Data processing method, monitoring method, system and electronic device

Abstract

An embodiment of the present application provides a data processing method, a monitoring method, a system, and an electronic device. Among them, the method includes: determining a first relationship between data corresponding to multiple fields; obtaining monitoring data; and determining whether the data relationships corresponding to multiple fields in the monitoring data conform to the first relationship, so as to judge whether the monitoring data is correct based on the determination result. The technical solution provided by the embodiment of the present application can use an automated method to discover the relationship between data corresponding to multiple fields, and can effectively improve the efficiency and accuracy of data monitoring.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a data processing method, a monitoring method, a system, and an electronic device. Background Art

[0002] With the rapid development of e-commerce, online transactions through various e-commerce platforms have now become the daily choice of most users. However, due to the wide variety of amounts involved in each commodity, such as pricing, discounted price, postage, full reduction, and various taxes and fees, it is inevitable that the calculation of commodity prices will be incorrect due to human factors or system anomalies during the operation of e-commerce platforms. Once users place orders to purchase commodities at the wrong price, it may cause unnecessary economic losses to merchants or users.

[0003] In the prior art, the reconciliation rules for monitoring commodity prices are generally achieved by manual sorting, which is prone to missing verification points, resulting in low monitoring efficiency and poor accuracy. In addition, most online asset loss failures are caused by missing verification points. Summary of the Invention

[0004] This application provides a data processing method, a system, and an electronic device that solve the above problems or at least partially solve the above problems.

[0005] In one embodiment of this application, a data processing method is provided. The method includes:

[0006] Determine the first relationship between the data corresponding to multiple fields;

[0007] Obtain monitoring data;

[0008] Determine whether the relationship between the data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to judge whether the monitoring data is correct based on the determination result.

[0009] In another embodiment of this application, a data processing method is provided. The method includes:

[0010] Determine the order amount calculation rule according to at least one historical order;

[0011] After receiving the order placement request triggered by the user, obtain the data corresponding to multiple fields included in the data to be ordered;

[0012] Use the order amount calculation rule to calculate the data corresponding to multiple fields included in the data to be ordered, and obtain the order amount corresponding to the data to be ordered;

[0013] Send the order amount to the client.

[0014] In another embodiment of the present application, a data processing method is provided. The method includes:

[0015] Determine an order amount calculation rule according to at least one historical order;

[0016] Obtain a monitored order;

[0017] Based on the order amount calculation rule, determine whether the order amount of the monitored order is correct.

[0018] In another embodiment of the present application, a data processing method is provided. The method includes:

[0019] Determine a first relationship between data corresponding to multiple fields;

[0020] Obtain data corresponding to multiple fields associated with a monitored object;

[0021] According to the data corresponding to the multiple fields associated with the monitored object and the first relationship, determine the texture mapping data corresponding to the monitored object;

[0022] Send the texture mapping data to a client for display on the client.

[0023] In another embodiment of the present application, a data processing method is provided. The method includes:

[0024] Determine a first relationship between data corresponding to multiple fields;

[0025] Obtain the data corresponding to the multiple fields associated with the monitored object and the first relationship, and determine whether the texture mapping data is correct.

[0026] In an embodiment of the present application, a data monitoring method is provided. The method includes:

[0027] In response to a user's creation operation, create a monitoring task, where the monitoring task contains data requirements for the data to be monitored;

[0028] In response to a monitoring task execution instruction triggered by the user, determine the relationship between data corresponding to multiple fields;

[0029] Obtain monitoring data that meets the data requirements;

[0030] Using the relationship, determine whether the relationship between data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0031] In an embodiment of the present application, a data processing system is provided. The system includes:

[0032] A client, configured to generate monitoring data and send the monitoring data to a server;

[0033] A server, configured to determine a first relationship among data corresponding to multiple fields; obtain the monitoring data; and determine whether the relationship among data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0034] In an embodiment of the present application, a data processing system is provided. The system includes:

[0035] A client, configured to send order data to be placed to a server in response to a placed order request triggered by a user;

[0036] A server, configured to determine an order amount calculation rule according to at least one historical order; obtain data corresponding to multiple fields included in the order data to be placed; calculate the data corresponding to the multiple fields included in the order data to be placed by using the order amount calculation rule to obtain the order amount corresponding to the order data to be placed; and send the order amount to the client;

[0037] The client is further configured to display the order amount.

[0038] In an embodiment of the present application, a data processing system is provided. The system includes:

[0039] A client, configured to generate a monitoring order and send the monitoring order to a server;

[0040] A server, configured to determine an order amount calculation rule according to at least one historical order; obtain the monitoring order; and determine whether the order amount of the monitoring order is correct based on the order amount calculation rule.

[0041] In an embodiment of the present application, a data processing system is provided. The system includes:

[0042] A server, configured to determine a first relationship among data corresponding to multiple fields; obtain data corresponding to multiple fields associated with a monitoring object; and determine texture data corresponding to the monitoring object according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship;

[0043] A client, configured to display the texture data corresponding to the monitoring object.

[0044] In an embodiment of the present application, a data processing system is provided. The system includes:

[0045] A client, configured to generate texture data corresponding to a monitoring object;

[0046] A server, configured to determine a first relationship among data corresponding to multiple fields; obtain data corresponding to the multiple fields associated with the monitoring object and the mapping data; and determine whether the mapping data is correct according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship.

[0047] In an embodiment of the present application, a data monitoring system is provided. The system includes:

[0048] A client, configured to create a monitoring task in response to a user's creation operation, where the monitoring task contains data requirements for the monitored data; send the monitoring task to the server; and send a start message for the monitoring task to the server in response to a monitoring task execution instruction triggered by the user.

[0049] A server, configured to receive and deploy the monitoring task, execute the monitoring task after receiving the start message, determine the relationship among data corresponding to multiple fields; obtain monitoring data that meets the data requirements; and use the relationship to determine whether the relationship among data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0050] In an embodiment of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, where

[0051] The memory is configured to store a program;

[0052] The processor is coupled to the memory and configured to execute the program stored in the memory for:

[0053] Determine a first relationship among data corresponding to multiple fields;

[0054] Obtain monitoring data;

[0055] Determine whether the relationship among data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0056] In another embodiment of the present application, an electronic device is provided. The electronic device includes: a memory, a processor, and a communication component, where

[0057] The memory is configured to store a program;

[0058] The processor is coupled to the memory and configured to execute the program stored in the memory for:

[0059] Determine an order amount calculation rule according to at least one historical order;

[0060] After receiving the order placement request triggered by the user, obtain the data corresponding to multiple fields included in the data to be ordered.

[0061] Using the order amount calculation rule, calculate the data corresponding to multiple fields included in the data to be ordered to obtain the order amount corresponding to the data to be ordered.

[0062] Send the order amount to the client through the communication component.

[0063] In another embodiment of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, wherein,

[0064] The memory is used to store programs.

[0065] The processor is coupled to the memory and is used to execute the program stored in the memory for:

[0066] Determine the order amount calculation rule according to at least one historical order.

[0067] Obtain the monitored order.

[0068] Based on the order amount calculation rule, determine whether the order amount of the monitored order is correct.

[0069] In another embodiment of the present application, an electronic device is provided. The electronic device includes: a memory, a processor, and a communication component, wherein,

[0070] The memory is used to store programs.

[0071] The processor is coupled to the memory and is used to execute the program stored in the memory for:

[0072] Determine the first relationship between the data corresponding to multiple fields.

[0073] Obtain the data corresponding to multiple fields associated with the monitored object.

[0074] According to the data corresponding to the multiple fields associated with the monitored object and the first relationship, determine the texture data corresponding to the monitored object.

[0075] Send the texture data to the client through the communication component for display on the client.

[0076] In another embodiment of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, wherein,

[0077] The memory is used to store programs.

[0078] The processor, coupled to the memory, is configured to execute the program stored in the memory for:

[0079] Determine a first relationship between data corresponding to multiple fields;

[0080] Obtain data corresponding to multiple fields associated with the monitoring object and the first relationship, and determine whether the sticker data is correct.

[0081] In another embodiment of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, wherein,

[0082] The memory is configured to store a program;

[0083] The processor, coupled to the memory, is configured to execute the program stored in the memory for:

[0084] In response to a user's creation operation, create a monitoring task, wherein the monitoring task contains data requirements for the monitored data;

[0085] In response to a monitoring task execution instruction triggered by the user, determine the relationship between data corresponding to multiple fields;

[0086] Obtain monitoring data that meets the data requirements;

[0087] Utilize the relationship to determine whether the relationship between data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0088] The technical solution provided in an embodiment of the present application first determines a first relationship between data corresponding to multiple fields, and then determines whether the relationship between data corresponding to multiple fields in the obtained monitoring data conforms to the first relationship, so as to determine whether the monitoring data is correct based on the determination result. Since the first relationship between data corresponding to multiple fields is mined by an automated method without human intervention and has a high accuracy, the efficiency and accuracy of data monitoring can be effectively improved. In addition, this solution can be applied to any scenario involving amount calculation, such as order amount calculation, order amount correctness verification, automated sticker landed price calculation, automated sticker correctness verification, etc., with a wide application range and strong practicability.

[0089] The technical solution provided by another embodiment of the present application provides a function for users to create monitoring tasks. Users can create monitoring tasks by themselves to specify data requirements for the monitored data, etc. In this way, after the user triggers the monitoring task execution instruction, the system can first determine the relationships between the data corresponding to multiple fields, and use these relationships to determine whether the relationships between the data corresponding to multiple fields in the obtained monitoring data that meet the data requirements conform to these relationships, so as to judge whether the monitoring data is correct based on the determination result. In the technical solution provided by this embodiment, the relationships between the data corresponding to multiple fields are automatically mined by using a relationship analysis model, without human intervention, with high accuracy, and thus can effectively improve the efficiency and accuracy of data monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0091] Figure 1 It is a schematic flowchart of a data processing method provided by an embodiment of the present application;

[0092] Figure 2 It is a schematic diagram of a commodity order provided by an embodiment of the present application;

[0093] Figure 3 It is a schematic diagram of the principle of an application platform mentioned in an embodiment of the present application;

[0094] Figure 4a It is a schematic diagram of the principle of the model registration process provided by an embodiment of the present application;

[0095] Figure 4b It is a schematic diagram of the principle of the model list mentioned in an embodiment of the present application;

[0096] Figure 5a It is a schematic diagram of the principle of creating an application scenario provided by an embodiment of the present application;

[0097] Figure 5b It is a schematic diagram of the principle of the scenario list mentioned in an embodiment of the present application;

[0098] Figure 6a It is a schematic diagram of the principle of creating a task provided by an embodiment of the present application;

[0099] Figure 6b It is a schematic diagram of the principle of the task list mentioned in an embodiment of the present application;

[0100] Figure 7 Schematic diagram of task operation results provided by an embodiment of the present application;

[0101] Figure 8 Flow schematic diagram of a data processing method provided by another embodiment of the present application;

[0102] Figure 9 Flow schematic diagram of a data processing method provided by yet another embodiment of the present application;

[0103] Figure 10a Flow schematic diagram of a data processing method provided by yet another embodiment of the present application;

[0104] Figure 10b Schematic diagram of an automated sticker provided by an embodiment of the present application;

[0105] Figure 11 Flow schematic diagram of a data processing method provided by yet another embodiment of the present application;

[0106] Figure 12 Flow schematic diagram of a data monitoring method provided by yet another embodiment of the present application;

[0107] Figure 13 Schematic diagram of the structure of a data processing system provided by an embodiment of the present application;

[0108] Figure 14 Schematic diagram of the structure of a data processing device provided by an embodiment of the present application;

[0109] Figure 15 Schematic diagram of the structure of a data processing device provided by another embodiment of the present application;

[0110] Figure 16 Schematic diagram of the structure of a data processing device provided by yet another embodiment of the present application;

[0111] Figure 17 Schematic diagram of the structure of a data processing device provided by yet another embodiment of the present application;

[0112] Figure 18 Schematic diagram of the structure of a data processing device provided by yet another embodiment of the present application;

[0113] Figure 19 Schematic diagram of the structure of a data monitoring device provided by an embodiment of the present application;

[0114] Figure 20 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0115] In view of the problems existing in the prior art, the technical solutions provided by the embodiments of the present application can utilize big data analysis and mining technologies to intelligently mine the calculation rules for the relationships between data corresponding to multiple fields, enabling users to monitor relevant data based on these calculation rules to timely discover incorrect data, and thus effectively discover the problem of asset losses caused by calculation errors. Moreover, this solution can be applied to various scenarios involving amount calculations, such as order amount calculations, automated sticker calculations, etc., with a wide range of applications and strong practicality. To enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application.

