determining relevant parameters of the first object to be detected.

Specifically, step 314 may be understood as a step of acquiring a pre-parameter, and similarly, before executing step 314, it may be determined whether the pre-parameter is acquired by the first detection task according to the configuration information of the first detection task, if yes, step 314 is executed, and if not, the relevant parameter of the first object to be detected may be determined directly according to the first detection task. And whether to start the pre-parameter acquisition or not is also configured for the first detection task by the service personnel through the end-side device before the data detection.

For the first detection task, whether the commodity sales on the same day is not smaller than the average value of commodity sales on each day in 7 days of history, and the task execution condition information of the first detection task, that is, commodity sales on the same day in 7 days of history is stored in the enterprise database, the business personnel can configure the first detection task to start the acquisition of the preposed parameters, and at this time, the time parameter of commodity sales on the same day and the time parameter of commodity sales on each day in 7 days of history need to be acquired.

Step 316: and determining a target data acquisition statement corresponding to the first object to be detected according to the related parameters of the first object to be detected.

Specifically, along with the above example, for the first detection task "detect whether the sales of commodity on the same day is not less than the average value of sales of commodity on each day in 7 days of history", in order to be able to run the first detection task, it is necessary to acquire the sales data of commodity on the same day and sales data of commodity on each day in 7 days of history from the database. The time parameter of the commodity sales on the same day and the time parameter of the commodity sales on each day in 7 days of history may be added to the SQL statement for data acquisition to obtain a target data acquisition statement that may be used to acquire commodity sales data on the same day and commodity sales data on each day in 7 days of history.

Step 318: and acquiring the data corresponding to the first object to be detected by using the target data acquisition statement, and detecting the data corresponding to the first object to be detected to obtain a data detection result.

Specifically, when commodity sales data on the same day and commodity sales data on each day in 7 days of history are obtained according to the target data obtaining statement, the target data obtaining statement may be executed in the execution space, the specific value of commodity sales on the same day and the specific value of commodity sales in 7 days of history are obtained according to the execution result, and the average value of commodity sales in 7 days of history is calculated, so as to determine whether the data satisfies the detection rule corresponding to the first detection task, "commodity sales on the same day should be not less than the average value of commodity sales in 7 days of history", so as to obtain the data detection result.

In addition, the data detection result can be sent to the end-side equipment, and the end-side equipment displays the data detection result to service personnel.

Corresponding to the above method embodiments, the present disclosure further provides an embodiment of an object processing apparatus, and fig. 4 shows a schematic structural diagram of an object processing apparatus provided in one embodiment of the present disclosure. As shown in fig. 4, the apparatus includes:

a first determining module 402 configured to determine a first object to be detected corresponding to the first detection task;

a second determining module 404 configured to determine a relevant parameter of the first object to be detected, in case it is determined that the first object to be detected satisfies a task execution condition of the first detection task;

a third determining module 406, configured to determine, according to the related parameters of the first object to be detected, a target data acquisition statement corresponding to the first object to be detected;

the detection module 408 is configured to obtain the data corresponding to the first object to be detected by using the target data obtaining statement, and detect the data corresponding to the first object to be detected, so as to obtain a data detection result.

In an alternative embodiment, the second determining module 404 is further configured to:

In an alternative embodiment, the apparatus further comprises a receiving module configured to:

In an alternative embodiment, the receiving module is further configured to:

In an alternative embodiment, the first determining module 402 is further configured to:

determining an execution space for executing the first detection task;

executing the step of determining a first object to be detected corresponding to a first detection task under the condition that the number of detection tasks executed in the execution space is determined to not reach a preset number threshold;

In an alternative embodiment, the detection module 408 is further configured to:

In an alternative embodiment, the third determining module 406 is further configured to:

determining an initial data acquisition statement;

and determining a first object to be detected corresponding to the first detection task.

In summary, the device determines a detection task, checks an object to be detected, determines relevant parameters of the object to be detected under the condition that the object to be detected meets the task execution condition, thereby determining a target data acquisition statement, realizing acquisition of data corresponding to the object to be detected, facilitating detection of the data, and avoiding detection false alarm through pre-check of the object to be detected, thereby improving detection efficiency, facilitating subsequent adjustment or alarm of abnormal data according to a data detection result, and guaranteeing quality of the data.

