Power marketing data processing method based on big data and machine learning

Abstract

The invention discloses an electricity marketing data processing method based on big data and machine learning, and belongs to the technical field of computers. Through the technical scheme provided by the embodiment of the invention, the first power marketing data, the second power marketing data, the marketing behavior data and the marketing environment data of the target power marketing behavior are acquired. And obtaining a marketing object set and an object data set corresponding to the target power marketing behavior and marketing behavior feedback data. And processing the obtained data through the first evaluation model, the second evaluation model and the third evaluation model to obtain a first marketing effect parameter, a second marketing effect parameter and a third marketing effect parameter, thereby realizing distributed processing of the power marketing data. And determining the target marketing effect parameter of the target electricity marketing behavior based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, thereby completing processing of the electricity marketing data of the target electricity marketing behavior, and the processing efficiency is relatively high.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for processing power marketing data based on big data and machine learning. Background Art

[0002] With the development of society, power companies also need to conduct power marketing activities to promote their business and expand their business scope. After conducting power marketing activities, corresponding power marketing data can be obtained. The power marketing data needs to be processed and analyzed to evaluate the effect of power marketing activities, so as to facilitate the organization of subsequent power marketing activities.

[0003] In the related art, the analysis of power marketing data is usually performed manually. When there is a large amount of power marketing data, a large number of people are required to process and analyze it, resulting in low processing efficiency of the power marketing data.

[0004] Therefore, how to improve the efficiency of processing power marketing data is a hot topic for power companies to study. Summary of the invention

[0005] The embodiment of the present application provides a method for processing power marketing data based on big data and machine learning, which can improve the efficiency of processing power marketing data. The technical solution is as follows:

[0006] In one aspect, a method for processing power marketing data based on big data and machine learning is provided, the method comprising:

[0007] Acquire first power marketing data, second power marketing data, marketing behavior data, and marketing environment data of a target power marketing behavior, and acquire an object data set of a set of marketed objects corresponding to the target power marketing behavior and marketing behavior feedback data, wherein the first power marketing data is estimated power marketing data before implementing the target power marketing behavior, and the second power marketing data is actual power marketing data after implementing the target power marketing behavior;

[0008] Determining, by a first evaluation model, a first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data;

[0009] Determining, by a second evaluation model, a second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data;

[0010] Determining, by a third evaluation model, a third marketing effect parameter of the target power marketing behavior based on the marketing behavior data, the object data set, and the marketing behavior feedback data;

[0011] Based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior, a target marketing effect parameter of the target power marketing behavior is determined.

[0012] In a possible implementation, determining the first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data includes:

[0013] Based on the first power marketing data, determining first marketing sub-data of the target power marketing behavior in multiple dimensions;

[0014] Based on the second power marketing data, determining second marketing sub-data of the target power marketing behavior in the multiple dimensions;

[0015] Based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions, a first marketing effect parameter of the target power marketing behavior is determined.

[0016] In a possible implementation manner, determining the first marketing effect parameter of the target power marketing behavior based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions includes:

[0017] Determining marketing data difference data of the target power marketing behavior in each dimension based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions;

[0018] Based on the weights of the various dimensions and the marketing data difference data of the target power marketing behavior in the various dimensions, a first marketing effect parameter of the target power marketing behavior is determined.

[0019] In a possible implementation, a method for determining the weight of each dimension includes:

[0020] For any dimension among the multiple dimensions, obtaining dimension description information of the dimension;

[0021] The weight of the dimension is determined based on the dimension description information of the dimension, the marketing environment data, and the object data set.

[0022] In a possible implementation manner, determining the second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data includes:

[0023] Based on the second power marketing data, determining second marketing sub-data of the target power marketing behavior in the multiple dimensions;

[0024] Based on the marketing environment data, determining the benchmark marketing sub-data of the multiple dimensions, wherein the benchmark marketing sub-data is the average of the marketing sub-data corresponding to the multiple power marketing behaviors;

[0025] The second marketing effect parameter is determined based on the second marketing sub-data of the target power marketing behavior in the multiple dimensions and the benchmark marketing sub-data.

[0026] In a possible implementation manner, determining the third marketing effect parameter of the target power marketing behavior based on the marketing behavior data, the object data set, and the marketing behavior feedback data includes:

[0027] Based on the marketing behavior data and the object data set, determining a matching parameter between the target marketing behavior and the marketing object set;

[0028] Determining, based on the object data set and the marketing behavior feedback data, an evaluation parameter of the marketing object set on the target marketing behavior;

[0029] Based on the matching parameters and the evaluation parameters, the third marketing effect parameter is determined.