[0116] In some processes described in the specification, claims, and the above-mentioned accompanying drawings of the present application, there are multiple operations that appear in a specific order. These operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish each different operation, and the serial numbers themselves do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types. In the present application, the term "or / and" only describes the association relationship between associated objects and indicates that there can be three relationships. For example, A or / and B means that A can exist alone, A and B can exist simultaneously, or B can exist alone. In the present application, the character " / " generally represents an "or" relationship between the associated objects before and after. In addition, the following embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0117] Before explaining the solutions provided by the embodiments of the present application, the terms involved in the present application will be briefly described first.

[0118] Automated sticker: Through various discounts applied for a product, a final price and some guiding copy can be calculated and a picture can be generated. After the promotion activity starts, it will replace the main picture on the product. The sticker data in the solution of the present application is the automated sticker.

[0119] Asset loss data platform: A platform used to collect all asset loss data.

[0120] Chaos God Platform: A data intelligent testing platform that can build a bridge for algorithm engineers and technology. Algorithms can register models on the platform to form an algorithm supermarket. For calculation tasks such as order amount calculation and automated sticker calculation, the required algorithms can be selected according to actual needs to obtain the desired results.

[0121] The execution subject of the data processing method provided in each of the following embodiments may be the corresponding data processing device. This device may be a hardware with an embedded program integrated in an electronic device, an application software installed in the electronic device, or a tool software embedded in the device operating system, etc. The embodiments of the present application do not make limitations in this regard. The electronic device may be a client or a server. Among them, the client may be any terminal device such as a mobile phone, a tablet computer, a smart wearable device, etc., and the server may be a common server, a cloud or a virtual server, etc. The embodiments of the present application do not make specific limitations in this regard.

[0122] Figure 1 The flowchart of the data processing method provided in an embodiment of the present application is shown. As Figure 1 shown, the method includes the following steps:

[0123] 101. Determine the first relationship between the data corresponding to the multiple fields;

[0124] 102. Obtain monitoring data;

[0125] 103. Determine whether the relationship between the data corresponding to the multiple fields in the monitoring data conforms to the first relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0126] In an implementable technical solution, in the above 101, the first relationship between the data corresponding to the multiple fields may be determined by analyzing historical data. Specifically, step 101 may include:

[0127] 1011. Obtain at least one piece of first historical data, where the first historical data includes the data corresponding to the multiple fields;

[0128] 1012. Based on the at least one piece of first historical data, determine the first relationship between the data corresponding to the multiple fields.

[0129] It should be noted here that the use of "first" to limit the historical data is only to distinguish it from the historical data that appears in the following text. The first historical data can be the historical orders of any target object (such as a commodity) within a preset time period, and the length of the preset time period can be flexibly set according to actual needs, such as 2 days, 5 days, one month, one quarter, one year, etc. Taking the first historical data as historical order data as an example, the multiple fields included in the historical order data can be fields related to the amount, such as af (order price, which can be understood as the actual paid amount), auction_price (unit price of the commodity), buy_amount (number of purchased items), discount_fee (amount of discounts, such as commodity discounts, store coupons, full reduction, etc.), stotaltax (total tax amount), sharedpostfee (postage), etc. The data corresponding to the multiple fields can be the specific values corresponding to the respective fields. Here, for the convenience of description, historical order data is used as an example. In fact, the first historical data can be any data with multiple fields and a certain relationship between the multiple fields. This embodiment does not make specific limitations on this.

[0130] In specific implementation, the execution entity of this embodiment can obtain at least one first historical data in any manner. For example, it can be obtained by the user trigger method, that is: the first historical data can be stored in a storage medium. When retrieving the first historical data for the processing of the above steps, the user can input the identifier of at least one first historical data or select at least one first historical data from multiple historical data through the interaction methods provided by the interaction interface, such as mouse, keyboard, voice, etc. After the user completes the input or selection, the execution entity provided by this embodiment will obtain at least one first historical data from the corresponding storage medium. In addition, the acquisition of the first historical data may not be triggered by the user, but by a task. For example, when performing a data monitoring task, at least one first historical data can be obtained according to the address specified by the monitoring task. In addition, the execution entity of this embodiment can also actively obtain at least one first historical data. For example, the execution entity of this embodiment can obtain at least one first historical data from a data source through a web crawler (also known as a web spider or web robot), where the data source can be, for example, the servers corresponding to each e-commerce platform.

[0131] In the above 1012, the relationship between the data corresponding to the multiple fields can be analyzed by analyzing the at least one first historical data. For example, the first historical data includes the following fields and the field values corresponding to the fields:

[0132] Field Field Value Commodity Price a Commodity Quantity b Discount Amount c Order Price d

[0133] Through analysis, it can be obtained that: d = a * b - c

[0134] From this, the relationship between the data corresponding to multiple fields can be obtained as: Order price = Commodity price * Quantity of commodities - Discount amount

[0135] In an implementable technical solution, the above analysis process can be implemented using a relationship analysis model. That is, the above 1012 "Determine the first relationship between the data corresponding to multiple fields based on the first historical data" can specifically include the following steps:

[0136] 10121. Obtain the relationship analysis model;

[0137] 10122. Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one first historical data, and obtain a calculation rule reflecting the first relationship between the data corresponding to multiple fields.

[0138] In the above 10121, the relationship analysis model can be a machine learning model; correspondingly, the training process of the relationship analysis model can include the following steps:

[0139] Obtain training samples; where the training samples include: sample values corresponding to multiple fields, and sample relationships between the sample values corresponding to multiple fields;

[0140] Use the sample values corresponding to multiple fields as the input of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship between the sample values corresponding to the multiple fields;

[0141] Optimize the relationship model based on the output result and the sample relationship.

[0142] In the above 10122, the data corresponding to multiple fields in at least one first historical data can be input into the relationship analysis model. After a series of analyses, judgments, calculations, etc. by the relationship analysis model, a calculation rule that can reflect the first relationship between the data corresponding to multiple fields can be output.

[0143] Exemplarily, taking Figure 2 a sub - order of a certain international commodity A shown as an example, this sub - order contains multiple fields related to amounts, such as: af (actual paid amount 10), auction_price (commodity unit price 11), buy_amount (quantity purchased 12), discount_fee (discount amount 13, such as discounts, store coupons, full - reduction, etc.), sharedpostfee (postage 14), adjustfee (manual adjustment amount, that is, it can be understood as the amount manually modified by the seller, such as the amount modified during returns and exchanges, Figure 2 not shown in the figure), stotaltax (total tax amount, which can be understood as the import tax 15 in this figure), and Figure 2Taxcustomdutyfee (tariff), adjusttaxfee (adjustment tax and fees), taxvatfee (value-added tax), taxexcisedutyfee (excise tax), customssubtotalfee (dutiable value of goods), customsCouponFee (customs preferential fees), taxcoupondiscount (tax-inclusive preferential amount), customsInsuranceFee (customs insurance fees), taxCountFee (specific duty), etc., which are not shown in the figure, input the data corresponding to each of the above multiple fields into the relationship analysis model. After analyzing and calculating the data corresponding to each of the above multiple fields, the relationship analysis model will output the following calculation rules reflecting the first relationship between the data corresponding to multiple fields:

[0144] af = auction_price * buy_amount - discount_fee + adjustfee - adjusttaxfee + stotaltax + sharedpostfee

[0145] = customssubtotalfee + stotaltax - customscouponfee;

[0146] stotaltax = taxexcisedutyfee + taxcustomdutyfee + taxvatfee;

[0147] It should be noted here that: in the solution provided in this embodiment, the relationship analysis model can be pre-written program code, application program, functional module, etc., and can be registered in the corresponding storage medium through the interaction interface. The detailed content of the registration of the relationship analysis model will be elaborated in detail below.

[0148] In the above 102, the monitoring data may include, but is not limited to: data sent from the network side device (such as a client device), data in the local storage area, user-specified data, etc. This embodiment does not make specific limitations on this.

[0149] In the above 103, according to the calculation rules obtained through the relationship analysis model that can reflect the first relationship between the data corresponding to multiple fields, analyze the data corresponding to multiple fields in the monitoring data, so as to determine whether the relationship between the data corresponding to multiple fields in the monitoring data conforms to the first relationship. When the relationship between the data corresponding to multiple fields in the monitoring data does not conform to the first relationship, it is determined that the monitoring data is incorrect data; otherwise, the monitoring data is correct data.

[0150] The technical solution provided in this embodiment determines whether the relationships between the data corresponding to multiple fields in the acquired monitoring data conform to the first relationship based on the first relationship between the data corresponding to multiple fields determined based on at least one piece of first historical data, so as to judge whether the monitoring data is correct based on the determination result. Since the first relationship between the data corresponding to multiple fields is automatically mined by using a relationship analysis model without human intervention and has high accuracy, the efficiency and accuracy of data monitoring can be effectively improved. In addition, this solution can be applied to any scenario involving amount calculation, such as order amount calculation, order amount correctness verification, automated sticker landed price calculation, automated sticker correctness verification, etc., with a wide application range and strong practicability.

[0151] In fact, the relationships between the data corresponding to multiple fields may change. For example, in the calculation of the order amount, the preferential strategy changes, resulting in changes in the relationships between the data corresponding to multiple fields in the order. Therefore, in the implementation application, it is necessary to re-determine the relationships between the data corresponding to multiple fields regularly or irregularly (such as manually triggered). That is, the method provided in this embodiment may further include the following steps:

[0152] 104a. In response to the event of re-determining the relationship, obtain at least one piece of second historical data;

[0153] 104b. Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one piece of second historical data to obtain a second relationship between the data corresponding to multiple fields;

[0154] 104c. When the second relationship changes compared with the first relationship, output a user-perceivable prompt indicating that the relationship has changed.

[0155] The second historical data is different from the first historical data, and this difference can be the difference in the generation time of the historical data. For the specific implementation processes of the above steps 104a to 104b, reference can be made to the corresponding content in the above embodiment, which will not be elaborated here.

[0156] In the above 104c, when the second relationship changes compared with the first relationship, the execution entity of this embodiment can output a user-perceivable prompt indicating that the second relationship has changed compared with the first relationship and send the user-perceivable prompt to the client device for display. The user-perceivable prompt can be any one or more of forms such as images, texts, voices, etc., which are not specifically limited in this embodiment.

[0157] Further, after the above step 104c, it further includes at least one of the following steps:

[0158] 104d. Regularly trigger the event of re-determining the relationship;

[0159] 104e. Generate the relationship re-determination event in response to the trigger of the user.

[0160] In specific implementation, the relationship re-determination event can be triggered regularly or by the user. For example, the execution entity of this embodiment can be pre-configured with a relationship re-determination period, and the execution entity can initiate a request for relationship re-determination regularly according to the relationship re-determination period; for another example, when the execution entity of this embodiment is a server, the client device communicatively connected to the server configures a corresponding function for the user, such as a touchable control or a voice control interface is displayed on the user interface, etc., and the user can trigger the relationship re-determination event through this function to send a request for relationship re-determination to the server device.