The above is a schematic solution of an object processing apparatus of the present embodiment. It should be noted that, the technical solution of the object processing apparatus and the technical solution of the object processing method belong to the same concept, and details of the technical solution of the object processing apparatus, which are not described in detail, can be referred to the description of the technical solution of the object processing method.

Referring to fig. 5, fig. 5 shows a flowchart of another object processing method provided in an embodiment of the present disclosure, which is applied to an end-side device, and before data detection, in order to ensure the customization and flexibility of a detection task, service personnel are required to implement the pre-configuration of the detection task and the detection rule through the end-side device, and specifically includes the following steps.

Step 502: and when the touch operation of the display interface is detected and the touch operation is determined to trigger a data detection request, displaying a task configuration interface through the display interface.

The display interface can be understood as an interface that the terminal device displays to the service personnel. The touch operation may be understood as an operation performed by a business person on a display interface, including, but not limited to, a click operation using a mouse, a double click operation, a box selection operation, a sliding operation, an input operation using a keyboard, and the like.

Based on the above, the terminal device displays the task configuration interface for the service personnel through the display interface under the conditions that the touch operation of the display interface is monitored and the touch operation is determined to trigger the data detection request, so that the service personnel can conveniently configure the detection task at the task configuration interface.

Step 504: and detecting touch operation aiming at a first control in the task configuration interface, determining corresponding task configuration information, and generating at least one initial detection task, wherein the first control is any one of at least one control on the task configuration interface.

Specifically, a touch operation for any one of at least one control in a task configuration interface is detected, service configuration information corresponding to the touch operation is determined, and at least one initial detection task is generated.

It can be appreciated that the initial detection task may be generated at the end-side device, or the service configuration information may be sent to the cloud-side device, where the initial detection task is generated.

In particular implementations, the controls on the task-configuration interface may include a selection control and an input control. When the corresponding task configuration information is determined, the configuration can be performed according to the input operation and/or the touch operation, and the specific implementation manner is as follows:

the detecting determines corresponding task configuration information according to the touch operation of the first control in the task configuration interface, and generates at least one initial detection task, including:

detecting input operation of an input control and/or touch operation of a selection control in the task configuration interface, and determining corresponding task information, priority information corresponding to the task information and task execution condition information;

And generating at least one initial detection task according to the task information, and the priority information and the task execution condition information corresponding to the task information.

Specifically, an input operation for an input control in the task configuration interface may be detected, and corresponding task information may be determined. And detecting input operation of an input control and/or touch operation of a selection control in the task configuration interface, and determining priority information and task execution condition information corresponding to the task information.

For example, an input box may be shown in the task configuration interface, and the end-side device receives task information input in the input box by the service personnel. The task configuration interface can also display a checking frame, and the terminal side equipment receives the selection operation of the service personnel aiming at the checking frame, so that the priority information and the task execution condition information are configured for the detection task, or the terminal side equipment can also receive the priority information and the task execution condition information of the detection task, which are input in the input frame by the service personnel.

Accordingly, at least one initial detection task can be generated at the end side device according to the task information, the priority information corresponding to the task information and the task execution condition information. The task information, the priority information corresponding to the task information, and the task execution condition information may be sent to the cloud-side device, and the cloud-side device may generate at least one initial detection task.

Step 506: and receiving a data detection result obtained based on the at least one initial detection task, and displaying the data detection result through the display interface.

Specifically, after the end side device configures the initial detection task, the data detection result sent by the cloud side device and obtained based on the initial detection task may be received, and the data detection result may be displayed to the service personnel through the display interface. The data detection result may include information such as a detection task, a detection rule, a detection time, a target data acquisition statement, etc., so that a service person can know details of the data detection.

In addition, when the data detection result is that the data does not pass, an alarm notification can be generated according to the task information of the detection task and the data detection result, and the alarm notification is displayed to the business personnel in a push or message reminding mode, so that the alertness of the business personnel to the alarm notification is improved.

In conclusion, the detection task is configured on the terminal side equipment, the data detection result is displayed, the detection of the data is realized, the subsequent adjustment or alarm of abnormal data according to the data detection result is facilitated, and the quality of the data is ensured.

