Data detection method, device and equipment, readable storage medium and program product

Abstract

The embodiment of the invention provides a data detection method and device, equipment, a readable storage medium and a program product, and relates to the field of data detection and the like, and application scenes include but are not limited to business data detection scenes. The method comprises the following steps: acquiring a detection alarm rule corresponding to a target service, wherein the detection alarm rule comprises a first service data identifier of each service level, a level relationship between the service levels, and a first alarm condition corresponding to each service level; based on the first service data identifier of each service level, determining service data of each service level of the target service from the to-be-detected service data set; based on the hierarchical relationship among the service levels, determining a target service level of which the service data meets the first alarm condition of the corresponding service level from each service level in sequence, and generating alarm content for each target service level; and based on the alarm content transmission rule, determining target alarm content meeting the alarm content transmission rule in the alarm content set.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically, to a data detection method, apparatus, device, readable storage medium, and program product. Background Technology

[0002] In existing technologies, detection and alarm systems detect and alarm business data on business platforms. If the business data meets the alarm threshold, an alarm is triggered. Detection and alarm systems often perform single-indicator detection and alarm based on the business data reported by the business platform. They cannot analyze the reported business data layer by layer or perform targeted alarms by level. As a result, alarms are frequently triggered for business data with large fluctuations, while alarms are not easily triggered for business data with excessively high alarm thresholds, resulting in low alarm efficiency for business data. Summary of the Invention

[0003] This disclosure addresses the shortcomings of existing methods by proposing a data detection method, apparatus, device, computer-readable storage medium, and computer program product to solve the problem of how to improve the alarm efficiency of business data.

[0004] Firstly, this disclosure provides a data detection method, including: Obtain the target business's business dataset, which includes business data from multiple business levels of the target business. Obtain the preset detection and alarm rules corresponding to the target business. The detection and alarm rules include the first business data identifier of each business level in multiple business levels, the hierarchical relationship between each business level, and the first alarm condition corresponding to each business level in each business level. Based on the first business data identifier of each business level, the business data of each business level of the target business is determined from the business data set to be detected. Based on the hierarchical relationship between various business levels, the target business level that meets the first alarm condition of the corresponding business level is determined from each business level in turn, and alarm content is generated for each target business level. If the target business level is a business level, the target business level is the highest level business level. If the target business level includes at least two business levels, the at least two business levels are at least two consecutive business levels including the highest level business level. Based on the preset alarm content transmission rules, the target alarm content that meets the alarm content transmission rules in the alarm content set is determined, and the target alarm content is transmitted to the target object. The alarm content set includes alarm content for each target business level.

[0005] In one embodiment, the business data of each business layer in the multiple business layers includes business data of multiple dimensions. The detection alarm rules also include the second business data identifier of each dimension in the multiple dimensions of each business layer, the hierarchical relationship between the multiple dimensions, and the second alarm condition corresponding to each dimension in the multiple dimensions of that business layer, and further include: For each business level, based on the second business data identifier of each dimension of that business level, the business data of each dimension of that business level is determined from the business data of that business level; For each target business level, based on the hierarchical relationship between multiple dimensions of the target business level, the target dimensions from the multiple dimensions of the target business level that satisfy the corresponding second alarm conditions are determined in turn, and alarm content for each target dimension is generated. The alarm content collection also includes alarm content for each target dimension.

[0006] In one embodiment, based on the hierarchical relationship between various business layers, the target business layer whose business data meets the first alarm condition of the corresponding business layer is determined sequentially from each business layer, and alarm content for each target business layer is generated, including: Based on the hierarchical relationship between the various business levels, the following judgment operations are performed sequentially in descending order of the hierarchical level of each business level until the business data of the current business level does not meet the first alarm condition corresponding to the current business level, or the business data of the current business level meets the first alarm condition corresponding to the current business level and the current business level is the last level. The judgment operation includes the following steps: Based on the first alarm condition corresponding to the current business level, determine whether the current business level is the target business level; If the current business level is determined to be the target business level, then based on the first alarm condition, an alarm content corresponding to the target business level is generated, and the next business level of the current business level is used as the current business level for the next judgment operation.

[0007] In one embodiment, for each target business level, based on the hierarchical relationship between multiple dimensions of the target business level, the target dimensions from the multiple dimensions of the target business level that satisfy the corresponding second alarm conditions are determined sequentially, and alarm content for each target dimension is generated, including: For each target business level, based on the hierarchical relationship between multiple dimensions of that business level, the following judgment operations are performed sequentially in descending order of dimension level, until the business data of the current dimension does not meet the second alarm judgment condition corresponding to the current dimension, or the business data of the current dimension meets the second alarm condition corresponding to the current dimension and the current dimension is the last dimension level: The judgment operation includes the following steps: Based on the second alarm condition corresponding to the current dimension, determine whether the current dimension is the target dimension; If the current dimension is determined to be the target dimension, then based on the second alarm condition, an alarm content corresponding to the target dimension is generated, and the next dimension of the current dimension is used as the current dimension for the next judgment operation.

[0008] In one embodiment, based on preset alarm content transmission rules, target alarm content that meets the alarm content transmission rules in the alarm content set is determined, and the target alarm content is transmitted to the target object, including at least one of the following: Based on the alarm frequency of each type of alarm content in the preset alarm content transmission rules, the alarm content that matches each type of alarm content is determined from the alarm content set, the matched alarm content is determined as the target alarm content, and the target alarm content is transmitted to the target object according to the alarm frequency. Based on the deduplication strategy for alarm content of the same type in the alarm content transmission rules, the alarm content of the same type in the alarm content set is deduplicated to obtain the deduplicated alarm content. The deduplicated alarm content is determined as the target alarm content and transmitted to the target object within a preset time period.

[0009] In one embodiment, before obtaining the target service's dataset for detection, the method further includes: Retrieve the raw data and store it in the message queue; The raw data in the message queue is divided into multiple arrays based on a preset time interval by a preset distributed streaming data processing engine, and the multiple arrays are stored in a preset database. Obtain the dataset of the target business to be detected, including: Based on a timed triggering data detection request for a target service, an array for the target service is obtained from a preset database. The dataset of services to be detected for the target service includes the array for the target service.

[0010] In one embodiment, transmitting the target alarm content to the target object includes: Display the alarm screen to the target object based on the target alarm content. The alarm screen includes the target alarm content and at least one of the following: Business data at the business level for the target alarm content, business data at the dimension for the target alarm content, the first alarm condition at the business level for the target alarm content, and the second alarm condition at the dimension for the target alarm content.

[0011] Secondly, this disclosure provides a data detection device, comprising: The first processing module is used to obtain the target business's business dataset to be detected, which includes business data from multiple business levels of the target business. The second processing module is used to obtain the preset detection alarm rules corresponding to the target service. The detection alarm rules include the first service data identifier of each service level in multiple service levels, the hierarchical relationship between each service level, and the first alarm condition corresponding to each service level in each service level. The third processing module is used to determine the business data of each business level of the target business from the business data set to be detected based on the first business data identifier of each business level. The fourth processing module is used to determine the target business level from each business level that the business data meets the first alarm condition of the corresponding business level based on the hierarchical relationship between each business level, and to generate alarm content for each target business level. If the target business level is a business level, the target business level is the highest level business level. If the target business level includes at least two business levels, the at least two business levels are at least two consecutive business levels including the highest level business level. The fifth processing module is used to determine the target alarm content in the alarm content set that meets the alarm content transmission rules based on the preset alarm content transmission rules, and transmit the target alarm content to the target object. The alarm content set includes alarm content for each target business level.

[0012] Thirdly, this disclosure provides an electronic device, including: a processor, a memory, and a bus; A bus is used to connect the processor and memory; Memory, used to store operation instructions; A processor is configured to execute the data detection method of the first aspect of this disclosure by invoking operation instructions.

[0013] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that is used to perform the data detection method of the first aspect of this disclosure.

[0014] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data detection method in the first aspect of this disclosure.

[0015] The technical solutions provided in this disclosure have at least the following beneficial effects: The process involves: acquiring a dataset of business data to be detected for the target business, which includes business data from multiple business layers of the target business; acquiring preset detection and alarm rules corresponding to the target business, wherein the detection and alarm rules include the first business data identifier of each business layer, the hierarchical relationship between the business layers, and the first alarm condition corresponding to each business layer; determining the business data of each business layer of the target business from the dataset of business data to be detected based on the first business data identifier of each business layer; determining the target business layer whose business data satisfies the first alarm condition of the corresponding business layer based on the hierarchical relationship between the business layers, and generating alarm content for each target business layer, wherein if the target business layer is a single business layer, it is the highest-level business layer; if the target business layer includes at least two business layers, the at least two business layers include the highest-level business layer. The system operates on a continuous business hierarchy. Based on preset alarm content transmission rules, it identifies target alarm content that meets these rules within the alarm content set and transmits it to the target object. The alarm content set includes alarm content for each target business level. Thus, based on a progressive alarm mechanism (the alarm detection rules include the first business data identifier of each business level, the hierarchical relationship between business levels, and the first alarm condition corresponding to each business level), it determines the root cause of the problem for the target business (e.g., advertising business), i.e., the alarm content set for the target business. Based on alarm content transmission rules (e.g., alarm frequency control, alarm deduplication), it obtains the target alarm content within the alarm content set. The target alarm content is then sent and displayed to the target object (e.g., the advertising platform). This achieves layer-by-layer analysis of business data and targeted alarms by business level, improving the alarm efficiency for the target business, i.e., improving the alarm efficiency for business data. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments of this disclosure will be briefly introduced below.