[0030] In a possible implementation, the determining, based on the marketing behavior data and the object data set, a matching parameter between the target marketing behavior and the marketing object set includes:

[0031] Based on the marketing behavior data, determining multiple target object types corresponding to the target marketing behavior;

[0032] Based on the object data set, determining a plurality of reference object types corresponding to the marketing object set, the reference object types being object types of the marketing objects in the marketing object set;

[0033] Based on the multiple target object types and the multiple reference object types, a matching parameter between the target marketing behavior and the set of marketed objects is determined.

[0034] In a possible implementation, determining the evaluation parameters of the marketing object set for the target marketing behavior based on the object data set and the marketing behavior feedback data includes:

[0035] Determining, based on the object data set, feedback credibility of a plurality of marketing objects in the marketing object set;

[0036] Based on the feedback credibility of multiple marketing objects in the marketing object set, target feedback data is screened out from the marketing behavior feedback data, wherein the target feedback data includes sub-feedback data provided by marketing objects whose feedback credibility is greater than or equal to a credibility threshold;

[0037] Based on the target feedback data, an evaluation parameter of the set of marketing objects for the target marketing behavior is determined.

[0038] In a possible implementation, determining a target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter, and the third marketing effect parameter of the target power marketing behavior includes:

[0039] The first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior are weightedly fused to obtain the target marketing effect parameter of the target power marketing behavior.

[0040] In a possible implementation manner, after determining the target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter, and the third marketing effect parameter of the target power marketing behavior, the method further includes:

[0041] Based on the marketing behavior data, the marketing environment data and the target marketing effect parameters, a modification suggestion for the target marketing behavior is generated.

[0042] In one aspect, a power marketing data processing device based on big data and machine learning is provided, the device comprising:

[0043] an acquisition module, configured to acquire first power marketing data, second power marketing data, marketing behavior data, and marketing environment data of a target power marketing behavior, and acquire an object data set of a set of marketed objects corresponding to the target power marketing behavior and marketing behavior feedback data, wherein the first power marketing data is estimated power marketing data before the target power marketing behavior is implemented, and the second power marketing data is actual power marketing data after the target power marketing behavior is implemented;

[0044] A first marketing effect parameter determination module, configured to determine a first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data by using a first evaluation model;

[0045] a second marketing effect parameter determination module, configured to determine a second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data through a second evaluation model;

[0046] A third marketing effect parameter determination module, configured to determine a third marketing effect parameter of the target power marketing behavior based on the marketing behavior data, the object data set and the marketing behavior feedback data through a third evaluation model;

[0047] The target marketing effect parameter determination module is used to determine the target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior.

[0048] In a possible implementation, the first marketing effect parameter determination module is used to determine the first marketing sub-data of the target power marketing behavior in multiple dimensions based on the first power marketing data; determine the second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data; and determine the first marketing effect parameter of the target power marketing behavior based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions.

[0049] In a possible implementation, the first marketing effect parameter determination module is used to determine the marketing data difference data of the target power marketing behavior in each dimension based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions; and determine the first marketing effect parameter of the target power marketing behavior based on the weight of each dimension and the marketing data difference data of the target power marketing behavior in each dimension.

[0050] In a possible implementation, the device further includes a weight determination module for obtaining dimension description information of any dimension among the multiple dimensions; and determining the weight of the dimension based on the dimension description information of the dimension, the marketing environment data, and the object data set.

[0051] In a possible implementation, the second marketing effect parameter determination module is used to determine the second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data; determine the benchmark marketing sub-data of the multiple dimensions based on the marketing environment data, the benchmark marketing sub-data being the average of the marketing sub-data corresponding to the multiple power marketing behaviors; and determine the second marketing effect parameter based on the second marketing sub-data of the target power marketing behavior in the multiple dimensions and the benchmark marketing sub-data.

[0052] In one possible implementation, the third marketing effect parameter determination module is used to determine the matching parameters between the target marketing behavior and the marketed object set based on the marketing behavior data and the object data set; determine the evaluation parameters of the marketed object set for the target marketing behavior based on the object data set and the marketing behavior feedback data; and determine the third marketing effect parameter based on the matching parameters and the evaluation parameters.

[0053] In a possible implementation, the third marketing effect parameter determination module is used to determine a plurality of target object types corresponding to the target marketing behavior based on the marketing behavior data; determine a plurality of reference object types corresponding to the set of marketed objects based on the object data set, wherein the reference object type is an object type of the marketed objects in the set of marketed objects; and determine matching parameters between the target marketing behavior and the set of marketed objects based on the plurality of target object types and the plurality of reference object types.