Referring to fig. 6, fig. 6 is a schematic view of a scenario of an object processing method according to an embodiment of the present disclosure. As shown in fig. 6, the end device 602 may present a task configuration interface to a service person, where the service person performs an input operation and a check operation on the task configuration interface, and configures a detection task. For example, the task information "record that the same primary key should not exist in the table" may be input through the input box of the task configuration interface, and the priority information of the detection task is input as 1. And checking whether the front verification and the front parameter are required to be started or not for the detection task through a checking frame of the task configuration interface, and inputting information such as the table name of a data table required to be detected by the detection task. After the task configuration interface configures a series of information of the detection task, the determined detection task may be sent to the cloud-side device 604, or the series of information may be sent to the cloud-side device 604, and the cloud-side device 604 determines the corresponding detection task.

The cloud-side device 604 executes the object processing method, detects the data stored in the data table based on the detection task, finally obtains a data detection result, sends the data detection result to the end-side device 602, and displays the data detection result to the user through the display interface. As shown in table 3, the data detection result may include information such as a table name of the detected data table, a number of detected data, a number of detection tasks, a detection task state, a number of detection task failures, etc., where the data detection result may be displayed to a service person in a list form on a display interface, and correspondingly, for the data detection result, a selection control may be displayed on the display interface, and the service person may process the data detection result by clicking or otherwise operating the selection control.

TABLE 3 Table 3

Table name of data table	Detecting data quantity	Detecting the number of tasks	Detecting task status	Number of failed tasks
					Table A
	1	7	Failure of	2
					Table B	1	6	Failure of	1
Table C	2	2	Successful	0

In addition, for the data detection result of the detection task failure, the cloud-side device 604 may also send, through the other end-side device 602, alarm information to the service personnel, where the alarm information includes the alarm time, the table name of the detected data table, the task information of the detection task, the detection field, the detection rule corresponding to the detection task, and other contents.

Corresponding to the above method embodiment, the present disclosure further provides another embodiment of an object processing apparatus, which is applied to an end-side device, and fig. 7 shows a schematic structural diagram of another object processing apparatus provided in one embodiment of the present disclosure. As shown in fig. 7, the apparatus includes:

the display module 702 is configured to detect a touch operation of the display interface, and display a task configuration interface through the display interface when determining that the touch operation triggers a data detection request;

the generating module 704 is configured to detect a touch operation for a first control in the task configuration interface, determine corresponding task configuration information, and generate at least one initial detection task, where the first control is any one of at least one control on the task configuration interface;

and the receiving module 706 is configured to receive the data detection result obtained based on the at least one initial detection task and display the data detection result through the display interface.

In an alternative embodiment, the controls include an input control and a selection control; the generating module 704 is further configured to:

Corresponding to the above method embodiments, the present disclosure further provides an object processing system embodiment, and fig. 8 shows a schematic structural diagram of an object processing system provided in one embodiment of the present disclosure. As shown in fig. 8, the system 800 includes:

an object detection unit 802, and a data storage unit 804, wherein,

the object detection unit 802 is configured to perform the above-mentioned object processing method, and the data storage unit 804 is configured to store an object to be detected and data corresponding to the object to be detected.

A data storage unit is understood to be a database in which data are stored.

Specifically, the object detection unit 802 may be configured to perform the above-mentioned object processing method, and detect the object to be detected and the data corresponding to the object to be detected stored in the data storage unit 804.

It should be noted that, the object detection unit 802 in the system may detect data stored in the plurality of data storage units 804.

The object detection method can be applied to the field of electronic commerce and is used for detecting index data of commodities, and based on the index data, corresponding to the method embodiment, the embodiment of the specification also provides a commodity data detection method embodiment, and the method comprises the following steps:

The first target to be detected may be understood as the first target to be detected, for example, may be the target data such as the throughput and sales of the target commodity. The target commodity may be understood as a commodity having a need for detection. The first detection task may be understood as a data detection task pre-configured for the index data of the target commodity, which may be configured according to the business requirements.

Specifically, in the case of determining that the index data of the target commodity is updated, or at preset time intervals, a data detection task configured in advance for the index data of the target commodity may be determined, and whether the first to-be-detected index satisfies a task execution condition of the data detection task is determined, and in the case of determining that the first to-be-detected index satisfies a task execution condition of the first detection task, a relevant parameter of the first to-be-detected index is determined, thereby determining the target data acquisition statement. And acquiring data corresponding to the first index to be detected by using the target data acquisition statement, thereby obtaining a data detection result.