[0161] Further, the method provided in this embodiment further includes the following steps:

[0162] 105a. When it is determined that the relationship between the data corresponding to multiple fields in the monitoring data does not conform to the first relationship, determine that the monitoring data is incorrect data;

[0163] 105b. Send the incorrect data to the client device; and / or correct the monitoring data according to the first relationship, and update the monitoring data to the corrected monitoring data.

[0164] In specific implementation, an application for analyzing incorrect data (such as a loss data analysis platform) can be installed on the client device. After receiving the incorrect data, the client device can use an application such as the loss data analysis platform to analyze the incorrect data so as to obtain a parameter reflecting the degree of influence of the error. Of course, the execution entity of this embodiment can also directly collect the monitored incorrect data, calculate a parameter reflecting the degree of influence of the error based on the collected incorrect data, and then send the parameter to the client device for display, so that the user can make targeted adjustments to the monitoring data based on the parameter. That is, the method provided in this embodiment further includes the following steps:

[0165] Collect the monitored incorrect data;

[0166] Calculate a parameter reflecting the degree of influence of the error according to the collected incorrect data;

[0167] Send the parameter to the client device for display on the client device.

[0168] The data processing method provided in the above embodiment is applicable to any application scenario involving amount calculation, such as application scenarios of order amount calculation, correctness verification of order amount, automated sticker calculation, and correctness verification of automated stickers, etc. The following embodiments will illustrate the technical solutions provided in this application from these perspectives respectively.

[0169] Before introducing other embodiments, a brief description will be given of the implementation basis of the technical solution provided in this embodiment. The technical solutions provided in each embodiment of this application can be implemented on a platform implemented under the Figure 3 system framework shown. Figure 3 The system framework shown provides background support for the client. Users can use the various functions provided by the system framework through the client to complete tasks such as creating monitoring tasks and training models. See Figure 3 The system schematic diagram shown provides users with available data sources, such as ODPS, MYSQL, ADS, HBASE, etc., and also provides some underlying algorithms. Users can use the data provided by the data sources and these underlying algorithms to train the required models. The trained models can be stored in the Figure 3 model center of the system framework shown. Among them, the underlying algorithms can include: machine learning algorithms (such as clustering algorithms, K-nearest neighbor algorithms, decision tree algorithms, random forest algorithms, etc.), deep learning algorithms (such as neural network learning algorithms, deep reinforcement learning algorithms, recurrent neural network algorithms, etc.), big data mining algorithms, etc. Each model in the model center (such as model 1, model 2, model 3, model 4,...) can be called by the corresponding task. For example, Figure 3 the tasks related to data volume statistics shown in, such as access task volume statistics, model call volume statistics, processed data volume statistics, problem discovery volume statistics, data monitoring views, etc.; tasks related to data monitoring, such as monitoring task trigger mechanisms, execution strategies, retry mechanisms, scheduling strategies, notification mechanisms, etc. These tasks provide support for specific products or services.

[0170] It can be seen from this that Figure 3 the platform implemented under the system framework shown provides support for users to implement corresponding functions. For example, users can create models through the client interface corresponding to this platform to provide various computing capabilities for this platform. See Figure 4a and Figure 4b shown. By triggering the "Create Model" control under the model center list in the interaction interface, it is possible to jump to the model registration page shown in Figure 4a . After inputting the relevant information of the relationship analysis model, such as the name of the relationship analysis model "Relational Field Deduction", the model type "Inter-field Association Relationship", the model configuration {"projectName": "***", "readTableName": "Please enter the data table name", "targetFileld": "Please enter the target field", "relatedField" = "Please enter the associated field"}, the template code type "PYODPS3", as well as the model code and model description (the relevant content is not shown in the figure) to the corresponding positions on this model registration page, and then triggering the "Submit" control, it is possible toFigure 4b In the displayed model list, a relationship analysis model named "Relational Field Deduction" is shown. Among them, projectName in the above model configuration represents the path where the model code is located; readTableName represents the data source of the model input; targetFileld represents the target field in the input data, such as Figure 2 the field af corresponding to the actual paid amount in Figure 2 the fields auction_price, buy_amount, discount_fee, sharedpostfee, adjustfee, etc. associated with the field af in

[0171] Based on the algorithm and data support provided by the system as shown in Figure 3 the user can train a calculation rule through the corresponding client of the platform that can automatically discover the relationship between the data corresponding to multiple fields, so that when the user needs to monitor the monitoring data in a certain application scenario, such as commodity order data, during the process of creating a monitoring task, the user can directly call the relationship analysis model to obtain the calculation rule for the relationship between the data corresponding to multiple fields in the monitoring data, and then based on this calculation rule, determine whether the monitoring data in the monitoring task is correct. Based on this, the monitoring data described in the above 102 can be obtained according to the monitoring task. In an implementable technical solution, step 102 may specifically include:

[0172] Obtain the monitoring task created by the user, where the monitoring task contains the data requirements of the monitored data;

[0173] Select the monitoring data from the received data and / or from the database according to the data requirements of the monitored data.

[0174] Specifically, the data requirements of the monitored data may be the address of the monitored data and / or the preset conditions of the monitored data, where the preset conditions may be determined according to the attributes set in the corresponding coding program of the relationship analysis model for multiple fields in the monitored data. When the data requirement is the address of the monitoring data, the monitoring data can be selected from the database according to the address of the monitoring data; when the data requirement is the preset condition of the monitoring data, the monitoring data that meets the preset condition can be filtered out from the received data according to the preset condition.

[0175] For example, in combination with the above Figure 3 shown platform, assuming that the user needs to monitor a certain commodity order in an application scenario, at this time, the user can first trigger the "Create Scenario" control in the scenario center list on the platform interaction interface to jump to the create scenario interface (such asFigure 5a ) Enter information related to the application scenario to be created in the creation scenario interface, such as the scenario name "Monitoring Scenario", the task described in the scenario "*** International Import", the administrator, and the scenario description (related content Figure 5a not shown in the figure), etc., and then trigger the Figure 5a "Submit" control shown in the figure to complete the creation of the application scenario. The created application scenario can be displayed in the scenario list. For details, please refer to Figure 5b the scenario display list shown in the figure. After completing the creation of the application scenario, a corresponding commodity order monitoring task can be created for this application scenario. Specifically, continue to refer to Figure 5b as shown in the figure, the "View Task" under the corresponding operation function of the application scenario with the scenario name "Monitoring Scenario" can be triggered to display a Figure 6a task creation interface as shown in the figure. Enter relevant information for the commodity order monitoring task in this task creation interface, such as: the task name of this monitoring task "*** International Order Table Analysis", the information corresponding to the relationship analysis model required to complete this monitoring task, such as the model type "Inter-field Association Relationship", the model name "Relational Field Deduction", and the information related to the commodity order to be monitored. Specifically, it can be referred to the information shown in the task configuration module in this task creation interface. For example, "projectName": "***", "readTableName": "s_tmallhk_biz_order_money_field_service_sub_Only", "targetFileld": "af", and "relatedField": "auction_price, buy_amount, discount_fee, sharedpostfee, adjustfee, stotaltax, taxcustomdutyfee, adjusttaxfee, taxvatfee, taxexcisedutyfee, customssubtotalfee, customscouponfee, taxcoupondiscount, customsInsuranceFee, taxCountFee, pay_status". The above pay_status can represent the payment status of the order. The meanings corresponding to the other specific information shown in the above task configuration module can be referred to the relevant introductions in the above other embodiments and will not be elaborated here; then trigger the "Submit" control on this task creation interface to complete the creation of the monitoring task. The created commodity order monitoring task can be displayed in Figure 6aShown in the task display list. When the user triggers the "Run" control under the corresponding operation with the task name "*** International Order Form Analysis", the execution entity in this embodiment will select all the order data recorded in the data table named "s_tmallhk_biz_order_money_field_service_sub_Only" from the corresponding database, and at the same time screen out the monitoring orders that meet the requirements of the preset conditions from all the received order data according to the set preset conditions. Specifically, when the relationship analysis model receives multiple fields in the monitoring data input by the user, such as fields like af, auction_price, buy_amount, discount_fee, sharedpostfee, adjustfee, etc., it will determine whether each of them is an enumerable field according to the respective attributes (such as enumeration attributes) corresponding to the preset multiple fields, and use the enumerable fields among the multiple fields as classification fields to divide all the received orders into several parts according to the classification fields, so as to select the monitoring orders from the received data.

[0176] For example, continue to refer to Figure 6a and Figure 6b shown. When the user triggers the "Run" control under the corresponding operation with the task name "*** International Order Form Analysis", the execution entity of this embodiment will divide all the order data obtained from the data table "s_tmallhk_biz_order_money_field_service_sub_Only" into several parts according to the preset conditions; input each part of the order data into the relationship analysis model respectively, and the relationship analysis model can obtain the respective calculation rules corresponding to each part of the order data through analyzing and calculating the received order data for each part; analyze each part of the order data according to the respective calculation rules corresponding to each part of the order data, and then the orders that do not meet their corresponding calculation rules in each part of the order data can be determined. For the specific results, refer to Figure 7 described. In Figure 7Among them, the set preset conditions are the values corresponding to the classification fields (i.e., adjustfee, adjusttaxfee, taxcustomdutyfee, Customssubtotalfee, pay_status). Among them, pay_status corresponds to 7 different values, respectively representing different payment statuses. Specifically, pay_stuaty = 1 means unfrozen / unpaid -> waiting for the buyer to pay; pay_stuaty = 2 means frozen / paid -> waiting for the seller to ship; pay_stuaty = 4 means refunded -> transaction closed; pay_stuaty = 6 means transferred transaction -> transaction successful; pay_stuaty = 7 means no external transaction (Alipay transaction) is created; pay_stuaty = 8 means the transaction is closed by Taobao; pay_stuaty = 9 means non-payable (not shown in the figure).

[0177] Figure 8 The flowchart of the data processing method provided by another embodiment of the present application is shown. The execution subject of this method can be the server. As Figure 8 shown, this method includes the following steps:

[0178] 201. Determine the order amount calculation rule according to at least one historical order;

[0179] 202. After receiving the order placement request triggered by the user, obtain the data corresponding to multiple fields included in the order placement data;

[0180] 203. Use the order amount calculation rule to calculate the data corresponding to multiple fields included in the to-be-ordered data to obtain the order amount corresponding to the to-be-ordered data;

[0181] 204. Send the order amount to the client.

[0182] In the above 201, the historical order can be the historical order corresponding to any target object (such as a commodity) within a preset time period. The length of the preset time period can be flexibly set according to actual needs, such as 2 days, 5 days, 8 days, one month or longer. This embodiment does not make specific limitations on this. In addition, in this historical order, there are multiple fields related to the amount, such as af (order price, which can be understood as the actual paid amount in the order), auctionPrice (unit price of the commodity), buyAmount (number of purchased items), discountFee (amount of discount, such as commodity discount, store coupon, full reduction, etc.), sTotalTax (total tax amount), sharedPostFee (postage), etc. Each of the multiple fields corresponds to a specific value. In specific implementation, the relationship analysis model can be used to complete the process of determining the order amount calculation rule. That is, in a feasible technical solution, the above step 201 "determine the order amount calculation rule according to at least one historical order" can specifically include the following steps:

[0183] 2011. Obtain the relationship analysis model;

[0184] 2012. Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical order, and obtain an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in the at least one historical order.

[0185] In specific implementation, the relationship analysis model is a machine learning model, which can be obtained by training with training samples. The training process can refer to the corresponding content above and will not be elaborated here.

[0186] In the above 202, the user can trigger an order placement request for at least one target object through a personal handheld client (such as any terminal device like a mobile phone, tablet computer, smart wearable device, etc.). The order placement request carries the identification information of at least one target object. After receiving the order placement request sent by the user through the client, the server can obtain the data corresponding to multiple fields included in the order placement data according to the identification of at least one target object carried in the order placement request.