[0054] In a possible implementation, the third marketing effect parameter determination module is used to determine, based on the object data set, feedback credibility of multiple marketed objects in the marketed object set; based on the feedback credibility of multiple marketed objects in the marketed object set, filter out target feedback data from the marketing behavior feedback data, the target feedback data including sub-feedback data provided by marketed objects whose feedback credibility is greater than or equal to a credibility threshold; based on the target feedback data, determine the evaluation parameters of the marketed object set for the target marketing behavior.

[0055] In a possible implementation, the target marketing effect parameter determination module is used to weightedly fuse the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior to obtain the target marketing effect parameter of the target power marketing behavior.

[0056] In a possible implementation, the device further includes:

[0057] The modification suggestion generating module is used to generate modification suggestions for the target marketing behavior based on the marketing behavior data, the marketing environment data and the target marketing effect parameters.

[0058] On the one hand, a computer device is provided, which includes one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the power marketing data processing method based on big data and machine learning.

[0059] On the one hand, a computer-readable storage medium is provided, in which at least one computer program is stored, and the computer program is loaded and executed by a processor to implement the power marketing data processing method based on big data and machine learning.

[0060] On the one hand, a computer program product or a computer program is provided, which includes a program code, and the program code is stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device executes the above-mentioned power marketing data processing method based on big data and machine learning.

[0061] Through the technical solution provided in the embodiment of the present application, the first power marketing data, the second power marketing data, the marketing behavior data and the marketing environment data of the target power marketing behavior are obtained. The set of marketed objects and the set of object data and the marketing behavior feedback data corresponding to the target power marketing behavior are obtained. The acquired data is processed by the first evaluation model, the second evaluation model and the third evaluation model to obtain the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, so as to realize the distributed processing of the power marketing data. Based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, the target marketing effect parameter of the target power marketing behavior is determined, and the processing of the power marketing data of the target power marketing behavior is completed, and the processing efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 It is a schematic diagram of an implementation environment of a power marketing data processing method based on big data and machine learning provided in an embodiment of the present application;

[0064] Figure 2 It is a flow chart of a method for processing power marketing data based on big data and machine learning provided in an embodiment of the present application;

[0065] Figure 3 is a flow chart of another method for processing power marketing data based on big data and machine learning provided in an embodiment of the present application;

[0066] Figure 4It is a schematic diagram of the structure of a power marketing data processing device based on big data and machine learning provided in an embodiment of the present application;

[0067] Figure 5 It is a structural diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0069] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with basically the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on quantity and execution order.

[0070] Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0071] Machine Learning (ML) is a multi-disciplinary subject that involves probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are spread across all areas of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and teaching learning.

[0072] Semantic features: features used to represent the semantics expressed by text. Different texts can correspond to the same semantic features. For example, the text "How is the weather today" and the text "How is the weather today" can correspond to the same semantic feature. Computer devices can map characters in text to character vectors, and combine and operate on character vectors based on the relationship between characters to obtain the semantic features of the text. For example, computer devices can use bidirectional encoder representations from transformers (BERT).

[0073] Normalization: Mapping sequences with different value ranges to the interval (0, 1) facilitates data processing. In some cases, the normalized values ​​can be directly implemented as probabilities.

[0074] Embedded Coding: Embedded coding mathematically represents a correspondence, that is, mapping data in X space to Y space through a function F, where the function F is an injective function, and the mapping result is structural preservation. The injective function indicates that the data after mapping is uniquely corresponding to the data before mapping, and structural preservation indicates that the size relationship of the data before mapping is the same as the size relationship of the data after mapping. For example, there are data X1 and X2 before mapping, and after mapping, Y1 corresponding to X1 and Y2 corresponding to X2 are obtained. If the data X1 before mapping is greater than X2, then correspondingly, the data Y1 after mapping is greater than Y2. For words, it means mapping the words to another space to facilitate subsequent machine learning and processing.

[0075] Attention weight: It can indicate the importance of a certain data in the training or prediction process. Importance indicates the influence of input data on output data. Data with high importance has a higher corresponding attention weight value, and data with low importance has a lower corresponding attention weight value. In different scenarios, the importance of data is different. The process of training the attention weight of the model is also the process of determining the importance of data.

[0076] Figure 1 This is a schematic diagram of the implementation environment of a power marketing data processing method based on big data and machine learning provided in an embodiment of the present application, see Figure 1 , the implementation environment may include a terminal 110 and a server 140.

[0077] The terminal 110 is connected to the server 140 via a wireless network or a wired network. Optionally, the terminal 110 is a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal 110 is installed and runs an application program that supports power marketing data processing.

[0078] The server 140 is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, distribution networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The server 140 provides background services for applications running on the terminal 110.

[0079] Figure 2 This is a flowchart of a method for processing power marketing data based on big data and machine learning provided in an embodiment of the present application, see Figure 2 Taking the execution subject as a server as an example, the method includes the following steps.