For example, for the production amount of the target commodity, in order to ensure sales of the target commodity, the daily production amount of the target commodity should be greater than 0, and based on this, the data detection task configured for this production amount may be "detect whether the daily production amount of the commodity is greater than 0". In the case of determining a daily throughput update of a target commodity stored in the database, it may be determined whether the daily throughput is stored in the database, if so, a time parameter and/or a storage location parameter of the daily throughput may be determined, and the time parameter and/or the storage location parameter may be added to an SQL statement for data acquisition, and a target data acquisition statement may be determined. And when the target data acquisition statement is operated, acquiring daily throughput data of the commodity from a database, and detecting the daily throughput data to obtain a data detection result. If the daily throughput data is larger than 0, the updated daily throughput in the database is indicated to meet the service requirement, if the daily throughput data is smaller than 0, the updated daily throughput in the database is indicated to not meet the service requirement, and an abnormality exists, an alarm is sent to a detector. The detection personnel can check the database according to the alarm, judge whether the business operation of the enterprise is abnormal or the situation of input errors and the like when inputting production data into the database, and further determine the subsequent strategy.

Referring to fig. 9, fig. 9 illustrates a frame diagram of another object handling system provided by an embodiment of the present description, which may include a plurality of end-side devices 902 and cloud-side devices 904. Communication connection can be established between the plurality of end-side devices 902 through the cloud-side device 904, in a data detection scenario, the cloud-side device 904 is used for providing data detection services between the plurality of end-side devices 902, the plurality of end-side devices 902 can be respectively used as presentation ends, and data detection results sent by the cloud-side device 904 are received and presented.

The user may interact with the cloud-side device 904 through the end-side device 902 to receive data sent by other end-side devices 902, or send data to other end-side devices 902, etc. In the data detection scenario, a user may issue task configuration information to the cloud side device 904 through the end side device 902, where the cloud side device 904 determines a detection task according to the configuration information, obtains a data detection result, and pushes the data detection result to other end side devices 902 that establish communication.

Wherein, a connection is established between the end-side device 902 and the cloud-side device 904 through a network. The network provides a medium for communication links between clients and servers. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others. Data transmitted by the end-side device 902 may need to be encoded, transcoded, compressed, etc. before being published to the cloud-side device 904.

The end-side device 902 may be a browser, APP (Application), or web Application such as H5 (HyperText Markup Language, hypertext markup language5 th edition) Application, or a light Application (also referred to as applet, a lightweight Application), or cloud Application, etc., and the end-side device 902 may be based on a software development kit (SDK, software Development Kit) of a corresponding service provided by the service end, such as a real-time communication (RTC, real Time Communication) based SDK development acquisition, etc. The end-side device 902 may be deployed in an electronic device, need to run depending on the device or some APP in the device, etc. The electronic device may for example have a display screen and support information browsing etc. as may be a personal mobile terminal such as a mobile phone, tablet computer, personal computer etc. Various other types of applications are also commonly deployed in electronic devices, such as human-machine conversation type applications, model training type applications, text processing type applications, web browser applications, shopping type applications, search type applications, instant messaging tools, mailbox clients, social platform software, and the like.

Cloud-side device 904 may include servers that provide various services, such as servers that provide communication services for multiple front-ends, as well as servers for background training that provide support for models used on the front-ends, as well as servers that process data sent by the front-ends, and so on. It should be noted that, the cloud-side device 904 may be implemented as a distributed server cluster formed by a plurality of servers, or may be implemented as a single server. The server may also be a server of a distributed system or a server that incorporates a blockchain. The server may also be a cloud server for cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN, content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms, or an intelligent cloud computing server or an intelligent cloud host with artificial intelligence technology.

It should be noted that, the object processing method provided in the embodiment of the present disclosure is generally executed by the server, but in other embodiments of the present disclosure, the client may have a similar function to the server, so as to execute the object processing method provided in the embodiment of the present disclosure. In other embodiments, the object processing method provided in the embodiments of the present disclosure may be performed by a client and a server together.

Fig. 10 illustrates a block diagram of a computing device 1000 provided in accordance with one embodiment of the present description. The components of the computing device 1000 include, but are not limited to, a memory 1010 and a processor 1020. Processor 1020 is coupled to memory 1010 via bus 1030 and database 1050 is used to store data.