[0187] In the above 203, the data corresponding to multiple fields included in the to-be-ordered data can be used as input parameters of the order amount calculation rule, so as to obtain the order amount corresponding to the to-be-ordered data.

[0188] In the above 204, sending the order amount to the client can facilitate the client to generate relevant order information based on the order amount.

[0189] The technical solution provided in this embodiment determines the order amount calculation rule based on at least one historical order, and obtains the data corresponding to multiple fields included in the order placement data after receiving the order placement request triggered by the user. Then, the data corresponding to multiple fields included in the to-be-ordered data is calculated by using the order amount calculation rule, so as to obtain the order amount corresponding to the to-be-ordered data. After that, the order amount is sent to the client, which is convenient for the client to generate corresponding order information based on the order amount. Since the order amount calculation rule is automatically mined by using the relationship analysis model without manual intervention and has high accuracy, it helps to improve the calculation efficiency and accuracy of the order amount corresponding to the to-be-ordered data.

[0190] Of course, the technical solution provided in this embodiment can also regularly use the relationship analysis model to re-determine the order amount calculation rule for the relationship between the data corresponding to multiple fields in at least one historical order, so as to timely discover whether the order amount calculation rule has changed. That is, the method provided in this embodiment further includes the following steps:

[0191] Furthermore, the method provided in this embodiment further includes the following steps:

[0192] 205. Regularly use the relationship analysis model to determine the order amount calculation rule for the relationship between the data corresponding to multiple fields in at least one historical order;

[0193] 206. When it is determined that the order amount calculation rule has changed, output a corresponding user-perceivable prompt.

[0194] Specifically, the user-perceivable prompt can be at least one of the following, but not limited to: image, text, voice, etc.

[0195] It should be noted here that for the content not detailed in each step of the data processing method provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. In addition, in addition to the above steps, the method provided in the embodiments of the present application may further include some or all of the other steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, which will not be elaborated here.

[0196] Figure 9 shows a schematic flowchart of a data processing method provided in another embodiment of the present application. As Figure 9 shown, the method includes the following steps:

[0197] 301. Determine the order amount calculation rule according to at least one historical order;

[0198] 302. Obtain the monitored order;

[0199] 303. Determine whether the order amount of the monitored order is correct based on the above order amount calculation rule.

[0200] For the specific content of the above steps 301 to 303, reference may be made to the corresponding descriptions in the above embodiments.

[0201] In the above 301, a relationship analysis model can be used to complete the determination process of the order amount calculation rule. That is, in one implementable technical solution, the above step 301, "determine the order amount calculation rule according to at least one historical order", may specifically include the following steps:

[0202] 3011. Obtain a relationship analysis model;

[0203] 3012. Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical order, and obtain an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in the at least one historical order.

[0204] The monitoring data described in the above 302 can be obtained according to a monitoring task. That is, one implementable technical solution for "obtain a monitored order" is: obtain a detection task created by a user, where the monitoring task contains the order requirements of the order to be monitored; according to the order requirements of the order to be monitored, select the monitored order from the received order data and / or from the database.

[0205] The technical solution provided in this embodiment determines the order amount calculation rule according to at least one historical order, and uses this order amount calculation rule to determine whether the order amount in the obtained monitored order is correct. Since the order amount calculation rule is automatically mined using a relationship analysis model without human intervention and has a high accuracy, it helps to improve the monitoring efficiency and accuracy of orders.

[0206] Furthermore, the method provided in this embodiment may further include the following steps:

[0207] 304a. Regularly use the relationship analysis model to determine the order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in at least one historical order;

[0208] 304b. When it is determined that the order amount calculation rule has changed, output a corresponding user-perceivable prompt.

[0209] Specifically in implementation, the user-perceivable prompt may be, but is not limited to, at least one of the following: image, text, voice, etc.

[0210] Furthermore, the method provided in this embodiment may further include the following steps:

[0211] 305a. When it is determined that the order amount of the monitored order is incorrect, determine that the monitored order is an incorrect order;

[0212] 305b. Send the incorrect order to the client device; and / or correct the monitored order according to the order amount calculation rule, and update the monitored order to the corrected monitored order.

[0213] Further, the method described above may further include the following steps:

[0214] 305c. Collect the monitored incorrect orders;

[0215] 305d. Calculate a parameter reflecting the degree of error impact according to the collected incorrect orders;

[0216] 305e. Send the parameter to the client device for display on the client device.

[0217] It should be noted here that for the content not detailed in each step of the data processing method provided in the embodiments of the present application, reference may be made to the corresponding content in the above embodiments, and details will not be repeated here. In addition, in addition to the above steps, the method provided in the embodiments of the present application may further include other parts or all of the steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, and details will not be repeated here.

[0218] Figure 10a Shows a schematic flowchart of a data processing method provided in another embodiment of the present application. As Figure 10a shown, the method includes the following steps:

[0219] 401. Determine the first relationship between the data corresponding to multiple fields;

[0220] 402. Obtain the data corresponding to multiple fields associated with the monitored object;

[0221] 403. Determine the texture data corresponding to the monitored object according to the data corresponding to the multiple fields associated with the monitored object and the first relationship;

[0222] 404. Send the texture data to the client for display on the client.

[0223] For the specific content of the above steps 401 to 404, reference may be made to the corresponding descriptions in the above embodiments.

[0224] The above 401 may specifically include:

[0225] 4011. Obtain at least one piece of historical data, where the historical data includes the data corresponding to multiple fields;

[0226] 4012. Determine a first relationship between the data corresponding to multiple fields based on the at least one historical data.

[0227] In specific implementation, a relationship analysis model can be used to complete the process of determining the first relationship between the data corresponding to multiple fields. That is, in one implementable technical solution, step 4012, "Determine a first relationship between the data corresponding to multiple fields according to at least one historical data", may specifically include: obtaining a relationship analysis model; using the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical data to obtain a calculation rule reflecting the first relationship between the data corresponding to multiple fields.

[0228] In the above 402, the data corresponding to the multiple fields associated with the monitoring object can be obtained according to the monitoring task. That is, one implementable technical solution for step 402, "Obtain the data corresponding to the multiple fields associated with the monitoring object", is as follows:

[0229] 4021. Obtain a monitoring task created by a user, where the monitoring task contains data requirements for the data corresponding to multiple fields associated with the monitored object;

[0230] 4022. Select the data corresponding to the multiple fields associated with the monitored object from the received data and / or from the database according to the data requirements for the data corresponding to the multiple fields associated with the monitored object.

[0231] In the above 403, the data corresponding to the multiple fields associated with the monitoring object obtained can be calculated according to the calculation rule reflecting the first relationship between the data corresponding to multiple fields, so as to obtain the sticker data of the monitoring object.

[0232] For example, assume that the monitoring object is a commodity, specifically it can be Figure 10b the shoe B shown in. Before the start of a certain promotion activity (such as the 9th anniversary celebration), the discounts applied by the shoe B for signing up for this promotion activity are the activity price of ￥259, a ￥40 immediate discount for placing an order, and a ￥20 coupon. After the execution entity of this embodiment receives the discount information for the shoe's discount registration, it can automatically calculate the estimated final price 31 during the promotion activity of the shoe B and some guiding copywriting (such as copywriting 32, copywriting 32) according to the calculation rule reflecting the first relationship between the data corresponding to multiple fields, and a sticker data 30 corresponding to the shoe B can be generated by combining the estimated final price 31 and some guiding copywriting; send the sticker data 30 to the client, so as to replace the main Figure 20

[0233] The technical solution provided in this embodiment can determine the first relationship between the data corresponding to multiple fields in at least one historical data based on the obtained at least one historical data; then, according to the data corresponding to multiple fields associated with the monitoring object and the first relationship, the mapping data corresponding to the monitoring object can be determined, and then the mapping data is sent to the client for display on the client. Since the first relationship between the data corresponding to multiple fields in at least one historical data is automatically mined based on the relationship analysis model without human intervention, it has a high accuracy and can effectively improve the efficiency and accuracy of mapping data calculation.

[0234] Further, the method provided in this embodiment further includes the following steps:

[0235] 405. Regularly use the relationship analysis model to determine the first relationship between the data corresponding to multiple fields in at least one historical data;

[0236] 406. When the determined first relationship changes, output a corresponding user-perceivable prompt.

[0237] Specifically, the user-perceivable prompt can be at least one of the following: image, text, voice, etc.

[0238] It should be noted here that: for the content not detailed in each step of the data processing method provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. In addition, in addition to the above steps, the method provided in the embodiments of the present application may further include other parts or all of the steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, which will not be elaborated here.

[0239] Figure 11 shows a schematic flowchart of a data processing method provided in another embodiment of the present application. As Figure 11 shown, the method includes the following steps:

[0240] 501. Determine the first relationship between the data corresponding to multiple fields;

[0241] 502. Obtain the data corresponding to multiple fields associated with the monitoring object and the mapping data;

[0242] 503. Determine whether the mapping data is correct according to the data corresponding to multiple fields associated with the monitoring object and the first relationship.

[0243] For the specific content of the above steps 501 to 503, reference can be made to the corresponding description in the above embodiments.

[0244] In the above 501, the first relationship between the data corresponding to multiple fields can be determined based on the at least one historical data. For example, the process of determining the first relationship between the data corresponding to multiple fields is completed by using a relationship analysis model. That is, in one implementable technical solution, the step of "determining the first relationship between the data corresponding to multiple fields according to at least one historical data" may specifically include: obtaining a relationship analysis model; using the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical data, and obtaining a calculation rule reflecting the first relationship between the data corresponding to multiple fields.

[0245] The technical solution provided in this embodiment can determine the first relationship between the data corresponding to multiple fields based on the at least one historical data obtained. Furthermore, based on the data corresponding to multiple fields associated with the monitoring object and the first relationship obtained, it can be determined whether the mapped data associated with the monitoring object is correct. Since the first relationship between the data corresponding to multiple fields is automatically mined by using a relationship analysis model without human intervention and has a high accuracy, it helps to improve the monitoring efficiency and accuracy of the mapped data.

[0246] Of course, the execution entity of this embodiment can also regularly use the relationship analysis model to determine whether the first relationship between the data corresponding to multiple fields in at least one historical data has changed. That is, the method provided in this embodiment further includes the following steps:

[0247] 504a. Regularly use the relationship analysis model to determine the first relationship between the data corresponding to multiple fields in at least one historical data;

[0248] 504b. When the determined first relationship changes, output a corresponding user-perceivable prompt.

[0249] Specifically in implementation, the user-perceivable prompt can be at least one of the following, but not limited to: image, text, voice, etc.

[0250] Furthermore, the method provided in this embodiment further includes the following steps:

[0251] 505a. When the determination result is that the mapped data is incorrect, determine the mapped data as error data;

[0252] 505b. Send the error data to the client device, and / or correct the mapped data based on the data corresponding to multiple fields associated with the monitoring object and the first relationship, and update the mapped data to the corrected mapped data.

[0253] Furthermore, the above-mentioned method further includes the following steps:

[0254] 505c. Collect the monitored error data;

[0255] 505d. Calculate a parameter reflecting the degree of error impact based on the collected error data;

[0256] 505e. Send the parameter to the client device for display on the client device.

[0257] It should be noted here that for the content not detailed in each step of the data processing method provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. In addition, in the method provided in the embodiments of the present application, in addition to the above steps, it may also include other parts or all of the steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, which will not be elaborated here.

[0258] Figure 12 The flowchart of the data monitoring method provided in another embodiment of the present application is shown. As Figure 12 shown, the method includes the following steps:

[0259] 601. In response to a user's creation operation, create a monitoring task, where the monitoring task contains data requirements for the data to be monitored;

[0260] 602. In response to a monitoring task execution instruction triggered by the user, determine the relationship between the data corresponding to multiple fields;

[0261] 603. Obtain monitoring data that meets the data requirements;

[0262] 604. Use the relationship to determine whether the relationship between the data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct or not based on the determination result.