[0017] Figure 1 This is a schematic diagram of the architecture of the data detection system provided in the embodiments of this disclosure; Figure 2 A flowchart illustrating a data detection method provided in an embodiment of this disclosure; Figure 3 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 4 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 5 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 6 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 7 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 8 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 9 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 10 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 11 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 12 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 13 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 14 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 15 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 16 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 17 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 18 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 19 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 20 A schematic diagram of data detection provided in an embodiment of this disclosure; Figure 21 A flowchart illustrating a data detection method provided in an embodiment of this disclosure; Figure 22 This is a schematic diagram of the structure of a data detection device provided in an embodiment of the present disclosure; Figure 23 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0018] The embodiments of this disclosure are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this disclosure, and do not constitute a limitation on the technical solutions of the embodiments of this disclosure.

[0019] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this disclosure mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element are connected through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” indicates implementation as “A,” or implementation as “B,” or implementation as “A and B.”

[0020] It is understood that in the specific embodiments of this disclosure, data related to data detection is involved. When the above embodiments of this disclosure are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0021] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0022] This disclosure provides a data detection method for a data detection system, which relates to fields such as data detection.

[0023] To better understand and explain the solutions of the embodiments of this disclosure, some technical terms involved in the embodiments of this disclosure will be briefly explained below.

[0024] Rule Engine: Evolved from Inference Engine, the rule engine is a component embedded in an application that separates business decisions from application code and uses predefined semantic modules to write business decisions. The rule engine accepts data input, interprets business rules, and makes business decisions based on those rules.

[0025] Wuji Configuration: Wuji Configuration is a general configuration management system. In essence, Wuji Configuration is a data management system. As more and more people use it to manage configurations, Wuji Configuration has become a configuration system. In other words, Wuji Configuration provides a series of supporting facilities for static data production and consumption, which can be used to fill in various configuration information required by business or services.

[0026] Flink: Flink is a distributed processing engine and framework for state computation of bounded and unbounded data streams. Flink is a stream computing framework mainly used to process streaming data.

[0027] The existing technologies for detecting and alerting business data have the following problems: Existing detection and alarm systems detect and alarm business data from business platforms. If the business data meets an alarm threshold, an alarm is triggered. These systems often detect and alarm based on a single indicator of the business data reported by the platform. For example, current alarm methods typically detect and alarm based on the success or failure of a service interface. In some scenarios, alarms are triggered based on a single indicator reported by the platform, setting thresholds or volatility. Existing technologies cannot analyze the reported business data layer by layer, nor can they provide targeted alarms at different levels. They also cannot aggregate business indicators from multiple perspectives. As a result, alarms are frequently triggered for highly volatile business data, while alarms are not easily triggered for business data with excessively high alarm thresholds, leading to low alarm efficiency. Furthermore, current technologies have limited alarm content, failing to display multiple indicators. Their detection and alarm rules are also limited, failing to detect hierarchical relationships between different business levels. Moreover, alarms are based on data aggregation over a recent period, failing to achieve multi-dimensional data aggregation.

[0028] Based on this, the present disclosure provides a data detection method, apparatus, device, computer-readable storage medium, and program product, and the specific technical solutions will be described in detail below.

[0029] The solutions provided in this disclosure relate to data detection technology. Specific embodiments are described in detail below. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this disclosure will now be described with reference to the accompanying drawings.

[0030] To better understand the solution provided in this disclosure, the solution will be described below in conjunction with a specific application scenario.

[0031] In one embodiment, Figure 1The diagram shows an architecture schematic of a data detection system applicable to embodiments of this disclosure. It is understood that the data detection method provided in these embodiments can be applied to, but is not limited to, applications such as... Figure 1 In the application scenarios shown.

[0032] In this example, as Figure 1 As shown, the architecture of the data detection system in this example may include, but is not limited to, server 10, terminal 20, and database 30. Server 10, terminal 20, and database 30 can interact with each other via network 40.

[0033] Server 10 acquires the target service's data set to be tested, which includes service data from multiple service layers of the target service. Server 10 acquires preset detection alarm rules corresponding to the target service, wherein the detection alarm rules include the first service data identifier of each service layer, the hierarchical relationship between the service layers, and the first alarm condition corresponding to each service layer. Based on the first service data identifier of each service layer, Server 10 determines the service data of each service layer of the target service from the data set to be tested. Based on the hierarchical relationship between the service layers, Server 10 sequentially retrieves service data from each service layer. The system determines the target business layer whose business data meets the first alarm condition of the corresponding business layer, and generates alarm content for each target business layer. If the target business layer is a single business layer, it is the highest-level business layer. If the target business layer includes at least two business layers, these at least two consecutive business layers include the highest-level business layer. Based on preset alarm content transmission rules, the server 10 determines the target alarm content in the alarm content set that meets the rules and transmits it to the target object. The alarm content set includes alarm content for each target business layer. The server 10 stores the target alarm content in the database 30 and transmits it to the terminal 20 for display, such as the target object.

[0034] It is understood that the above is only one example, and this embodiment is not limited here.

[0035] Terminals include, but are not limited to, smartphones (such as Android phones, iOS phones, etc.), mobile phone emulators, tablets, laptops, digital broadcast receivers, MIDs (Mobile Internet Devices), PDAs (Personal Digital Assistants), smart voice interaction devices, smart home appliances, and in-vehicle terminals.

[0036] A server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server or server cluster that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0037] The aforementioned networks may include, but are not limited to, wired networks and wireless networks. Wired networks include local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs). Wireless networks include Bluetooth, Wi-Fi, and other networks that enable wireless communication. Specific details can be determined based on actual application scenario requirements and are not limited here.

[0038] See Figure 2 , Figure 2 This illustration shows a flowchart of a data detection method provided in an embodiment of this disclosure. The method can be executed by any electronic device, such as a server. As an optional implementation, the method can be executed by a server. For ease of description, in the following description of some optional embodiments, the method will be executed by a server as an example of the execution subject. Figure 2 As shown, the data detection method provided in this embodiment includes the following steps: S201, Obtain the target business's business dataset to be tested. The business dataset to be tested includes business data from multiple business layers of the target business.

[0039] Specifically, the target business includes, for example, advertising, e-commerce, and payment services. The data types of the business data in the target business's dataset include, for example, task data and statistical data. The hierarchical levels of each business level are arranged in descending order as: first business level, second business level, third business level, etc., and each business level corresponds to its corresponding business data.

[0040] S202, obtain the preset detection alarm rules corresponding to the target service, wherein the detection alarm rules include the first service data identifier of each service level in multiple service levels, the hierarchical relationship between each service level, and the first alarm condition corresponding to each service level in each service level.

[0041] Specifically, the first business data identifier at each business level includes, for example, the number of clicks on an ad video or the number of ad deliveries to the ad platform. The hierarchical relationship between the business levels is as follows: the four business levels are arranged in descending order as the first, second, third, and fourth business levels, with the first business level being the highest level. If the business data of each business level meets the corresponding first alarm condition, an alarm is generated for that business level. For example, the first alarm condition might be that today's ad video clicks are less than 80% of yesterday's ad video clicks.

[0042] S203, based on the first business data identifier of each business level, determine the business data of each business level of the target business from the business data set to be detected.

[0043] Specifically, based on the first business data identifier of a certain business level, the business data corresponding to the first business data identifier of that business level is determined from the business dataset to be detected. For example, the first business data identifier of the business level is the number of clicks (click count) of an advertisement video. Based on the number of clicks of the advertisement video, the business data corresponding to the number of clicks of the advertisement video is determined from the business dataset to be detected. For example, the business data corresponding to the number of clicks of the advertisement video is 10000.

[0044] S204, based on the hierarchical relationship between various business layers, determine the target business layer from each business layer in turn to ensure that the business data meets the first alarm condition of the corresponding business layer, and generate alarm content for each target business layer. Wherein, if the target business layer is a business layer, the target business layer is the highest level business layer; if the target business layer includes at least two business layers, the at least two business layers are at least two consecutive business layers including the highest level business layer.

[0045] Specifically, if the business data at the target business level meets the first alarm condition, an alarm is generated for that target business level. For example, if there is one target business level (the highest-level business level) among all business levels, an alarm is generated for that target business level. Alternatively, if there are at least two consecutive target business levels among all business levels, and one of these consecutive target business levels is the highest-level business level, alarms are generated for both consecutive target business levels. For instance, if all business levels include a first, second, third, and fourth business level, with the first business level being the highest-level business level, and the first, second, and third business levels are target business levels, alarms are generated for the first, second, and third business levels, respectively.

[0046] S205, based on the preset alarm content transmission rules, determine the target alarm content in the alarm content set that meets the alarm content transmission rules, and transmit the target alarm content to the target object. The alarm content set includes alarm content for each target business level.

[0047] Specifically, based on preset alarm content transmission rules, target alarm content that meets the alarm content transmission rules in the alarm content set is determined through alarm frequency control, alarm deduplication, etc., and the target alarm content is displayed to the target object, such as an advertising business platform, a middle platform (push middle platform), etc.