Computing device 1000 also includes access device 1040, which access device 1040 enables computing device 1000 to communicate via one or more networks 1060. Examples of such networks include public switched telephone networks (PSTN, public Switched Telephone Network), local area networks (LAN, local Area Network), wide area networks (WAN, wide Area Network), personal area networks (PAN, personal Area Network), or combinations of communication networks such as the internet. The access device 1040 may include one or more of any type of network interface, wired or wireless, such as a network interface card (NIC, network interface controller), such as an IEEE802.11 wireless local area network (WLAN, wireless Local Area Network) wireless interface, a worldwide interoperability for microwave access (Wi-MAX, worldwide Interoperability for Microwave Access) interface, an ethernet interface, a universal serial bus (USB, universal Serial Bus) interface, a cellular network interface, a bluetooth interface, a near-field communication (NFC, near Field Communication) interface, and so forth.

In one embodiment of the present application, the above-described components of computing device 1000, as well as other components not shown in FIG. 10, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device illustrated in FIG. 10 is for exemplary purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as desired.

Computing device 1000 may be any type of stationary or mobile computing device including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smart phone), wearable computing device (e.g., smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or personal computer (PC, personal Computer). Computing device 1000 may also be a mobile or stationary server.

Wherein the processor 1020 is configured to execute computer-executable instructions that, when executed by the processor, perform the steps of the methods described above.

The foregoing is a schematic illustration of a computing device of this embodiment. It should be noted that, the technical solution of the computing device and the technical solution of the object processing method belong to the same concept, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the technical solution of the method.

An embodiment of the present disclosure also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of the above-described method.

The above is an exemplary version of a computer-readable storage medium of the present embodiment. It should be noted that, the technical solution of the storage medium and the technical solution of the object processing method belong to the same concept, and details of the technical solution of the storage medium which are not described in detail can be referred to the description of the technical solution of the method.

An embodiment of the present specification also provides a computer program, wherein the computer program, when executed in a computer, causes the computer to perform the steps of the above method.

The above is an exemplary version of a computer program of the present embodiment. It should be noted that, the technical solution of the computer program and the technical solution of the object processing method belong to the same conception, and details of the technical solution of the computer program, which are not described in detail, can be referred to the description of the technical solution of the method.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The computer instructions include computer program code that may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be increased or decreased appropriately according to the requirements of the patent practice, for example, in some areas, according to the patent practice, the computer readable medium does not include an electric carrier signal and a telecommunication signal.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the embodiments are not limited by the order of actions described, as some steps may be performed in other order or simultaneously according to the embodiments of the present disclosure. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the embodiments described in the specification.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are merely used to help clarify the present specification. Alternative embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the teaching of the embodiments. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. This specification is to be limited only by the claims and the full scope and equivalents thereof.

Claims

1. An object processing method, comprising:

2. The method of claim 1, wherein, in a case where it is determined that the first object to be detected satisfies a task execution condition of the first detection task, determining a relevant parameter of the first object to be detected comprises:

3. The method according to claim 1, further comprising, before determining the first object to be detected corresponding to the first detection task:

4. A method according to claim 3, further comprising:

5. The method according to claim 1, further comprising, before determining the first object to be detected corresponding to the first detection task:

determining an execution space for executing the first detection task;

correspondingly, the acquiring the data corresponding to the first object to be detected by using the target data acquisition statement includes:

6. The method of claim 5, after determining the execution space to execute the first detection task, further comprising:

7. The method of claim 1, wherein the determining, according to the related parameters of the first object to be detected, a target data acquisition statement corresponding to the first object to be detected includes:

determining an initial data acquisition statement;

8. The method of claim 1, further comprising:

9. The method of claim 1, the determining a first object to be detected corresponding to the first detection task, comprising:

10. An object processing method applied to an end-side device comprises the following steps:

11. The method of claim 10, the controls comprising an input control and a selection control;

correspondingly, the detecting determines corresponding task configuration information for the touch operation of the first control in the task configuration interface, and generates at least one initial detection task, including:

12. An object processing system comprises an object detection unit and a data storage unit, wherein,

the object detection unit is configured to perform the method according to any one of claims 1 to 9, and the data storage unit is configured to store an object to be detected and data corresponding to the object to be detected.

13. A merchandise data detection method comprising:

14. A computing device, comprising:

a memory and a processor;

the memory is configured to store computer executable instructions, the processor being configured to execute the computer executable instructions, which when executed by the processor, implement the steps of the method of any one of claims 1 to 11 or 13.