[0263] Further, the user can also specify a model for determining the relationship between the data corresponding to multiple fields. Specifically, the monitoring task also contains a specified relationship analysis model. Correspondingly, in step 602 above, "determine the relationship between the data corresponding to multiple fields" can be specifically: use the relationship analysis model to analyze the data corresponding to multiple fields in at least one historical data to obtain the relationship between the data corresponding to multiple fields.

[0264] In the above, the monitoring task also contains: the period for calling the relationship analysis model to determine the relationship between the data corresponding to multiple fields. Correspondingly, the method provided in this embodiment further includes the following steps:

[0265] 605. Periodically use the relationship analysis model to determine the relationship between the data corresponding to multiple fields;

[0266] 606. When the determined relationship changes, output a corresponding user-perceivable prompt.

[0267] In specific implementation, the user-perceivable prompt may be, but is not limited to, at least one of the following: image, text, voice, etc.

[0268] It should be noted here that: for the content not elaborated in each step of the data monitoring method provided in the embodiments of the present application, reference may be made to the corresponding content in the above embodiments, and details will not be repeated here. In addition, in the method provided in the embodiments of the present application, in addition to the above steps, it may also include other parts or all of the steps in the above embodiments. For specific details, reference may be made to the corresponding content in the above embodiments, and details will not be repeated here.

[0269] Figure 13 The structural schematic diagram of a data processing system provided by an embodiment of the present application is shown. As Figure 13 shown, the data processing system includes: a client 701 and a server 702; where

[0270] The client 701 is configured to generate monitoring data and send the monitoring data to the server.

[0271] The server 702 is configured to determine a first relationship between data corresponding to multiple fields; obtain the monitoring data; and determine whether the relationship between data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0272] In specific implementation, the client 701 may be, for example, a desktop computer, a tablet computer, a smart phone, a smart wearable device (such as a smart watch, glasses, etc.), and so on; the server 702 may be a server device, and the server device may be a server, a server cluster, a virtual server or a cloud device, etc., which is not specifically limited here. In this embodiment, the client 701 and the server 702 may be connected through a wireless or wired network. If the client 701 and the server 702 are connected through a mobile network, the network mode of the mobile network may be any one of 4G (LTE), 4G+ (LTE+), WiMax, 5G, etc.

[0273] The technical solution provided in this embodiment determines whether the relationships between the data corresponding to multiple fields in the acquired monitoring data conform to the first relationship based on the first relationship between the data corresponding to multiple fields determined from at least one piece of first historical data, so as to judge whether the monitoring data is correct based on the determination result. Since the first relationship between the data corresponding to multiple fields is mined by an automated method without human intervention and has high accuracy, the efficiency and accuracy of data monitoring can be effectively improved. In addition, this solution can be applied to any scenario involving amount calculation, such as order amount calculation, order amount correctness verification, automated sticker landed price calculation, automated sticker correctness verification, etc., with a wide application range and strong practicability.

[0274] It should be noted here that for the functions that can be realized by the server and the client in the data processing system provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. That is, in addition to the above functions, the server and the client in the data processing system provided in the embodiments of the present application can also realize the functions corresponding to other parts or all of the steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, which will not be elaborated here.

[0275] An embodiment of the present application further provides a data processing system, and the structure of this data monitoring system is the same as that above Figure 13 . Specifically, the data monitoring system includes:

[0276] A client, which is used to send the data to be ordered to the server in response to an order placement request triggered by a user;

[0277] A server, which is used to determine an order amount calculation rule according to at least one historical order; acquire the data corresponding to multiple fields included in the data to be ordered; calculate the data corresponding to multiple fields included in the data to be ordered by using the order amount calculation rule to obtain the order amount corresponding to the data to be ordered; and send the order amount to the client;

[0278] The client is further used to display the order amount.

[0279] Similarly, for the functions that can be realized by the server and the client in the data processing system provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. That is, in addition to the above functions, the server and the client in the data processing system provided in the embodiments of the present application can also realize the functions corresponding to other parts or all of the steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, which will not be elaborated here.

[0280] An embodiment of the present application further provides a data processing system, and the structure of this data processing system is the same as that aboveFigure 13 Specifically, the data monitoring system includes:

[0281] A client for generating a monitoring order and sending the monitoring order to the server;

[0282] A server for determining an order amount calculation rule based on at least one historical order; obtaining a monitoring order; and determining whether the order amount of the monitoring order is correct based on the order amount calculation rule.

[0283] Similarly, for the functions that can be realized by the server and the client in the data processing system provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. That is, in addition to having the above functions, the server and the client in the data processing system provided in the embodiments of the present application can also realize the functions corresponding to other parts or all of the steps in the above embodiments. For specific details, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here.

[0284] An embodiment of the present application further provides a data processing system, and the structure of this data processing system is the same as the above Figure 13 Specifically, the data monitoring system includes:

[0285] A server for determining a first relationship between data corresponding to multiple fields; obtaining data corresponding to multiple fields associated with a monitoring object; and determining mapping data corresponding to the monitoring object according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship;

[0286] A client for displaying the mapping data corresponding to the monitoring object.

[0287] Similarly, for the functions that can be realized by the server and the client in the data processing system provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. That is, in addition to having the above functions, the server and the client in the data processing system provided in the embodiments of the present application can also realize the functions corresponding to other parts or all of the steps in the above embodiments. For specific details, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here.

[0288] An embodiment of the present application further provides a data monitoring system, and the structure of this data processing system is the same as the above Figure 13 Specifically, the data monitoring system includes:

[0289] A client for generating mapping data corresponding to a monitoring object;

[0290] A server, configured to determine a first relationship among data corresponding to multiple fields; obtain data corresponding to the multiple fields associated with the monitoring object and the mapping data; and determine whether the mapping data is correct according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship.

[0291] Similarly, for the functions that can be realized by the server and the client in the data processing system provided in the embodiments of the present application, reference can be made to the corresponding content in the above embodiments, which will not be elaborated here. That is, in addition to the above functions, the server and the client in the data processing system provided in the embodiments of the present application can also realize functions corresponding to other parts or all of the steps in the above embodiments. For specific reference, see the corresponding content in the above embodiments, which will not be elaborated here.

[0292] An embodiment of the present application further provides a data monitoring system, and the structure of the data processing system is the same as that in the above Figure 13 . Specifically, the data monitoring system includes:

[0293] A client, configured to create a monitoring task in response to a user's creation operation, where the monitoring task contains data requirements for the monitored data; send the monitoring task to the server; and send a start message for the monitoring task to the server in response to a monitoring task execution instruction triggered by the user.

[0294] A server, configured to receive and deploy the monitoring task, execute the monitoring task after receiving the start message, determine the relationship among data corresponding to multiple fields; obtain monitoring data that meets the data requirements; and use the relationship to determine whether the relationship among data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct based on the determination result.

[0295] The technical solution provided in this embodiment can create a monitoring task containing data requirements for the monitored data in response to a user's creation operation; then, in response to a monitoring task execution instruction triggered by the user, determine the relationship among data corresponding to multiple fields; and use this relationship to determine whether the obtained monitoring data that meets the data requirements is correct. Since the relationship among data corresponding to multiple fields is automatically mined by using a relationship analysis model without manual intervention and has high accuracy, it helps to improve the efficiency and accuracy of monitoring.

[0296] Figure 14 The structural block diagram of a data processing device provided in an embodiment of the present application is shown. As Figure 14 shown, the data processing device includes: a first determination module 801, a second acquisition module 802, and a second determination module 803; where

[0297] The first determination module 801 is configured to determine the first relationship among the data corresponding to multiple fields;

[0298] The acquisition module 802 is configured to acquire monitoring data;

[0299] The second determination module 803 is configured to determine whether the relationship among the data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to judge whether the monitoring data is correct based on the determination result.

[0300] Specifically, the determination module 801 may include an acquisition unit and a determination unit. The acquisition unit is configured to acquire at least one piece of first historical data, where the first historical data includes the data corresponding to multiple fields. The determination unit is configured to determine the first relationship among the data corresponding to multiple fields based on the at least one piece of first historical data.

[0301] The technical solution provided in this embodiment determines whether the relationship among the data corresponding to multiple fields in the acquired monitoring data conforms to the first relationship based on the first relationship among the data corresponding to multiple fields determined from the acquired at least one piece of first historical data, so as to judge whether the monitoring data is correct based on the determination result. Since the first relationship among the data corresponding to multiple fields is automatically mined by using a relationship analysis model without manual intervention and has a high accuracy, the efficiency and accuracy of data monitoring can be effectively improved. In addition, this solution can be applied to any scenario involving amount calculation, such as order amount calculation, order amount correctness verification, automated sticker landed price calculation, automated sticker correctness verification, etc., and has a wide application range and strong practicability.

[0302] Further, when the first determination module 801 determines the first relationship among the data corresponding to multiple fields based on the at least one piece of first historical data, it is specifically configured to: acquire a relationship analysis model; use the relationship analysis model to analyze the data corresponding to multiple fields of the at least one piece of first historical data, and obtain a calculation rule reflecting the first relationship among the data corresponding to multiple fields.

[0303] Further, the relationship analysis model is a machine learning model. Correspondingly, the relationship analysis model can be trained in the following manner:

[0304] Acquire training samples; where the training samples include: sample values corresponding to multiple fields, and sample relationships among the sample values corresponding to multiple fields;

[0305] Use the sample values corresponding to multiple fields as the input of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship among the sample values corresponding to multiple fields;

[0306] Optimize the relationship analysis model based on the output result and the sample relationship.

[0307] Further, the device provided in this embodiment further includes: a response module, an analysis module, and an output module, where,

[0308] The response module is configured to obtain at least one piece of second historical data in response to the relationship re - determination event;

[0309] The analysis module is configured to analyze the data corresponding to multiple fields in the at least one piece of second historical data by using the relationship analysis model to obtain a second relationship between the data corresponding to the multiple fields;

[0310] The output module is configured to output a user - perceivable prompt that the relationship has changed when the second relationship changes compared with the first relationship.

[0311] Further, the device provided in this embodiment further includes at least one of the following: a trigger module, a generation module; where,

[0312] The trigger module is configured to periodically trigger the relationship re - determination event;

[0313] The generation module is configured to generate the relationship re - determination event in response to the user's trigger.

[0314] Further, the device provided in this embodiment further includes:

[0315] The judgment module is configured to judge that the monitoring data is incorrect data when it is determined that the relationship between the data corresponding to multiple fields in the monitoring data does not conform to the first relationship;

[0316] The first sending module is configured to send the incorrect data to the client; and / or the error - correction module is configured to correct the monitoring data according to the first relationship and update the monitoring data to the corrected monitoring data.

[0317] Further, the device provided in this embodiment further includes:

[0318] The collection module is configured to collect the monitored incorrect data;

[0319] The calculation module is configured to calculate a parameter reflecting the degree of error impact according to the collected incorrect data;

[0320] The second sending module is configured to send the parameter to the client device for display on the client device.

[0321] Further, when obtaining the monitoring data, the obtaining module 802 is specifically configured to: obtain a monitoring task created by a user, where the monitoring task contains data requirements for the monitored data; and select the monitoring data from the received data and / or from a database according to the data requirements for the monitored data.

[0322] It should be noted here that: the data processing device provided in this embodiment can implement the technical solutions described in the above Figure 8 data processing method embodiments shown. For the specific principles implemented by the above modules or units, reference can be made to the corresponding content in the above Figure 8 data processing method embodiments shown, which will not be elaborated here.