[0048] In this embodiment, a target service data set to be detected is obtained, which includes service data from multiple service levels of the target service; a preset detection alarm rule corresponding to the target service is obtained, wherein the detection alarm rule includes a first service data identifier for each service level in the multiple service levels, a hierarchical relationship between the service levels, and a first alarm condition corresponding to each service level; based on the first service data identifier of each service level, the service data of each service level of the target service is determined from the target service data set to be detected; based on the hierarchical relationship between the service levels, the target service level whose service data satisfies the first alarm condition of the corresponding service level is determined sequentially from each service level, and alarm content is generated for each target service level, wherein if the target service level is one service level, the target service level is the highest-level service level; if the target service level includes at least two service levels, the at least two service levels include the highest-level service level. At least two consecutive business layers are involved. Based on preset alarm content transmission rules, target alarm content that meets the alarm content transmission rules in the alarm content set is determined, and the target alarm content is transmitted to the target object. The alarm content set includes alarm content for each target business layer. Thus, based on a hierarchical alarm mechanism (the alarm detection rules include the first business data identifier of each business layer in multiple business layers, the hierarchical relationship between each business layer, and the first alarm condition corresponding to each business layer), the root cause of the problem for the target business (e.g., advertising business) is determined, i.e., the alarm content set for the target business. Based on alarm content transmission rules (e.g., alarm frequency control, alarm deduplication, etc.), the target alarm content in the alarm content set is obtained. The target alarm content is sent and displayed to the target object (e.g., advertising platform). This achieves layer-by-layer analysis of business data and targeted alarms by business layer, improving the alarm efficiency for the target business, i.e., improving the alarm efficiency of business data.

[0049] In one embodiment, the business data of each business layer in the multiple business layers includes business data of multiple dimensions. The detection alarm rules also include the second business data identifier of each dimension in the multiple dimensions of each business layer, the hierarchical relationship between the multiple dimensions, and the second alarm condition corresponding to each dimension in the multiple dimensions of that business layer, and further include: For each business level, based on the second business data identifier of each dimension of that business level, the business data of each dimension of that business level is determined from the business data of that business level; For each target business level, based on the hierarchical relationship between multiple dimensions of the target business level, the target dimensions from the multiple dimensions of the target business level that satisfy the corresponding second alarm conditions are determined in turn, and alarm content for each target dimension is generated. The alarm content collection also includes alarm content for each target dimension.

[0050] Specifically, business-level dimensions include business dimensions, channel dimensions, and vendor dimensions. Business dimensions include functions (user interaction information), urgency (emergency information), operations (operational information), and strategies (personalized information). Channel dimensions include iOS channels and Android channels. Vendor dimensions include vendor 1, vendor 2, vendor 3, and vendor 4. The second business data identifier for each dimension is, for example, the distribution rate corresponding to the vendor dimension.

[0051] For example, such as Figure 3 As shown, the multiple business layers include a first business layer, a second business layer, a third business layer, and a fourth business layer. The hierarchy of the first, second, third, and fourth business layers is from highest to lowest, with the first business layer being the highest level. In the fourth business layer: the advertising platform receives 1 billion messages, such as video ads. The advertising platform sends 800 million messages to the middle platform, resulting in a delivery rate of 8 / 10 = 80%. However, due to policy interception, filtering, and deduplication, the advertising platform ultimately delivers only 800 million messages to the middle platform. In the third business layer: the middle platform receives 800 million messages and sends 400 million messages to various types of channels, such as iOS channels and Android channels. For channels of type d, the distribution rate of the middle platform is 4 / 8 = 50%. Due to policy interception and filtering / deduplication, the middle platform ultimately distributes 400 million messages to various types of channels. In the second business layer: various types of channels receive 400 million messages and send 200 million messages to the vendor. The total distribution rate of all channels (various types) is 2 / 4 = 50%. Again, due to policy interception and filtering / deduplication, 200 million messages are ultimately distributed to the vendor. In the first business layer: the vendor receives 200 million messages and sends 100 million messages to 100 million terminals. The delivery rate (the vendor's distribution rate) is 1 / 2 = 50%. If 50 million of these 100 million messages are clicked by the corresponding terminal users, the click-through rate is 50% (0.5 / 1 = 50%).

[0052] For example, such as Figure 3As shown, the first alarm condition corresponding to the first business level includes a delivery rate of less than 90%, the first alarm condition corresponding to the second business level includes a total channel delivery rate of less than 90%, the first alarm condition corresponding to the third business level includes a delivery rate of less than 90% for the middle platform, and the first alarm condition corresponding to the fourth business level includes a delivery rate of less than 90% for the advertising business platform. Here, 90% is a threshold. Based on the first alarm condition corresponding to the first business level (delivery rate less than 90%), it is determined that the business data of the first business level (delivery rate 1 / 2 = 50%) meets the first alarm condition, i.e., a delivery rate of 50% is less than 90%. Therefore, the first alarm condition is determined to be... A target business layer is defined, and based on the first alarm condition corresponding to the first business layer, alarm content corresponding to the first business layer is generated. The alarm content corresponding to the first business layer includes a delivery rate of 50%, which is less than 90%. Based on the first alarm condition corresponding to the second business layer (total channel delivery rate is less than 90%), it is determined that the business data of the second business layer (total channel delivery rate is 2 / 4 = 50%) meets the first alarm condition, that is, the total channel delivery rate of 50% is less than 90%. The second business layer is then determined as the target business layer, and based on the first alarm condition corresponding to the second business layer, a second business layer is generated. The alarm content corresponding to the second business level includes a total channel delivery rate of 50%, which is less than 90%. Based on the first alarm condition corresponding to the third business level (the delivery rate of the middle platform is less than 90%), it is determined that the business data of the third business level (the delivery rate of the middle platform is 4 / 8=50%) meets the first alarm condition, that is, the delivery rate of the middle platform is 50% less than 90%. The third business level is determined as the target business level, and based on the first alarm condition corresponding to the third business level, the alarm content corresponding to the third business level is generated. The alarm content corresponding to the third business level includes the middle platform's delivery rate of 50%. 0%, the distribution rate of this middle platform is less than 90%; based on the first alarm condition corresponding to the fourth business level (the distribution rate of the advertising business platform is less than 90%), it is determined that the business data of the fourth business level (the distribution rate of the advertising business platform is 8 / 10=80%) meets the first alarm condition, that is, the distribution rate of the advertising business platform is 80% less than 90%, the fourth business level is determined as the target business level, and based on the first alarm condition corresponding to the fourth business level, alarm content corresponding to the fourth business level is generated. The alarm content corresponding to the fourth business level includes the distribution rate of the advertising business platform being 80%, and the distribution rate of the advertising business platform being less than 90%.

[0053] For example, such as Figure 4 As shown, for Figure 3The first business level is determined by identifying the business data of each dimension of the first business level based on the second business data identifier of each dimension of the first business level. The multiple dimensions of the first business level are business dimension, channel dimension and vendor dimension. Function (user interaction information), emergency (emergency information), operation (operation information) and strategy (personalized information) are all business dimensions. iOS type channels and Android type channels are both channel dimensions. Vendor 1, vendor 2, vendor 3, vendor 4, vendor 5 and vendor 6 are all vendor dimensions.

[0054] For example, such as Figure 4 As shown, in the first business layer: the manufacturer receives 200 million messages and sends 100 million messages to 100 million terminals, with a delivery rate (the manufacturer's delivery rate) of 1 / 2 = 50%. Based on the first alarm condition corresponding to the first business layer (delivery rate less than 90%), it is determined that the business data of the first business layer (delivery rate of 1 / 2 = 50%) meets the first alarm condition, i.e., the delivery rate of 50% is less than (below) 90%. The first business layer is determined as the target business layer, and based on the first alarm condition corresponding to the first business layer, alarm content corresponding to the first business layer is generated. The alarm content corresponding to the first business layer includes a delivery rate of 50%, which is less than 90%. The first business layer has multiple dimensions, including business dimension, channel dimension, and manufacturer dimension. Among them, the dimension levels of business dimension, channel dimension, and manufacturer dimension are from high to low, with business dimension being the highest level dimension. Function (user interaction information), emergency (emergency information), operation (operational information), and strategy (personalized information) are all business dimensions. iOS type channels and Android type channels are both channel dimensions. Vendors 1, 2, 3, 4, 5, and 6 are all vendor dimensions. If the arrival rate of a business dimension (e.g., user interaction information) reaches 60% and meets the second alarm condition (the arrival rate of the business dimension is less than 90%), then an alarm is generated for the business dimension. The alarm content includes the arrival rate of the business dimension reaching 60% and the arrival rate of the business dimension reaching less than 90%. Here, 90% is a threshold. If the business dimension is the target dimension and drills down to the channel dimension, and the arrival rate of the channel dimension (e.g., an iOS type channel) reaches 10% and meets the second alarm condition (the arrival rate of the channel dimension reaching less than 90%), then an alarm is generated for the channel dimension. The alarm content includes the arrival rate of the channel dimension reaching 10% and the arrival rate of the channel dimension reaching less than 90%. If the channel dimension is the target dimension and drills down to the vendor dimension, and the arrival rate of a vendor dimension (e.g., vendor 1) reaches 10% and meets the second alarm condition (the arrival rate of the vendor dimension reaching less than 90%), then an alarm is generated for the vendor dimension. The alarm content includes the arrival rate of the vendor dimension reaching 10% and the arrival rate of the vendor dimension reaching less than 90%.

[0055] For example, such as Figure 5 As shown, the business hierarchy is Figure 3 The second, third, or fourth business level in the process involves determining the business data for each dimension of the business level based on the second business data identifier of each dimension of that business level. The multiple dimensions of this business level are business dimensions, channel dimensions, and vendor dimensions. Functions (user interaction information), emergency (emergency information), operations (operational information), and strategies (personalized information) are all business dimensions. iOS-type channels and Android-type channels are both channel dimensions. Vendor 1, Vendor 2, Vendor 3, Vendor 4, Vendor 5, and Vendor 6 are all vendor dimensions.

[0056] For example, such as Figure 5 As shown, the business hierarchy is Figure 3 The second, third, or fourth business level in the context is the target business level. The alarm content corresponding to this business level includes a delivery rate of 50%, which is less than (or lower than) 90%. This business level has multiple dimensions, including business dimensions, channel dimensions, and vendor dimensions. The dimensions are ranked from highest to lowest, with business dimensions being the highest level. Functions (user interaction information), urgency (emergency information), operations (operational information), and strategies (personalized information) are all business dimensions. iOS and Android channels are both channel dimensions. Vendor 1, Vendor 2, Vendor 3, Vendor 4, Vendor 5, and Vendor 6 are all vendor dimensions. If the delivery rate of a business dimension (e.g., user interaction information) is 50%, satisfying the second alarm condition (a delivery rate of less than 90% for the business dimension), then... Generate alarm content for the business dimension, including a delivery rate of 50% and a delivery rate less than (or lower than) 90% for the business dimension; if the business dimension is the target dimension and drilled down to the channel dimension, and the delivery rate of the channel dimension (e.g., an Android type channel) is 30%, satisfying the second alarm condition (the delivery rate of the channel dimension is less than 90%), then generate alarm content for the channel dimension, including a delivery rate of 30% and a delivery rate less than 90% for the channel dimension; if the channel dimension is the target dimension and drilled down to the vendor dimension, and the delivery rate of the vendor dimension (e.g., vendor 2) is 30%, satisfying the second alarm condition (the delivery rate of the vendor dimension is less than 90%), then generate alarm content for the vendor dimension, including a delivery rate of 30% and a delivery rate less than 90% for the vendor dimension.

[0057] It should be noted that, based on a hierarchical alarm mechanism, detection of layer A (e.g., Figure 3If the business data at layer A is abnormal, drill down to each dimension of layer A (e.g., the first business layer). Figure 4 (Business dimension, channel dimension, and vendor dimension), drill down along each dimension (e.g.) Figure 4 The alerting process involves drilling down from the business dimension to the channel dimension, and from the channel dimension to the vendor dimension to refine the granularity; and then further down to the next lower level B (e.g., ...). Figure 3 The second business layer (B) checks for anomalies in the business data of layer B. If anomalies are found in the business data of layer B, it drills down to each dimension of layer B (e.g., ...). Figure 5 The system performs detailed alerts based on business, channel, and vendor dimensions. It then checks the business data at the next lower level (C-layer) for anomalies, and so on, analyzing each layer downwards and drilling down through each dimension until no anomalies are found in the business data at the next level. This enables layer-by-layer analysis of business data and targeted alerts based on business level, improving the efficiency of alerts for the target business, i.e., improving the efficiency of alerts for business data.

[0058] In one embodiment, based on the hierarchical relationship between various business layers, the target business layer whose business data meets the first alarm condition of the corresponding business layer is determined sequentially from each business layer, and alarm content for each target business layer is generated, including: Based on the hierarchical relationship between the various business levels, the following judgment operations are performed sequentially in descending order of the hierarchical level of each business level until the business data of the current business level does not meet the first alarm condition corresponding to the current business level, or the business data of the current business level meets the first alarm condition corresponding to the current business level and the current business level is the last level. The judgment operation includes the following steps: Based on the first alarm condition corresponding to the current business level, determine whether the current business level is the target business level; If the current business level is determined to be the target business level, then based on the first alarm condition, an alarm content corresponding to the target business level is generated, and the next business level of the current business level is used as the current business level for the next judgment operation.

[0059] Specifically, for example, such as Figure 3As shown, the multiple business layers include a first business layer, a second business layer, a third business layer, and a fourth business layer. The hierarchy of the first, second, third, and fourth business layers is from highest to lowest, with the first business layer being the highest level. In the fourth business layer: the advertising platform receives 1 billion messages, such as video ads. The advertising platform sends 800 million messages to the middle platform, resulting in a delivery rate of 8 / 10 = 80%. However, due to policy interception, filtering, and deduplication, the advertising platform ultimately delivers only 800 million messages to the middle platform. In the third business layer: the middle platform receives 800 million messages and sends 400 million messages to various types of channels, such as iOS channels and Android channels. For channels of type d, the distribution rate of the middle platform is 4 / 8 = 50%. Due to policy interception and filtering / deduplication, the middle platform ultimately distributes 400 million messages to various types of channels. In the second business layer: various types of channels receive 400 million messages and send 200 million messages to the vendor. The total distribution rate of all channels (various types) is 2 / 4 = 50%. Again, due to policy interception and filtering / deduplication, 200 million messages are ultimately distributed to the vendor. In the first business layer: the vendor receives 200 million messages and sends 100 million messages to 100 million terminals. The delivery rate (the vendor's distribution rate) is 1 / 2 = 50%. If 50 million of these 100 million messages are clicked by the corresponding terminal users, the click-through rate is 50% (0.5 / 1 = 50%).

[0060] For example, such as Figure 3As shown, the first alarm condition corresponding to the first business level includes a delivery rate of less than 90%; the first alarm condition corresponding to the second business level includes a total channel delivery rate of less than 90%; the first alarm condition corresponding to the third business level includes a delivery rate of less than 90% for the middle platform; and the first alarm condition corresponding to the fourth business level includes a delivery rate of less than 90% for the advertising business platform. Based on the first alarm condition corresponding to the first business level (delivery rate less than 90%), it is determined that the business data of the first business level (delivery rate 1 / 2 = 50%) meets the first alarm condition, i.e., the delivery rate 50% is less than 90%, thus the first business level is determined as the target... The system identifies the target business layer and generates alarm content for it based on the first alarm condition. This alarm content includes a delivery rate of 50%, which is less than 90%. Based on the first alarm condition for the second business layer (total channel delivery rate less than 90%), the system determines that the business data for the second business layer (total channel delivery rate of 2 / 4 = 50%) meets the first alarm condition, i.e., the total channel delivery rate of 50% is less than 90%. Therefore, the second business layer is designated as the target business layer, and alarm content for the second business layer is generated based on its corresponding alarm condition. The alarm content for the second business level includes a total channel delivery rate of 50%, which is less than 90%. Based on the first alarm condition for the third business level (the middle platform's delivery rate is less than 90%), it is determined that the business data of the third business level (the middle platform's delivery rate is 4 / 8 = 50%) meets the first alarm condition, i.e., the middle platform's delivery rate of 50% is less than 90%. Therefore, the third business level is determined as the target business level, and based on the first alarm condition for the third business level, alarm content corresponding to the third business level is generated. The alarm content for the third business level includes a middle platform delivery rate of 50%. The distribution rate of this middle platform is less than 90%. Based on the first alarm condition corresponding to the fourth business level (the distribution rate of the advertising business platform is less than 90%), it is determined that the business data of the fourth business level (the distribution rate of the advertising business platform is 8 / 10=80%) meets the first alarm condition, that is, the distribution rate of the advertising business platform is 80% less than 90%. The fourth business level is determined as the target business level. Based on the first alarm condition corresponding to the fourth business level, alarm content corresponding to the fourth business level is generated. The alarm content corresponding to the fourth business level includes the distribution rate of the advertising business platform being 80%, which is less than 90%.

[0061] It should be noted that, based on the hierarchical alarm mechanism, the business data is analyzed layer by layer and targeted alarms are issued according to the business level, which improves the alarm efficiency for the target business, that is, improves the alarm efficiency of business data.

[0062] In one embodiment, for each target business level, based on the hierarchical relationship between multiple dimensions of the target business level, the target dimensions from the multiple dimensions of the target business level that satisfy the corresponding second alarm conditions are determined sequentially, and alarm content for each target dimension is generated, including: For each target business level, based on the hierarchical relationship between multiple dimensions of that business level, the following judgment operations are performed sequentially in descending order of dimension level, until the business data of the current dimension does not meet the second alarm judgment condition corresponding to the current dimension, or the business data of the current dimension meets the second alarm condition corresponding to the current dimension and the current dimension is the last dimension level: The judgment operation includes the following steps: Based on the second alarm condition corresponding to the current dimension, determine whether the current dimension is the target dimension; If the current dimension is determined to be the target dimension, then based on the second alarm condition, an alarm content corresponding to the target dimension is generated, and the next dimension of the current dimension is used as the current dimension for the next judgment operation.