[0323] Figure 15 The structural block diagram of a data processing device provided in another embodiment of the present application is shown. As Figure 15 shown, the data processing device includes: a determination module 901, an obtaining module 902, a calculation module 903, and a sending module 904; where,

[0324] The first determination module 901 is configured to determine an order amount calculation rule according to at least one historical order;

[0325] The obtaining module 902 is configured to, after receiving a placing order request triggered by a user, obtain data corresponding to multiple fields included in the data to be placed in an order;

[0326] The calculation module 904 is configured to use the order amount calculation rule to calculate the data corresponding to multiple fields included in the data to be placed in an order, so as to obtain the order amount corresponding to the data to be placed in an order;

[0327] The sending module 904 is configured to send the order amount to a client.

[0328] The technical solution provided in this embodiment determines an order amount calculation rule according to at least one historical order, obtains data corresponding to multiple fields included in the order placement data after receiving a placing order request triggered by a user, and then uses the order amount calculation rule to calculate the data corresponding to multiple fields included in the data to be placed in an order, so as to obtain the order amount corresponding to the data to be placed in an order, and then sends the order amount to the client, which is convenient for the client to generate corresponding order information based on the order amount. Since the order amount calculation rule is automatically mined by using a relationship analysis model without manual intervention and has high accuracy, it helps to improve the calculation efficiency and accuracy of the order amount corresponding to the data to be placed in an order.

[0329] Further, when the determining module 901 determines the order amount calculation rule according to at least one historical order, it is specifically configured to: obtain a relationship analysis model; use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical order, and obtain an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in the at least one historical order.

[0330] Further, the device provided in this embodiment further includes:

[0331] A second determining module, configured to periodically use the relationship analysis model to determine an order amount calculation rule for the relationship between the data corresponding to multiple fields in at least one historical order;

[0332] An output module, configured to output a corresponding user-perceivable prompt when it is determined that the order amount calculation rule has changed.

[0333] It should be noted here that: the data processing device provided in this embodiment can implement the technical solutions described in the above Figure 9 illustrated data processing method embodiment. For the specific implementation principles of the above modules or units, reference can be made to the corresponding content in the above Figure 9 illustrated data processing method embodiment, which will not be elaborated here.

[0334] Figure 16 shows a structural block diagram of a data processing device provided in another embodiment of the present application. As Figure 16 shown, the data processing device includes: a first determining module 1001, an obtaining module 1002, and a second determining module 1003; wherein,

[0335] The first determining module 1001 is configured to determine an order amount calculation rule according to at least one historical order;

[0336] The obtaining module 1002 is configured to obtain a monitored order;

[0337] The second determining module 1003 is configured to determine whether the order amount of the monitored order is correct based on the order amount calculation rule.

[0338] The technical solution provided in this embodiment determines the order amount calculation rule according to at least one historical order, and uses this order amount calculation rule to determine whether the order amount in the obtained monitored order is correct. Since the order amount calculation rule is automatically mined using the relationship analysis model without manual intervention and has a high accuracy, it helps to improve the efficiency and accuracy of monitoring.

[0339] Further, when the first determination module is configured to determine the order amount calculation rule according to at least one historical order, it is specifically configured to: obtain a relationship analysis model; use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical order, and obtain an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in the at least one historical order.

[0340] Further, the apparatus provided in this embodiment further includes:

[0341] A third determination module, configured to periodically use the relationship analysis model to determine an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in at least one historical order;

[0342] An output module, configured to output a corresponding user-perceivable prompt when it is determined that the order amount calculation rule has changed.

[0343] Further, the apparatus provided in this embodiment further includes:

[0344] A determination module, configured to determine that the monitored order is an incorrect order when the determination result is that the order amount of the monitored order is incorrect;

[0345] A sending module, configured to send the incorrect order to a client device; and / or an error correction module, configured to correct the monitored order according to the order amount calculation rule, and update the monitored order to the corrected monitored order.

[0346] Further, the apparatus provided in this embodiment further includes:

[0347] A collection module, configured to collect monitored incorrect orders;

[0348] A calculation module, configured to calculate a parameter reflecting the degree of error influence according to the collected incorrect orders;

[0349] A sending module, configured to send the parameter to a client device for display on the client device.

[0350] Further, when the obtaining module is configured to obtain a monitored order, it is specifically configured to: obtain a detection task created by a user, where the monitoring task contains the order requirements of the monitored order; select the monitored order from the received order data and / or from a database according to the order requirements of the monitored order.

[0351] It should be noted here that: the data processing apparatus provided in this embodiment can implement the technical solutions described in the data processing method embodiment shown above. The specific implementation principles of the above modules or units can be referred to the above Figure 10a shown. Figure 10aThe corresponding content in the embodiment of the data processing method shown will not be elaborated here.

[0352] Figure 17 The block diagram of the data processing device provided by another embodiment of the present application is shown. As Figure 17 shown, the data processing device includes: an acquisition module 1101, a determination module 1102, and a sending module 1103; wherein,

[0353] The determination module 1102 is configured to determine a first relationship between data corresponding to multiple fields;

[0354] The acquisition module 1101 is further configured to acquire data corresponding to multiple fields associated with the monitoring object;

[0355] The determination module 1102 is further configured to determine the mapping data corresponding to the monitoring object according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship;

[0356] The sending module 1103 is configured to send the mapping data to the client for display on the client.

[0357] Further, the acquisition module 1101 is further configured to acquire at least one historical data; so that the determination module 1102 determines the first relationship between data corresponding to multiple fields based on the at least one historical data.

[0358] The technical solution provided in this embodiment can determine the first relationship between data corresponding to multiple fields in at least one historical data based on the acquired at least one historical data; then, according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship, the mapping data corresponding to the monitoring object can be determined, and then the mapping data is sent to the client for display on the client. Since the first relationship between data corresponding to multiple fields in at least one historical data is automatically mined based on the relationship analysis model without manual intervention, it has a high accuracy and can effectively improve the efficiency and accuracy of mapping data calculation.

[0359] Further, when the above determination module determines the first relationship between data corresponding to multiple fields based on the at least one historical data, it is specifically configured to: acquire a relationship analysis model; use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical data to obtain a calculation rule reflecting the first relationship between data corresponding to multiple fields;

[0360] Further, the determining module is further configured to periodically use the relationship analysis model to determine a first relationship between data corresponding to multiple fields in at least one historical data; and, the apparatus further includes: an output module, configured to output a corresponding user-perceivable prompt when the determined first relationship changes.

[0361] Further, when the obtaining module is configured to obtain data corresponding to multiple fields associated with a monitoring object, specifically: obtain a monitoring task created by a user, where the monitoring task contains data requirements for data corresponding to multiple fields associated with the monitored object; and select, according to the data requirements for data corresponding to multiple fields associated with the monitored object, the data corresponding to multiple fields associated with the monitored object from the received data and / or from a database.

[0362] It should be noted here that: the data processing apparatus provided in this embodiment can implement the technical solutions described in the data processing method embodiment shown above. The specific implementation principles of the above modules or units can be referred to the corresponding content in the data processing method embodiment shown above, and will not be elaborated here. Figure 11 shown, and the principles specifically implemented by the above modules or units can be referred to the corresponding content in the data processing method embodiment shown above, and will not be elaborated here. Figure 11 shown in the data processing method embodiment, and will not be elaborated here.

[0363] Figure 18 shows a structural block diagram of a data processing apparatus provided in another embodiment of the present application. As Figure 18 shown, the data processing apparatus includes: an obtaining module 1201 and a determining module 1202; wherein,

[0364] The determining module 1202 is configured to determine a first relationship between data corresponding to multiple fields;

[0365] The obtaining module 1201 is further configured to obtain data corresponding to multiple fields associated with a monitoring object and mapping data;

[0366] The determining module 1202 is further configured to determine whether the mapping data is correct according to the data corresponding to multiple fields associated with the monitoring object and the first relationship.

[0367] Further, the obtaining module 1201 is further configured to obtain at least one historical data, where the historical data includes data corresponding to multiple fields; so that the determining module 1202 determines a first relationship between data corresponding to multiple fields based on the at least one historical data.

[0368] The technical solution provided in this embodiment can determine the first relationship between the data corresponding to multiple fields based on at least one piece of historical data obtained. Furthermore, based on the data corresponding to multiple fields associated with the monitoring object and the first relationship, it can be determined whether the mapped data associated with the monitoring object is correct. Since the first relationship between the data corresponding to multiple fields is automatically mined using a relationship analysis model without human intervention and has a high accuracy, it helps to improve the monitoring efficiency and accuracy of the mapped data.

[0369] Further, when the determining module determines the first relationship between the data corresponding to multiple fields based on the at least one piece of historical data, it specifically is used for: obtaining a relationship analysis model; using the relationship analysis model to analyze the data corresponding to multiple fields in the at least one piece of historical data to obtain a calculation rule reflecting the first relationship between the data corresponding to multiple fields.

[0370] Further, the above-mentioned determining module is also used for periodically using the relationship analysis model to determine the first relationship between the data corresponding to multiple fields in at least one piece of historical data; and, the above-mentioned device further includes: an output module for outputting a corresponding user-perceivable prompt when the determined first relationship changes.

[0371] Further, the above-mentioned device further includes:

[0372] A determination module for determining that the mapped data is incorrect data when the determination result is that the mapped data is incorrect;

[0373] A sending module for sending the incorrect data to a client device; and / or an error correction module for correcting the mapped data based on the data corresponding to multiple fields associated with the monitoring object and the first relationship, and updating the mapped data to the corrected mapped data.

[0374] Further, the above-mentioned device further includes:

[0375] A collection module for collecting the monitored incorrect data;

[0376] A calculation module for calculating a parameter reflecting the degree of error influence according to the collected incorrect data;

[0377] A sending module for sending the parameter to a client device for display on the client device.

[0378] It should be noted here that: The data processing device provided in this embodiment can implement the technical solution described in the data processing method embodiment shown above. The specific implementation principles of the above-mentioned modules or units can be referred to the above Figure 12 shown. Figure 12The corresponding content in the embodiment of the data processing method shown will not be elaborated here.

[0379] Figure 19 The block diagram of a data monitoring device provided by another embodiment of the present application is shown. As Figure 19 shown, the data monitoring device includes: a response module 1301, an acquisition module 1302, and a determination module 1303; wherein,

[0380] The response module 1301 is configured to create a monitoring task in response to a user's creation operation, wherein the monitoring task contains data requirements for the data to be monitored;

[0381] The response module 1301 is further configured to determine the relationship between the data corresponding to multiple fields in response to a monitoring task execution instruction triggered by the user;

[0382] The acquisition module 1302 is configured to acquire monitoring data that meets the data requirements;

[0383] The determination module 1303 is configured to use the relationship to determine whether the relationship between the data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct or not based on the determination result.

[0384] The above monitoring task further contains a relationship analysis model specified by the user; correspondingly, when the response module determines the relationship between the data corresponding to multiple fields, it is specifically configured to: analyze the data corresponding to multiple fields in at least one historical data by using the relationship analysis model to obtain the relationship between the data corresponding to multiple fields.

[0385] The above monitoring task further contains: a period for calling the relationship analysis model to determine the relationship between the data corresponding to multiple fields; correspondingly, the determination module is further configured to periodically use the relationship analysis model to determine the relationship between the data corresponding to multiple fields; and, the above-mentioned device further includes: an output module, configured to output a corresponding user-perceivable prompt when the determined relationship changes.

[0386] It should be noted here that: the data processing device provided in this embodiment can implement the technical solutions described in the embodiment of the data processing method shown above. The specific implementation principles of the above-mentioned modules or units can be referred to the corresponding content in the embodiment of the data processing method shown above, which will not be elaborated here. Figure 13 The block diagram of an electronic device provided by an embodiment of the present application is shown. As Figure 13 The corresponding content in the embodiment of the data processing method shown will not be elaborated here.