[0063] Specifically, for example, such as Figure 4 As shown, for Figure 3 The first business level is determined by identifying the business data of each dimension of the first business level based on the second business data identifier of each dimension of the first business level. The multiple dimensions of the first business level are business dimension, channel dimension and vendor dimension. Function (user interaction information), emergency (emergency information), operation (operation information) and strategy (personalized information) are all business dimensions. iOS type channels and Android type channels are both channel dimensions. Vendor 1, vendor 2, vendor 3, vendor 4, vendor 5 and vendor 6 are all vendor dimensions.

[0064] For example, such as Figure 4As shown, in the first business layer: the manufacturer receives 200 million messages and sends 100 million messages to 100 million terminals, resulting in a delivery rate (the manufacturer's delivery rate) of 1 / 2 = 50%. Based on the first alarm condition corresponding to the first business layer (delivery rate less than 90%), it is determined that the business data of the first business layer (delivery rate of 1 / 2 = 50%) meets the first alarm condition, i.e., the delivery rate of 50% is less than 90%. Therefore, the first business layer is determined as the target business layer, and alarm content corresponding to the first business layer is generated based on the first alarm condition. The alarm content corresponding to the first business layer includes the delivery rate of 50%, which is less than 90%. The first business layer has multiple dimensions, including business dimension, channel dimension, and manufacturer dimension. Among them, the dimension levels of business dimension, channel dimension, and manufacturer dimension are from high to low, with business dimension being the highest level dimension. Function (user interaction information), emergency (emergency information), operation (operational information), and strategy (personalized information) are all business dimensions. iOS type channels and Android type channels are both channel dimensions. Vendor 1, Vendor 2, Vendor 3, Vendor 4, Vendor 5, and Vendor 6 are all vendor dimensions. If the arrival rate of a business dimension (e.g., user interaction information) reaches 60% and meets the second alarm condition (the arrival rate of the business dimension is less than 90%), then an alarm is generated for the business dimension, including the arrival rate of 60% and the arrival rate of less than 90%. If the business dimension is the target dimension and drills down to the channel dimension, and the arrival rate of a channel dimension (e.g., an iOS type channel) reaches 10% and meets the second alarm condition (the arrival rate of the channel dimension is less than 90%), then an alarm is generated for the channel dimension, including the arrival rate of 10% and the arrival rate of less than 90%. If the channel dimension is the target dimension and drills down to the vendor dimension, and the arrival rate of a vendor dimension (e.g., Vendor 1) reaches 10% and meets the second alarm condition (the arrival rate of the vendor dimension is less than 90%), then an alarm is generated for the vendor dimension, including the arrival rate of 10% and the arrival rate of less than 90%.

[0065] For example, such as Figure 5 As shown, the business hierarchy is Figure 3The second, third, or fourth business level in the process involves determining the business data for each dimension of the business level based on the second business data identifier of each dimension of that business level. The multiple dimensions of this business level are business dimensions, channel dimensions, and vendor dimensions. Functions (user interaction information), emergency (emergency information), operations (operational information), and strategies (personalized information) are all business dimensions. iOS-type channels and Android-type channels are both channel dimensions. Vendor 1, Vendor 2, Vendor 3, Vendor 4, Vendor 5, and Vendor 6 are all vendor dimensions.

[0066] For example, such as Figure 5 As shown, the business hierarchy is Figure 3 The second, third, or fourth business level in the system is the target business level. The alarm content corresponding to this business level includes a delivery rate of 50% or less than 90%. This business level has multiple dimensions, including business dimensions, channel dimensions, and vendor dimensions. The business dimension, channel dimension, and vendor dimension are ranked from highest to lowest, with the business dimension being the highest level. Functions (user interaction information), urgency (emergency information), operations (operational information), and strategies (personalized information) are all business dimensions. iOS and Android channels are both channel dimensions. Vendor 1, Vendor 2, Vendor 3, Vendor 4, Vendor 5, and Vendor 6 are all vendor dimensions. If the delivery rate of a business dimension (e.g., user interaction information) is 50%, satisfying the second alarm condition (the delivery rate corresponding to the business dimension is less than 90%), then... If an alarm is generated for the business dimension, the alarm content includes a delivery rate of 50% and a delivery rate of less than 90% for the business dimension. If the business dimension is the target dimension and drilled down to the channel dimension, and the delivery rate of the channel dimension (e.g., an Android type channel) is 30%, satisfying the second alarm condition (the delivery rate of the channel dimension is less than 90%), then an alarm is generated for the channel dimension, including a delivery rate of 30% and a delivery rate of less than 90% for the channel dimension. If the channel dimension is the target dimension and drilled down to the vendor dimension, and the delivery rate of the vendor dimension (e.g., vendor 2) is 30%, satisfying the second alarm condition (the delivery rate of the vendor dimension is less than 90%), then an alarm is generated for the vendor dimension, including a delivery rate of 30% and a delivery rate of less than 90% for the vendor dimension.

[0067] It should be noted that, based on a hierarchical alarm mechanism, detection of layer A (e.g., Figure 3 If the business data at layer A is abnormal, drill down to each dimension of layer A (e.g., the first business layer). Figure 4(Business dimension, channel dimension, and vendor dimension), drill down along each dimension (e.g.) Figure 4 The alerting process involves drilling down from the business dimension to the channel dimension, and from the channel dimension to the vendor dimension to refine the granularity; and then further down to the next lower level B (e.g., ...). Figure 3 The second business layer (B) checks for anomalies in the business data of layer B. If anomalies are found in the business data of layer B, it drills down to each dimension of layer B (e.g., ...). Figure 5 The system performs detailed alerts based on business, channel, and vendor dimensions. It then checks the business data at the next lower level (C-layer) for anomalies, and so on, analyzing each layer downwards and drilling down through each dimension until no anomalies are found in the business data at the next level. This enables layer-by-layer analysis of business data and targeted alerts based on business level, improving the efficiency of alerts for the target business, i.e., improving the efficiency of alerts for business data.

[0068] In one embodiment, based on preset alarm content transmission rules, target alarm content that meets the alarm content transmission rules in the alarm content set is determined, and the target alarm content is transmitted to the target object, including at least one of the following: Based on the alarm frequency of each type of alarm content in the preset alarm content transmission rules, the alarm content that matches each type of alarm content is determined from the alarm content set, the matched alarm content is determined as the target alarm content, and the target alarm content is transmitted to the target object according to the alarm frequency. Based on the deduplication strategy for alarm content of the same type in the alarm content transmission rules, the alarm content of the same type in the alarm content set is deduplicated to obtain the deduplicated alarm content. The deduplicated alarm content is determined as the target alarm content and transmitted to the target object within a preset time period.

[0069] Specifically, for example, such as Figure 6As shown, the data detection system includes a detection and alarm module, a data statistics module, and a task module. The task module includes a database (e.g., an MDB database) and a service module (push-task-read). The database stores task data, such as task-specific information like task issuance time and the number of users to whom the task was issued. The task module can be applied to different business scenarios; for example, in an advertising system, it can store advertising-related attribute information. The push-task-read service module reads task data from the database. The data statistics module is mainly used for collecting and processing statistical data. Depending on the business scenario, the method of collecting statistical data can vary, such as real-time collection or offline collection. The collected statistical data is aggregated and written to the database (e.g., a relational database management system like MySQL or MD). In the database (e.g., b database); the push statistics service in the data statistics module provides a MySQL proxy (proxy layer) to the outside world. The push statistics service provides different interfaces according to business scenarios, processes the statistical data in the database and returns it. The push statistics service accesses the statistical data in the database, performs preliminary processing on the statistical data, and outputs it to the caller through the interface; the middle platform push (the pusher of statistical data) sends the statistical data to TPNS (a third-party service) in the data statistics module. TPNS feeds the statistical data back through Kafka. The push middle platform consumes the statistical data in Kafka (a distributed message queue based on the publish / subscribe pattern) in real time, and processes the statistical data in real time through Flink to obtain the results aggregated on a minute-by-minute basis, and writes the results to the database. The daily data volume is large, so the statistical data in the database can be partitioned into tables by day;The detection and alerting module includes the Push_alert alerting service and the Wuji configuration management system. The Push_alert alerting service includes a rule engine. It periodically pulls task data from the task module's database and statistical data from the data statistics module's database. In other words, the Push_alert alerting service periodically pulls standardized data from different business scenarios and customizes different detection and alerting rules. These various detection and alerting rules are stored in the Wuji configuration management system. The Push_alert alerting service processes multiple alerting strategies under a given detection and alerting rule (e.g., if business data at a certain business level meets a first alert condition, corresponding alert content is generated) uniformly through the rule engine and forwards the processed result (a set of alert content) to the Push_alert alerting service for matching processing (e.g., alert frequency). The Push_alert alarm service, after meeting preset conditions (e.g., only 10 alarm messages will be sent today, and this is the 9th alarm message, so one more can be sent; or sending the alarm message to the advertising platform via SMS), decides whether to issue an alarm. Alarm content can be customized in different formats and targeted at different alarm groups. Specifically, alarm frequency control: different alarm frequency control strategies are set according to different types of alarm content (alarm messages) to avoid repeatedly disturbing users; alarm deduplication: deduplication strategies are set for the same type of alarm content (alarm messages) to avoid the same alarm content triggering alarms repeatedly within a certain period; alarm group selection: different alarm groups are set for different types of alarm content (alarm messages), and alarm content is notified to the corresponding message groups (user groups) of the appropriate alarm groups to avoid mutual interference.

[0070] For example, such as Figure 7As shown, TPNS feeds back statistical data via Kafka. The push platform consumes the statistical data from Kafka (a distributed message queue based on a publish / subscribe pattern) in real time, processes (aggregates) the statistical data in real time using Flink, and obtains the aggregated results on a minute-by-minute basis. The results (aggregated results) are then written to the database (MySQL). The Push_alert alert service is triggered periodically, pulling task data from the task module's database (task management) and statistical data from the data statistics module's database. Pulling task data from the task module's database (task management) includes: the Push_alert alert service performing conditional queries on the database (task management) for task data, and the database (task management) returning task data (task information) that meets the conditions. Pulling statistical data from the data statistics module's database includes: the Push_alert alert service requesting certain type of rule data (statistical data) from the Push statistics service, and the Push statistics service performing conditional queries on the database (MySQL) for the data source. The system returns statistical data to the Push statistics service, which aggregates and processes the data to obtain result data, which is then returned to the Push_alert alerting service. The Push_alert alerting service requests a specific detection and alerting rule (a certain type of Wuji configuration) from Wuji Configuration Management System. Wuji Configuration returns matching Wuji configurations to the Push_alert alerting service. Based on business data (e.g., task data and statistical data) and detection and alerting rules, the Push_alert alerting service processes the data through a rule engine to obtain the processed result (a set of alert content). Based on the alerting rules (alert content transmission rules), the Push_alert alerting service obtains the target alert content that meets the rules. The Push_alert alerting service can also perform logical judgments (e.g., collecting interactive messages after the alert content is sent, requiring corresponding business logic to be implemented in advance). The Push_alert alerting service triggers different types of alerts (target alert content) and sends these different types of alerts to different user groups.

[0071] It should be noted that different alarm frequency control strategies are set based on different types of alarm content; for alarm content of the same type, a deduplication strategy is set to avoid the same alarm content triggering alarms repeatedly within a certain period of time; in this way, users are not repeatedly disturbed, thereby improving the alarm efficiency of business data.

[0072] In one embodiment, before obtaining the target service's dataset for detection, the method further includes: Retrieve the raw data and store it in the message queue; The raw data in the message queue is divided into multiple arrays based on a preset time interval by a preset distributed streaming data processing engine, and the multiple arrays are stored in a preset database. Obtain the dataset of the target business to be detected, including: Based on a timed triggering data detection request for a target service, an array for the target service is obtained from a preset database. The dataset of services to be detected for the target service includes the array for the target service.

[0073] Specifically, for example, the raw data is obtained and stored in the message queue Kafka; the raw data in the message queue Kafka is divided into multiple arrays based on a preset time interval using the preset distributed streaming data processing engine Flink, and the multiple arrays are stored in the MDB database; among them, the multiple arrays are the result of minute-level aggregation, for example, each of the multiple arrays stores the total number of clicks in 1 minute.

[0074] In one embodiment, for example Figure 6 In MySQL, data storage can be designed to be stored offline. Offline storage can be implemented based on different business dimensions and metrics, such as daily and weekly comparisons of statistical indicators. These metrics could include, for example, click counts and reach counts. Figure 6 In MySQL, data storage can be stored online. Online storage can include real-time statistics of process data for various business scenarios and dimensions, aggregating the total data volume by minute, and storing statistical data by operation type, task type, etc. Operation types include gateway write, content loading, message triggering, middle platform distribution, channel distribution, push arrival, click count, task count, etc., and task types include functional tasks, urgent tasks, and ordinary tasks.

[0075] In one embodiment, transmitting the target alarm content to the target object includes: Display the alarm screen to the target object based on the target alarm content. The alarm screen includes the target alarm content and at least one of the following: Business data at the business level for the target alarm content, business data at the dimension for the target alarm content, the first alarm condition at the business level for the target alarm content, and the second alarm condition at the dimension for the target alarm content.

[0076] Specifically, for example, such as Figure 8As shown, the alarm screen includes the target alarm content, business data at the business level related to the target alarm content, and the first alarm condition at the business level related to the target alarm content. The target alarm content includes a task with over 30 million video pushes, with a successful delivery rate of 12% from the middle platform. The business data at the business level related to the target alarm content includes the number of successfully delivered tasks from the middle platform (4,021,707) and the total number of tasks delivered from the middle platform (32,468,855). The first alarm condition at the business level related to the target alarm content is that the number of users who successfully delivered a single task from the middle platform accounts for less than 30% of all users who received tasks from the middle platform.

[0077] For example, such as Figure 9 As shown, the alarm screen includes the target alarm content, business data at the business level for the target alarm content, and the first alarm condition at the business level for the target alarm content. The target alarm content includes the total number of arrivals on April 16, 2024, which was 197,025,269, with a volatility of -6% compared to April 15, 2024. The business data at the business level for the target alarm content includes the total number of arrivals on April 16, 2024, which was 197,025,269, with a volatility of -3% compared to April 9, 2024. The first alarm condition at the business level for the alarm content includes a threshold of less than 200 million, meaning the total number of arrivals is less than 200 million.

[0078] For example, such as Figure 10 As shown, the alarm screen includes the target alarm content, business data for the target alarm content, and a second alarm condition for the target alarm content. The target alarm content includes the percentage of video emergency task Android channel delivery volume compared to yesterday's Android channel delivery volume: 4%, broken down by vendor: Vendor 1 12%, Vendor 2 0%, Vendor 3 0%, Vendor 4 0%, Vendor 5 26%, and Vendor 6 20%. The business data for the target alarm content includes yesterday's delivery volume: 927,433,879, and today's delivery volume: 40,902,325. The second alarm condition for the target alarm content includes today's delivery volume being less than 80% of yesterday's delivery volume.

[0079] Applying the embodiments of this disclosure has at least the following beneficial effects: Based on a hierarchical alarm mechanism (the alarm detection rules include the first business data identifier of each business level in multiple business levels, the hierarchical relationship between each business level, and the first alarm condition corresponding to each business level; the alarm detection rules also include the second business data identifier of each dimension in multiple dimensions of each business level, the hierarchical relationship between multiple dimensions, and the second alarm condition corresponding to each dimension in multiple dimensions of that business level), the root cause of the problem for the target business (e.g., advertising business) is determined, i.e., the alarm content set for the target business; based on alarm content transmission rules (e.g., alarm frequency control, alarm deduplication, etc.), the target alarm content in the alarm content set is obtained; the target alarm content is sent and displayed to the target object (e.g., advertising platform); this achieves layer-by-layer analysis of business data and targeted alarms by business level, improving the alarm efficiency for the target business, i.e., improving the alarm efficiency of business data. By introducing a rules engine and Wuji configuration, massive amounts of business data are processed in a unified manner. Alarms for any business data (business metrics) can be easily generated through detection and alarm rules, simplifying the development process, reducing development costs, and improving development efficiency. At the same time, the aggregation of business data by minute also greatly reduces the storage costs of the database.

[0080] To better understand the methods provided in the embodiments of this disclosure, the solutions of the embodiments of this disclosure will be further explained below with reference to specific application scenarios.

[0081] In one embodiment, for example Figure 6 The hardware for the data detection system includes cloud servers and cloud storage databases. The cloud servers consist of a Linux system, a 2-core CPU (Central Processing Unit), 2GB of memory, and two container machines. The cloud storage database is TDSQL cloud-native, with a 16-core CPU, 64GB of memory, and a QPS of 8000-25000 times / s, configured as one primary and two backups. The Redis database is Redis 5.0, with 6GB*3 shards (cluster version), also configured as one primary and one backup.

[0082] In one embodiment, the push platform includes Figure 6 The data detection system in China detects and issues alerts on business data. For example, ... Figure 11 The real-time data shown. For example, as... Figure 12 The image shows a single-dimensional scene alarm based on real-time data. For example, such as... Figure 13 , Figure 14 , Figure 15 and Figure 16 The cumulative dimension data alerts shown are for real-time data. For example, such as... Figure 17The offline data shown includes message types such as user interaction messages, emergency messages, operational messages, and personalized messages. Channel types can be categorized based on factors such as the channel and data source. For example... Figure 18 , Figure 19 and Figure 20 Alarms based on offline data.

[0083] Message types include functional messages, emergency messages, operational messages, personalized messages, etc.; channel types are categorized by channel and data source.

[0084] In one embodiment, the data detection method provided in this disclosure is applied to a push platform to detect and alert on business data. It can detect problems in the system chain in real time, promptly report the chain funnel situation, promptly broadcast the reach of a single message, and notify operations to make timely changes to the strategy. It can also be applied to a push data management system. It can also be applied to other scenarios involving data detection and alerting, such as advertising systems, e-commerce systems, and payment systems.

[0085] In a specific application scenario, such as a business data detection scenario, see [link to relevant documentation]. Figure 21 This illustrates the processing flow of a data detection method, such as... Figure 21 As shown, the data detection method provided in this embodiment includes the following steps: S501: The server obtains the dataset of the target business to be detected and retrieves the corresponding detection alarm rules for the target business from the Wuji configuration management system.