[0387] Figure 20 shown. Figure 20As shown, the client device includes: a memory 1401 and a processor 1402. The memory 1401 can be configured to store various other data to support operations on the sensor. Examples of such data include instructions for any application or method for operating on the sensor. The memory 1401 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0388] The processor 1402, coupled to the memory 1401, is configured to execute the program stored in the memory 1401 for:

[0389] Determine a first relationship between data corresponding to multiple fields;

[0390] Obtain monitoring data;

[0391] Determine whether the relationship between data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to judge whether the monitoring data is correct based on the determination result.

[0392] Wherein, when the processor 1402 executes the program in the memory 1401, in addition to the above functions, other functions can also be implemented. For specific details, please refer to the descriptions of the previous embodiments.

[0393] Furthermore, as Figure 20 shown, the electronic device further includes: a communication component 1403, a power supply component 1405, a display 1406, and other components. Figure 20 Only some components are schematically shown, which does not mean that the electronic device only includes Figure 20 the components shown.

[0394] An embodiment of the present application further provides another electronic device, the structure of which is similar to the above Figure 20 specifically, the electronic device includes: a memory, a processor, and a communication component. The memory is used to store a program. The processor, coupled to the memory, is configured to execute the program stored in the memory for:

[0395] Determine an order amount calculation rule according to at least one historical order;

[0396] After receiving a placing order request triggered by a user, obtain data corresponding to multiple fields included in the data to be ordered;

[0397] Using the order amount calculation rule, calculate the data corresponding to multiple fields included in the data to be ordered to obtain the order amount corresponding to the data to be ordered;

[0398] Send the order amount to the client through the communication component.

[0399] Among them, when the processor executes the program in the memory, in addition to the above functions, other functions can also be realized. For specific details, please refer to the descriptions of the previous embodiments.

[0400] Further, as Figure 20 shown, the electronic device further includes: other components such as a communication component, a power supply component, and a display. Figure 20 Only some components are schematically shown herein, which does not mean that the electronic device only includes Figure 20 the components shown.

[0401] Another embodiment of the present application further provides an electronic device, and the structure of this electronic device is the same as that of the above Figure 20 similar. Specifically, the electronic device includes: a memory and a processor. The memory is used to store programs. The processor, coupled to the memory, is used to execute the programs stored in the memory for:

[0402] Determine an order amount calculation rule according to at least one historical order;

[0403] Obtain a monitored order;

[0404] Based on the order amount calculation rule, determine whether the order amount of the monitored order is correct.

[0405] Among them, when the processor executes the program in the memory, in addition to the above functions, other functions can also be realized. For specific details, please refer to the descriptions of the previous embodiments.

[0406] Further, as Figure 20 shown, the electronic device further includes: other components such as a communication component, a power supply component, and a display. Figure 20 Only some components are schematically shown herein, which does not mean that the electronic device only includes Figure 20 the components shown.

[0407] Another embodiment of the present application further provides an electronic device, and the structure of this electronic device is the same as that of the above Figure 20 similar. Specifically, the electronic device includes: a memory, a processor, and a communication component. The memory is used to store programs. The processor, coupled to the memory, is used to execute the programs stored in the memory for:

[0408] Determine the first relationship between the data corresponding to multiple fields;

[0409] Obtain data corresponding to multiple fields associated with the monitoring object;

[0410] Determine the mapping data corresponding to the monitoring object according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship;

[0411] Send the mapping data to the client through the communication component for display on the client.

[0412] Wherein, when the processor executes the program in the memory, in addition to the above functions, other functions can also be realized. For details, refer to the descriptions of the foregoing embodiments.

[0413] Further, as Figure 20 shown, the electronic device further includes other components such as a communication component, a power supply component, and a display. Figure 20 Only some components are schematically shown in Figure 20 and it does not mean that the electronic device only includes

[0414] Another embodiment of the present application further provides an electronic device, and the structure of this electronic device is the same as that of the above Figure 20 similar. Specifically, the electronic device includes: a memory and a processor. The memory is used to store programs. The processor, coupled to the memory, is used to execute the programs stored in the memory for:

[0415] Determine the first relationship between the data corresponding to multiple fields;

[0416] Obtain the data corresponding to multiple fields associated with the monitoring object and the mapping data;

[0417] Determine whether the mapping data is correct according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship.

[0418] Wherein, when the processor executes the program in the memory, in addition to the above functions, other functions can also be realized. For details, refer to the descriptions of the foregoing embodiments.

[0419] Further, as Figure 20 shown, the electronic device further includes other components such as a communication component, a power supply component, and a display. Figure 20 Only some components are schematically shown in Figure 20 and it does not mean that the electronic device only includes

[0420] Another embodiment of the present application further provides an electronic device, and the structure of this electronic device is the same as that of the above Figure 20Similar. Specifically, the electronic device includes: a memory and a processor. The memory is used to store programs. The processor, coupled to the memory, is used to execute the programs stored in the memory for:

[0421] In response to a user's creation operation, create a monitoring task, where the monitoring task contains data requirements for the data to be monitored;

[0422] In response to a monitoring task execution instruction triggered by the user, determine the relationships between the data corresponding to multiple fields;

[0423] Obtain monitoring data that meets the data requirements;

[0424] Using the relationships, determine whether the relationships between the data corresponding to multiple fields in the monitoring data conform to the relationships, so as to determine whether the monitoring data is correct or not based on the determination result.

[0425] Wherein, when the processor executes the programs in the memory, in addition to the above functions, other functions can also be realized. For specific details, please refer to the descriptions of the previous embodiments.

[0426] Further, as Figure 20 shown, the electronic device further includes: other components such as a communication component, a power supply component, and a display. Figure 20 Only some components are schematically shown herein, which does not mean that the electronic device only includes Figure 20 the components shown.

[0427] Correspondingly, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the data processing method steps or functions provided in the above embodiments.

[0428] Correspondingly, an embodiment of the present application also provides another computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the data monitoring method steps or functions provided in the above embodiments.

[0429] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0430] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0431] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data processing method, characterized in that, Including: Determine the first relationship among the data corresponding to multiple fields; Obtain monitoring data; Determine whether the relationship among the data corresponding to multiple fields in the monitoring data conforms to the first relationship, so as to judge whether the monitoring data is correct based on the determination result; wherein, the monitoring data whose relationship among the data corresponding to multiple fields does not conform to the first relationship is incorrect data; Collect the detected incorrect data; According to the collected incorrect data, calculate a parameter reflecting the degree of incorrect influence, and send the parameter to the client device for display on the client device.

2. The method according to claim 1, wherein Determine the first relationship among the data corresponding to multiple fields, including: Obtain at least one piece of first historical data, wherein the first historical data contains the data corresponding to multiple fields; Based on the at least one piece of first historical data, determine the first relationship among the data corresponding to multiple fields.

3. The method according to claim 2, wherein Based on the at least one piece of first historical data, determine the first relationship among the data corresponding to multiple fields, including: Obtain a relationship analysis model; Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one piece of first historical data, and obtain a calculation rule reflecting the first relationship among the data corresponding to multiple fields.

4. The method according to claim 3, wherein The relationship analysis model is a machine learning model; And Obtain training samples; wherein, the training samples include: sample values corresponding to multiple fields, and sample relationships among the sample values corresponding to multiple fields; Use the sample values corresponding to multiple fields as the input of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship among the sample values corresponding to multiple fields; Based on the output result and the sample relationship, optimize the relationship analysis model.

5. The method according to claim 3 or 4, characterized in that, Also including: In response to a relationship re-determination event, obtain at least one piece of second historical data; Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one piece of second historical data, and obtain a second relationship among the data corresponding to multiple fields; When the second relationship changes compared with the first relationship, output a user-perceivable prompt that the relationship has changed.

6. The method according to claim 5, wherein Also including at least one of the following: Regularly trigger the relationship re-determination event; In response to the user's trigger, generate the relationship re-determination event.

7. The method according to any one of claims 1 to 4, characterized in that, Also including: When the determination result is that the relationship among the data corresponding to multiple fields in the monitoring data does not conform to the first relationship, judge the monitoring data as incorrect data; Send the incorrect data to the client; And / or correct the monitoring data according to the first relationship, and update the monitoring data to the corrected monitoring data.

8. The method according to any one of claims 1 to 4, characterized in that, Obtain monitoring data, including: Obtain a monitoring task created by the user, and the monitoring task contains data requirements for the data to be monitored; According to the data requirements for the data to be monitored, select the monitoring data from the received data and / or from the database.

9. A data processing method, characterized in that, Including: Use the relationship analysis model to determine an order amount calculation rule according to at least one historical order; After receiving a placing order request triggered by the user, obtain the data corresponding to multiple fields included in the data to be ordered; Using the order amount calculation rule, calculate the data corresponding to multiple fields included in the data to be ordered to obtain the order amount corresponding to the data to be ordered; Send the order amount to the client; And, the method further includes: Obtain training samples; wherein, the training samples include: sample values corresponding to multiple fields, and sample relationships between the sample values corresponding to multiple fields; Use the sample values corresponding to multiple fields as the input of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship between the sample values corresponding to multiple fields; Optimize the relationship analysis model based on the output result and the sample relationship.

10. The method according to claim 9, wherein Using a relationship analysis model to determine an order amount calculation rule according to at least one historical order includes: Obtain a relationship analysis model; Using the relationship analysis model, analyze the data corresponding to multiple fields in the at least one historical order to obtain an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in the at least one historical order.

11. The method according to claim 10, wherein Further includes: Regularly use the relationship analysis model to determine an order amount calculation rule for the relationship between the data corresponding to multiple fields in at least one historical order; When it is determined that the order amount calculation rule has changed, output a corresponding user-perceivable prompt.

12. A data processing method, characterized in that, Includes: Determine an order amount calculation rule according to at least one historical order; Obtain a monitored order; Based on the order amount calculation rule, determine whether the order amount of the monitored order is correct; wherein, the monitored order with an incorrect order amount is an error order; Collect the monitored error orders; According to the collected error orders, calculate a parameter reflecting the degree of error impact, and send the parameter to the client device for display on the client device.

13. The method according to claim 12, characterized in that, Determine an order amount calculation rule according to at least one historical order, including: Obtain a relationship analysis model; Using the relationship analysis model, analyze the data corresponding to multiple fields in the at least one historical order to obtain an order amount calculation rule reflecting the relationship between the data corresponding to multiple fields in the at least one historical order.

14. The method according to claim 13, wherein Further includes: Regularly use the relationship analysis model to determine an order amount calculation rule for the relationship between the data corresponding to multiple fields in at least one historical order; When it is determined that the order amount calculation rule has changed, output a corresponding user-perceivable prompt.

15. The method according to any one of claims 12 to 14, characterized in that Further includes: When the determination result is that the order amount of the monitored order is incorrect, determine that the monitored order is an error order; Send the error order to the client device; And / or correct the monitored order according to the order amount calculation rule, and update the monitored order to the monitored order after error correction.

16. The method according to any one of claims 12 to 14, characterized in that, Obtain a monitored order, including: Obtain a monitoring task created by the user, and the monitoring task contains the order requirements of the order to be monitored; According to the order requirements of the order to be monitored, select the monitored order from the received order data and / or from the database.

17. A data processing method, characterized in that, Includes: Using a relationship analysis model to determine a first relationship between the data corresponding to multiple fields according to at least one historical data; Obtain the data corresponding to multiple fields associated with the monitoring object; Determine the sticker data corresponding to the monitored object according to the data corresponding to the multiple fields associated with the monitored object and the first relationship; Send the sticker data to the client for display on the client; And the method further includes: Obtain training samples; wherein, the training samples include: sample values corresponding to multiple fields, and sample relationships between the sample values corresponding to the multiple fields; Use the sample values corresponding to the multiple fields as the input of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship between the sample values corresponding to the multiple fields; Optimize the relationship analysis model based on the output result and the sample relationship.

18. The method according to claim 17, wherein Using the relationship analysis model to determine the first relationship between the data corresponding to multiple fields according to at least one historical data includes: Obtain the relationship analysis model; Obtain at least one historical data; Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical data, and obtain a calculation rule reflecting the first relationship between the data corresponding to the multiple fields.

19. The method according to claim 18, wherein Further includes: Regularly use the relationship analysis model to determine the first relationship between the data corresponding to multiple fields in at least one historical data; When the determined first relationship changes, output a corresponding user-perceivable prompt.

20. The method according to any one of claims 17 to 19, characterized in that, Obtain the data corresponding to the multiple fields associated with the monitored object, including: Obtain the monitoring task created by the user, and the monitoring task contains the data requirements corresponding to the multiple fields associated with the monitored object; Select the data corresponding to the multiple fields associated with the monitored object from the received data and / or from the database according to the data requirements corresponding to the multiple fields associated with the monitored object.

21. A data processing method, characterized in that, Includes: Determine the first relationship between the data corresponding to multiple fields; Obtain the data corresponding to the multiple fields associated with the monitored object and the sticker data; According to the data corresponding to the multiple fields associated with the monitored object and the first relationship, determine whether the sticker data is correct; wherein, the incorrect sticker data is error data; Collect the monitored error data; According to the collected error data, calculate a parameter reflecting the degree of error impact, and send the parameter to the client device for display on the client device.

22. The method according to claim 21, wherein Determine the first relationship between the data corresponding to multiple fields, including: Obtain the relationship analysis model; Obtain at least one historical data; Use the relationship analysis model to analyze the data corresponding to multiple fields in the at least one historical data, and obtain a calculation rule reflecting the first relationship between the data corresponding to the multiple fields.

23. The method according to claim 22, wherein Further includes: Regularly use the relationship analysis model to determine the first relationship between the data corresponding to multiple fields in at least one historical data; When the determined first relationship changes, output a corresponding user-perceivable prompt.

24. The method according to any one of claims 21 to 23, characterized in that, Further includes: When the determination result is that the sticker data is incorrect, determine the sticker data as error data; Send the error data to the client device, and / or correct the sticker data based on the data corresponding to the multiple fields associated with the monitored object and the first relationship, and update the sticker data to the corrected sticker data.

25. A data monitoring method, characterized in that, Includes: In response to a user's creation operation, a monitoring task is created, where the monitoring task contains data requirements for the monitored data; In response to a monitoring task execution instruction triggered by the user, determine the relationships between the data corresponding to multiple fields; Obtain monitoring data that meets the data requirements; Using the relationships, determine whether the relationships between the data corresponding to multiple fields in the monitoring data conform to the relationships, so as to determine whether the monitoring data is correct based on the determination result; among them, the incorrect monitoring data is error data; Collect the monitored error data; According to the collected error data, calculate a parameter reflecting the degree of error impact, and send the parameter to the client device for display on the client device.

26. The method according to claim 25, characterized in that, The monitoring task also contains a specified relationship analysis model; And Determining the relationships between the data corresponding to multiple fields includes: Using the relationship analysis model, analyze the data corresponding to multiple fields in at least one historical data to obtain the relationships between the data corresponding to multiple fields.

27. The method according to claim 26, wherein The monitoring task also contains: the period for calling the relationship analysis model to determine the relationships between the data corresponding to multiple fields; And, The method also includes: Periodically use the relationship analysis model to determine the relationships between the data corresponding to multiple fields; When the determined relationships change, output a corresponding user-perceivable prompt.

28. A data processing system, characterized in that, Including: A client for generating monitoring data and sending the monitoring data to the server; A server for determining a first relationship between the data corresponding to multiple fields; Obtain the monitoring data; Determine whether the relationships between the data corresponding to multiple fields in the monitoring data conform to the first relationship, so as to determine whether the monitoring data is correct based on the determination result, where the monitoring data whose relationships between the data corresponding to multiple fields do not conform to the first relationship is error data; collect the monitored error data; according to the collected error data, calculate a parameter reflecting the degree of error impact, and send the parameter to the client device for display on the client device.

29. A data processing system, characterized in that, Including: A client for sending data to be ordered to the server in response to a user-triggered order placement request; A server for using a relationship analysis model to determine an order amount calculation rule according to at least one historical order; obtain the data corresponding to multiple fields included in the data to be ordered; use the order amount calculation rule to calculate the data corresponding to multiple fields included in the data to be ordered to obtain the order amount corresponding to the data to be ordered; send the order amount to the client; The client is also used to display the order amount; And the server is also used to: obtain training samples; where the training samples include: sample values corresponding to multiple fields, sample relationships between the sample values corresponding to multiple fields; use the sample values corresponding to multiple fields as the input of the relationship analysis model, execute the relationship analysis model to obtain an output result reflecting the relationships between the sample values corresponding to multiple fields; optimize the relationship analysis model based on the output result and the sample relationships.

30. A data processing system, characterized in that, Including: A client for generating a monitoring order and sending the monitoring order to the server; The server is used to determine the order amount calculation rule according to at least one historical order; obtain the monitored order; and determine whether the order amount of the monitored order is correct based on the order amount calculation rule, where the monitored order with incorrect order amount is an error order. Collect the monitored error orders; calculate a parameter reflecting the degree of error impact according to the collected error orders, and send the parameter to the client device for display on the client device.

31. A data processing system, characterized in that, It includes: The server is used to determine the first relationship between the data corresponding to multiple fields according to at least one historical data by using a relationship analysis model. Obtain the data corresponding to multiple fields associated with the monitored object. Determine the mapping data corresponding to the monitored object according to the data corresponding to the multiple fields associated with the monitored object and the first relationship. The client is used to display the mapping data corresponding to the monitored object. And the server is further used to: obtain training samples; where the training samples include: sample values corresponding to multiple fields, and sample relationships between the sample values corresponding to the multiple fields; use the sample values corresponding to the multiple fields as the input of the relationship analysis model, execute the relationship analysis model to obtain an output result reflecting the relationship between the sample values corresponding to the multiple fields; and optimize the relationship analysis model based on the output result and the sample relationship.

32. A data processing system, characterized in that, It includes: The client is used to generate mapping data corresponding to the monitored object. The server is used to determine the first relationship between the data corresponding to multiple fields. Obtain the data corresponding to multiple fields associated with the monitored object and the mapping data. Determine whether the mapping data is correct according to the data corresponding to the multiple fields associated with the monitored object and the first relationship, where the incorrect mapping data is error data. Collect the monitored error data; calculate a parameter reflecting the degree of error impact according to the collected error data, and send the parameter to the client device for display on the client device.

33. A data monitoring system, characterized in that, It includes: The client is used to create a monitoring task in response to the user's creation operation, where the monitoring task contains data requirements for the monitored data; send the monitoring task to the server; and send a start message for the monitoring task to the server in response to the monitoring task execution instruction triggered by the user. The server is used to receive and deploy the monitoring task, execute the monitoring task after receiving the start message, determine the relationship between the data corresponding to multiple fields; obtain the monitoring data that meets the data requirements; use the relationship to determine whether the relationship between the data corresponding to multiple fields in the monitoring data meets the relationship, so as to determine whether the monitoring data is correct or not based on the determination result, where the monitoring data with the relationship between the data corresponding to multiple fields not meeting the relationship is error data; collect the monitored error data; calculate a parameter reflecting the degree of error impact according to the collected error data, and send the parameter to the client device for display on the client device.

34. An electronic device, characterized in that, It includes: A memory and a processor, where The memory is used to store programs. The processor, coupled to the memory, is configured to execute the program stored in the memory for: Determine a first relationship among data corresponding to multiple fields; Obtain monitoring data; Determine whether the relationship among data corresponding to multiple fields in the monitoring data conforms to the first relationship, and determine whether the monitoring data is correct based on the determination result. Among them, the monitoring data whose relationship among data corresponding to multiple fields does not conform to the first relationship is incorrect data; Collect the detected incorrect data; According to the collected incorrect data, calculate a parameter reflecting the degree of error impact, and send the parameter to the client device for display on the client device.

35. An electronic device, characterized in that, Comprising: A memory, a processor, and a communication component, wherein, The memory is configured to store a program; The processor, coupled to the memory, is configured to execute the program stored in the memory for: Using a relationship analysis model, determine an order amount calculation rule according to at least one historical order; After receiving a placed order request triggered by a user, obtain data corresponding to multiple fields included in the data to be ordered; Using the order amount calculation rule, calculate the data corresponding to the multiple fields included in the data to be ordered to obtain the order amount corresponding to the data to be ordered; Send the order amount to the client through the communication component; And, the processor is further configured to: Obtain training samples; wherein, the training samples include: sample values corresponding to multiple fields, and sample relationships among the sample values corresponding to multiple fields; Use the sample values corresponding to multiple fields as inputs of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship among the sample values corresponding to multiple fields; Optimize the relationship analysis model based on the output result and the sample relationship.

36. An electronic device, characterized in that, Comprising: A memory and a processor, wherein, The memory is configured to store a program; The processor, coupled to the memory, is configured to execute the program stored in the memory for: Determine an order amount calculation rule according to at least one historical order; Obtain a monitored order; Based on the order amount calculation rule, determine whether the order amount of the monitored order is correct; among them, the monitored order with an incorrect order amount is an incorrect order; Collect the detected incorrect orders; According to the collected incorrect orders, calculate a parameter reflecting the degree of error impact, and send the parameter to the client device for display on the client device.

37. An electronic device, characterized in that, Comprising: A memory, a processor, and a communication component, wherein, The memory is configured to store a program; The processor, coupled to the memory, is configured to execute the program stored in the memory for: Using a relationship analysis model, determine a first relationship among data corresponding to multiple fields according to at least one historical data; Obtain data corresponding to multiple fields associated with a monitoring object; According to the data corresponding to the multiple fields associated with the monitoring object and the first relationship, determine the texture data corresponding to the monitoring object; Send the texture data to the client through the communication component for display on the client; And the processor is further configured to: Obtain training samples; wherein, the training samples include: sample values corresponding to multiple fields, and sample relationships between the sample values corresponding to the multiple fields; Use the sample values corresponding to the multiple fields as the input of the relationship analysis model, and execute the relationship analysis model to obtain an output result reflecting the relationship between the sample values corresponding to the multiple fields; Optimize the relationship analysis model based on the output result and the sample relationship.

38. An electronic device, characterized in that, Comprising: A memory and a processor, wherein, The memory is used for storing programs; The processor is coupled to the memory and is used for executing the program stored in the memory for: Determine the first relationship between the data corresponding to multiple fields; Obtain the data corresponding to multiple fields associated with the monitoring object and the mapping data; Determine whether the mapping data is correct according to the data corresponding to the multiple fields associated with the monitoring object and the first relationship; wherein, the incorrect mapping data is error data; Collect the monitored error data; Calculate a parameter reflecting the degree of error impact according to the collected error data, and send the parameter to the client device for display on the client device.

39. An electronic device, characterized in that, Comprising: A memory and a processor, wherein, The memory is used for storing programs; The processor is coupled to the memory and is used for executing the program stored in the memory for: In response to a user's creation operation, create a monitoring task, wherein the monitoring task contains data requirements for the monitored data and a specified relationship analysis model; In response to a monitoring task execution instruction triggered by the user, use the relationship analysis model to determine the relationship between the data corresponding to multiple fields; Obtain monitoring data that meets the data requirements; Use the relationship to determine whether the relationship between the data corresponding to multiple fields in the monitoring data conforms to the relationship, so as to determine whether the monitoring data is correct based on the determination result; wherein, the incorrect monitoring data is error data; Collect the monitored error data; Calculate a parameter reflecting the degree of error impact according to the collected error data, and send the parameter to the client device for display on the client device.

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