[0086] Specifically, the business data set to be detected includes business data from multiple business layers of the target business. The detection alarm rules include the first business data identifier of each business layer, the hierarchical relationship between the business layers, the first alarm condition corresponding to each business layer, the second business data identifier of each dimension in the multiple dimensions of each business layer, the hierarchical relationship between the multiple dimensions, and the second alarm condition corresponding to each dimension in the multiple dimensions of the business layer.

[0087] S502: The server uses a rule engine to generate alarm content for each target business level and alarm content for each target dimension of the target business, based on the target business dataset to be detected and the corresponding detection alarm rules. Based on the alarm content for each target business level and the alarm content for each target dimension of the target business, the server constructs an alarm content set.

[0088] Specifically, based on the first business data identifier of each business level, business data of each business level of the target business is determined from the business dataset to be detected; based on the hierarchical relationship between each business level, the target business level whose business data satisfies the first alarm condition of the corresponding business level is determined sequentially from each business level, and alarm content is generated for each target business level. Wherein, if the target business level is a single business level, it is the highest-level business level; if the target business level includes at least two business levels, the at least two business levels are at least two consecutive business levels, including the highest-level business level; for each business level, based on the second business data identifier of each dimension of that business level, business data of each dimension of that business level is determined from the business data of that business level; for each target business level, based on the hierarchical relationship between multiple dimensions of that target business level, the target dimensions whose business data satisfies the corresponding second alarm condition are determined sequentially from the multiple dimensions of that target business level, and alarm content is generated for each target dimension; wherein, the alarm content set also includes alarm content for each target dimension.

[0089] S503, the server determines the target alarm content in the alarm content set that meets the alarm content transmission rules based on the alarm content transmission rules.

[0090] Specifically, for example, alarm frequency control: based on the alarm frequency of each type of alarm content in different types in the alarm content transmission rules, determine the alarm content that matches each type of alarm content from the alarm content set, determine the matched alarm content as the target alarm content, and transmit the target alarm content to the target object at the alarm frequency.

[0091] For example, alarm deduplication: Based on the deduplication strategy for alarm content of the same type in the alarm content transmission rules, the alarm content of the same type in the alarm content set is deduplicated to obtain the deduplicated alarm content. The deduplicated alarm content is determined as the target alarm content and transmitted to the target object within a preset time period.

[0092] S504, the server will display the alarm screen for the target alarm content to the target object.

[0093] Specifically, for example, the alarm screen includes target alarm content, business data at the business level for the target alarm content, business data at the dimension for the target alarm content, first alarm conditions at the business level for the target alarm content, and second alarm conditions at the dimension for the target alarm content.

[0094] Applying the embodiments of this disclosure has at least the following beneficial effects: Based on a hierarchical alarm mechanism (the alarm detection rules include the first business data identifier of each business level in multiple business levels, the hierarchical relationship between each business level, and the first alarm condition corresponding to each business level; the alarm detection rules also include the second business data identifier of each dimension in multiple dimensions of each business level, the hierarchical relationship between multiple dimensions, and the second alarm condition corresponding to each dimension in multiple dimensions of that business level), the root cause of the problem for the target business (e.g., advertising business) is determined, i.e., the alarm content set for the target business; based on alarm content transmission rules (e.g., alarm frequency control, alarm deduplication, etc.), the target alarm content in the alarm content set is obtained; the target alarm content is sent and displayed to the target object (e.g., advertising platform); this achieves layer-by-layer analysis of business data and targeted alarms by business level, improving the alarm efficiency for the target business, i.e., improving the alarm efficiency of business data.

[0095] This disclosure also provides a data detection device, the structural schematic diagram of which is shown below. Figure 22 As shown, the data detection device 60 includes a first processing module 601, a second processing module 602, a third processing module 603, a fourth processing module 604, and a fifth processing module 605.

[0096] The first processing module 601 is used to obtain the target business's business dataset to be detected, which includes business data from multiple business layers of the target business. The second processing module 602 is used to obtain the preset detection alarm rules corresponding to the target service. The detection alarm rules include the first service data identifier of each service level in multiple service levels, the hierarchical relationship between each service level, and the first alarm condition corresponding to each service level in each service level. The third processing module 603 is used to determine the business data of each business level of the target business from the business data set to be detected based on the first business data identifier of each business level. The fourth processing module 604 is used to determine the target business level from each business level in turn based on the hierarchical relationship between each business level, and generate alarm content for each target business level. If the target business level is a business level, the target business level is the highest level business level. If the target business level includes at least two business levels, the at least two business levels are at least two consecutive business levels including the highest level business level. The fifth processing module 605 is used to determine the target alarm content in the alarm content set that meets the alarm content transmission rules based on the preset alarm content transmission rules, and transmit the target alarm content to the target object. The alarm content set includes alarm content for each target business level.

[0097] In one embodiment, the business data of each business layer in the multiple business layers includes business data of multiple dimensions, and the detection alarm rules also include the second business data identifier of each dimension in the multiple dimensions of each business layer, the hierarchical relationship between the multiple dimensions, and the second alarm condition corresponding to each dimension in the multiple dimensions of the business layer. The fourth processing module 604 is further used for: For each business level, based on the second business data identifier of each dimension of that business level, the business data of each dimension of that business level is determined from the business data of that business level; For each target business level, based on the hierarchical relationship between multiple dimensions of the target business level, the target dimensions from the multiple dimensions of the target business level that satisfy the corresponding second alarm conditions are determined in turn, and alarm content for each target dimension is generated. The alarm content collection also includes alarm content for each target dimension.

[0098] In one embodiment, the fourth processing module 604 is specifically used for: Based on the hierarchical relationship between the various business levels, the following judgment operations are performed sequentially in descending order of the hierarchical level of each business level until the business data of the current business level does not meet the first alarm condition corresponding to the current business level, or the business data of the current business level meets the first alarm condition corresponding to the current business level and the current business level is the last level. The judgment operation includes the following steps: Based on the first alarm condition corresponding to the current business level, determine whether the current business level is the target business level; If the current business level is determined to be the target business level, then based on the first alarm condition, an alarm content corresponding to the target business level is generated, and the next business level of the current business level is used as the current business level for the next judgment operation.

[0099] In one embodiment, the fourth processing module 604 is specifically used for: For each target business level, based on the hierarchical relationship between multiple dimensions of that business level, the following judgment operations are performed sequentially in descending order of dimension level, until the business data of the current dimension does not meet the second alarm judgment condition corresponding to the current dimension, or the business data of the current dimension meets the second alarm condition corresponding to the current dimension and the current dimension is the last dimension level: The judgment operation includes the following steps: Based on the second alarm condition corresponding to the current dimension, determine whether the current dimension is the target dimension; If the current dimension is determined to be the target dimension, then based on the second alarm condition, an alarm content corresponding to the target dimension is generated, and the next dimension of the current dimension is used as the current dimension for the next judgment operation.

[0100] In one embodiment, the fifth processing module 605 is specifically configured to perform at least one of the following: Based on the alarm frequency of each type of alarm content in the preset alarm content transmission rules, the alarm content that matches each type of alarm content is determined from the alarm content set, the matched alarm content is determined as the target alarm content, and the target alarm content is transmitted to the target object according to the alarm frequency. Based on the deduplication strategy for alarm content of the same type in the alarm content transmission rules, the alarm content of the same type in the alarm content set is deduplicated to obtain the deduplicated alarm content. The deduplicated alarm content is determined as the target alarm content and transmitted to the target object within a preset time period.

[0101] In one embodiment, the first processing module 601 is further configured to: Retrieve the raw data and store it in the message queue; The raw data in the message queue is divided into multiple arrays based on a preset time interval by a preset distributed streaming data processing engine, and the multiple arrays are stored in a preset database. Obtain the dataset of the target business to be detected, including: Based on a timed triggering data detection request for a target service, an array for the target service is obtained from a preset database. The dataset of services to be detected for the target service includes the array for the target service.

[0102] In one embodiment, the fifth processing module 605 is specifically used for: Display the alarm screen to the target object based on the target alarm content. The alarm screen includes the target alarm content and at least one of the following: Business data at the business level for the target alarm content, business data at the dimension for the target alarm content, the first alarm condition at the business level for the target alarm content, and the second alarm condition at the dimension for the target alarm content.

[0103] Applying the embodiments of this disclosure has at least the following beneficial effects: The process involves: acquiring a dataset of business data to be detected for the target business, which includes business data from multiple business layers of the target business; acquiring preset detection and alarm rules corresponding to the target business, wherein the detection and alarm rules include the first business data identifier of each business layer, the hierarchical relationship between the business layers, and the first alarm condition corresponding to each business layer; determining the business data of each business layer of the target business from the dataset of business data to be detected based on the first business data identifier of each business layer; determining the target business layer whose business data satisfies the first alarm condition of the corresponding business layer based on the hierarchical relationship between the business layers, and generating alarm content for each target business layer, wherein if the target business layer is a single business layer, it is the highest-level business layer; if the target business layer includes at least two business layers, the at least two business layers include the highest-level business layer. The system operates on a continuous business hierarchy. Based on preset alarm content transmission rules, it identifies target alarm content that meets these rules within the alarm content set and transmits it to the target object. The alarm content set includes alarm content for each target business level. Thus, based on a progressive alarm mechanism (the alarm detection rules include the first business data identifier of each business level, the hierarchical relationship between business levels, and the first alarm condition corresponding to each business level), it determines the root cause of the problem for the target business (e.g., advertising business), i.e., the alarm content set for the target business. Based on alarm content transmission rules (e.g., alarm frequency control, alarm deduplication), it obtains the target alarm content within the alarm content set. The target alarm content is then sent and displayed to the target object (e.g., the advertising platform). This achieves layer-by-layer analysis of business data and targeted alarms by business level, improving the alarm efficiency for the target business, i.e., improving the alarm efficiency for business data.

[0104] This disclosure also provides an electronic device, the structural schematic diagram of which is shown below. Figure 23 As shown, Figure 23The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this disclosure.

[0105] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0106] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 23 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0107] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0108] The memory 4003 is used to store computer programs that execute embodiments of the present disclosure, and is controlled by the processor 4001 to execute them. The processor 4001 is used to execute the computer programs stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0109] Electronic devices include, but are not limited to, servers.

[0110] Applying the embodiments of this disclosure has at least the following beneficial effects: The process involves: acquiring a dataset of business data to be detected for the target business, which includes business data from multiple business layers of the target business; acquiring preset detection and alarm rules corresponding to the target business, wherein the detection and alarm rules include the first business data identifier of each business layer, the hierarchical relationship between the business layers, and the first alarm condition corresponding to each business layer; determining the business data of each business layer of the target business from the dataset of business data to be detected based on the first business data identifier of each business layer; determining the target business layer whose business data satisfies the first alarm condition of the corresponding business layer based on the hierarchical relationship between the business layers, and generating alarm content for each target business layer, wherein if the target business layer is a single business layer, it is the highest-level business layer; if the target business layer includes at least two business layers, the at least two business layers include the highest-level business layer. The system operates on a continuous business hierarchy. Based on preset alarm content transmission rules, it identifies target alarm content that meets these rules within the alarm content set and transmits it to the target object. The alarm content set includes alarm content for each target business level. Thus, based on a progressive alarm mechanism (the alarm detection rules include the first business data identifier of each business level, the hierarchical relationship between business levels, and the first alarm condition corresponding to each business level), it determines the root cause of the problem for the target business (e.g., advertising business), i.e., the alarm content set for the target business. Based on alarm content transmission rules (e.g., alarm frequency control, alarm deduplication), it obtains the target alarm content within the alarm content set. The target alarm content is then sent and displayed to the target object (e.g., the advertising platform). This achieves layer-by-layer analysis of business data and targeted alarms by business level, improving the alarm efficiency for the target business, i.e., improving the alarm efficiency for business data.

[0111] This disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0112] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0113] It should be understood that although arrows indicate various operation steps in the flowcharts of the embodiments of this disclosure, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of the embodiments of this disclosure, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured as required, and the embodiments of this disclosure do not limit this.

[0114] The above description is only an optional implementation method for some implementation scenarios of this disclosure. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this disclosure without departing from the technical concept of this disclosure also fall within the protection scope of the embodiments of this disclosure.

Claims

1. A data detection method, characterized in that, include: Obtain the target service's data set to be detected, wherein the data set includes service data from multiple service levels of the target service; Obtain the preset detection and alarm rules corresponding to the target service, wherein the detection and alarm rules include the first service data identifier of each service level in the multiple service levels, the hierarchical relationship between the service levels, and the first alarm condition corresponding to each service level in the multiple service levels. Based on the first business data identifier of each business level, the business data of each business level of the target business is determined from the business dataset to be detected; Based on the hierarchical relationship between the various business layers, the target business layer that satisfies the first alarm condition of the corresponding business layer is determined sequentially from each business layer, and alarm content is generated for each target business layer. Wherein, if the target business layer is a business layer, the target business layer is the highest level business layer; if the target business layer includes at least two business layers, the at least two business layers are at least two consecutive business layers including the highest level business layer. Based on preset alarm content transmission rules, target alarm content that meets the alarm content transmission rules in the alarm content set is determined, and the target alarm content is transmitted to the target object. The alarm content set includes alarm content for each target service level.

2. The method according to claim 1, characterized in that, The business data of each of the multiple business layers includes business data of multiple dimensions. The detection and alarm rules also include a second business data identifier for each dimension of each business layer, the hierarchical relationship between the multiple dimensions, and a second alarm condition corresponding to each dimension of the business layer. The method further includes: For each business level, based on the second business data identifier of each dimension of that business level, the business data of each dimension of that business level is determined from the business data of that business level; For each target business level, based on the hierarchical relationship between multiple dimensions of the target business level, the target dimensions from the multiple dimensions of the target business level that satisfy the corresponding second alarm conditions are determined in turn, and alarm content for each target dimension is generated. The alarm content set also includes alarm content for each target dimension.

3. The method according to claim 1 or 2, characterized in that, Based on the hierarchical relationship between the various business layers, the target business layer that satisfies the first alarm condition of the corresponding business layer is determined sequentially from each business layer, and alarm content for each target business layer is generated, including: Based on the hierarchical relationship between the various business levels, the following judgment operations are performed sequentially in descending order of the hierarchical level of each business level until the business data of the current business level does not meet the first alarm condition corresponding to the current business level, or the business data of the current business level meets the first alarm condition corresponding to the current business level and the current business level is the last level. The determination operation includes the following steps: Based on the first alarm condition corresponding to the current business level, determine whether the current business level is the target business level; If the current business level is determined to be the target business level, then based on the first alarm condition, an alarm content corresponding to the target business level is generated, and the next business level of the current business level is used as the current business level for the next judgment operation.

4. The method according to claim 2 or 3, characterized in that, For each target business level, based on the hierarchical relationship between multiple dimensions of that target business level, the target dimensions from the multiple dimensions of that target business level that satisfy the corresponding second alarm conditions are determined sequentially, and alarm content for each target dimension is generated, including: For each target business level, based on the hierarchical relationship between multiple dimensions of that business level, the following judgment operations are performed sequentially in descending order of dimension level, until the business data of the current dimension does not meet the second alarm judgment condition corresponding to the current dimension, or the business data of the current dimension meets the second alarm condition corresponding to the current dimension and the current dimension is the dimension of the last dimension level: The determination operation includes the following steps: Based on the second alarm condition corresponding to the current dimension, determine whether the current dimension is the target dimension; If the current dimension is determined to be the target dimension, then based on the second alarm condition, an alarm content corresponding to the target dimension is generated, and the next dimension of the current dimension is used as the current dimension for the next judgment operation.

5. The method according to claim 1, characterized in that, The process of determining target alarm content that satisfies the preset alarm content transmission rules within the alarm content set, and transmitting the target alarm content to the target object, includes at least one of the following: Based on the alarm frequency of each type of alarm content in the preset alarm content transmission rules, the alarm content that matches each type of alarm content is determined from the alarm content set, the matched alarm content is determined as the target alarm content, and the target alarm content is transmitted to the target object at the alarm frequency. Based on the deduplication strategy for alarm content of the same type in the alarm content transmission rules, the alarm content of the same type in the alarm content set is deduplicated to obtain the deduplicated alarm content. The deduplicated alarm content is determined as the target alarm content and transmitted to the target object within a preset time period.

6. The method according to claim 1, characterized in that, Before obtaining the target service's dataset for detection, the method further includes: Obtain the raw data and store it in a message queue; The original data in the message queue is divided into multiple arrays based on a preset time interval by a preset distributed streaming data processing engine, and the multiple arrays are stored in a preset database. The process of obtaining the target service's dataset for detection includes: Based on a timed triggering data detection request for the target service, an array for the target service is obtained from the preset database. The target service's dataset to be detected includes the array for the target service.

7. The method according to claim 2, characterized in that, The step of transmitting the target alarm content to the target object includes: An alarm screen for the target alarm content is displayed to the target object. The alarm screen includes the target alarm content and at least one of the following: The business data at the business level for the target alarm content, the business data at the dimension for the target alarm content, the first alarm condition at the business level for the target alarm content, and the second alarm condition at the dimension for the target alarm content.

8. A data detection device, characterized in that, include: The first processing module is used to acquire the target business's business dataset to be detected, wherein the business dataset to be detected includes business data of multiple business layers of the target business; The second processing module is used to obtain the preset detection alarm rules corresponding to the target service, wherein the detection alarm rules include the first service data identifier of each service level in the plurality of service levels, the hierarchical relationship between the service levels, and the first alarm condition corresponding to each service level in the plurality of service levels. The third processing module is used to determine the business data of each business level of the target business from the business data set to be detected based on the first business data identifier of each business level. The fourth processing module is used to determine the target business layer from each business layer in sequence, based on the hierarchical relationship between the various business layers, the target business layer that the business data meets the first alarm condition of the corresponding business layer, and generate alarm content for each target business layer. If the target business layer is a single business layer, the target business layer is the highest-level business layer. If the target business layer includes at least two business layers, the at least two business layers are at least two consecutive business layers including the highest-level business layer. The fifth processing module is used to determine the target alarm content in the alarm content set that meets the alarm content transmission rules based on the preset alarm content transmission rules, and transmit the target alarm content to the target object, wherein the alarm content set includes alarm content for each target service level.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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