[0080] 201. The server obtains first power marketing data, second power marketing data, marketing behavior data and marketing environment data of a target power marketing behavior, and obtains an object data set of a set of marketed objects corresponding to the target power marketing behavior and marketing behavior feedback data, wherein the first power marketing data is the estimated power marketing data before implementing the target power marketing behavior, and the second power marketing data is the actual power marketing data after implementing the target power marketing behavior.

[0081] Among them, the target power marketing behavior refers to the power marketing behavior that has been implemented and is to be analyzed. The power marketing behavior refers to the marketing behavior related to power or power enterprises. For example, the promotion of power business by power enterprises belongs to power marketing behavior. Power marketing data is data representing the marketing results of power marketing behavior. The first power marketing data is the estimated power marketing data before the implementation of the target power marketing behavior, which can also represent the estimated marketing results of the target power marketing behavior; the second power marketing data is the actual power marketing data after the implementation of the target power marketing behavior, which can also represent the actual marketing results of the target power marketing behavior. The marketing behavior data is used to represent the behavior of the target power marketing behavior, that is, to reflect the situation of multiple marketing measures in the target power marketing behavior. The marketing environment data is used to represent the situation of the power marketing environment, which is generally determined based on the results of multiple power marketing behaviors. The marketing object set includes multiple marketing objects, the object data set includes object data of multiple marketing objects, and the object data is used to represent the object situation of the marketing object. The marketing behavior feedback data includes feedback data of multiple marketing objects, which is used to represent the evaluation of the marketing object on the target power marketing behavior. In some embodiments, the marketing object refers to the marketing user.

[0082] 202. The server determines a first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data through a first evaluation model.

[0083] Among them, the first marketing effect parameter is used to represent the marketing effect evaluation of the target power marketing behavior in the first aspect, and the first evaluation model is a machine learning model used to determine the first marketing effect parameter.

[0084] 203. The server determines a second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data through a second evaluation model.

[0085] Among them, the second marketing effect parameter is used to represent the marketing effect evaluation of the target power marketing behavior in the second aspect, and the second evaluation model is a machine learning model used to determine the second marketing effect parameter.

[0086] 204. The server determines a third marketing effect parameter of the target power marketing behavior based on the marketing behavior data, the object data set, and the marketing behavior feedback data through a third evaluation model.

[0087] Among them, the third marketing effect parameter is used to represent the marketing effect evaluation of the target power marketing behavior in the third aspect, and the third evaluation model is a machine learning model used to determine the third marketing effect parameter.

[0088] 205. The server determines a target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter, and the third marketing effect parameter of the target power marketing behavior.

[0089] Among them, the target marketing effect parameter is the final marketing effect parameter, which can be regarded as the processing result of the marketing data of the target power marketing behavior.

[0090] Through the technical solution provided in the embodiment of the present application, the first power marketing data, the second power marketing data, the marketing behavior data and the marketing environment data of the target power marketing behavior are obtained. The set of marketed objects and the set of object data and the marketing behavior feedback data corresponding to the target power marketing behavior are obtained. The acquired data is processed by the first evaluation model, the second evaluation model and the third evaluation model to obtain the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, so as to realize the distributed processing of the power marketing data. Based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, the target marketing effect parameter of the target power marketing behavior is determined, and the processing of the power marketing data of the target power marketing behavior is completed, and the processing efficiency is high.

[0091] The above steps 201-205 are a brief description of the technical solution provided in the embodiment of the present application. The following will combine some examples to more clearly describe the technical solution provided in the embodiment of the present application. Figure 3 Taking the execution subject as a server as an example, the method includes the following steps.

[0092] 301. The server obtains first power marketing data, second power marketing data, marketing behavior data and marketing environment data of a target power marketing behavior, and obtains an object data set of a set of marketed objects corresponding to the target power marketing behavior and marketing behavior feedback data, wherein the first power marketing data is the estimated power marketing data before implementing the target power marketing behavior, and the second power marketing data is the actual power marketing data after implementing the target power marketing behavior.

[0093] Among them, the target power marketing behavior refers to the power marketing behavior that has been implemented and is to be analyzed. The power marketing behavior refers to the marketing behavior related to power or power enterprises. For example, the promotion of power business by power enterprises belongs to power marketing behavior. The power marketing data is data representing the marketing results of the power marketing behavior. The first power marketing data is the estimated power marketing data before the implementation of the target power marketing behavior, which can also represent the estimated marketing results of the target power marketing behavior. In some embodiments, the first power marketing data is estimated by the marketing personnel. The second power marketing data is the actual power marketing data after the implementation of the target power marketing behavior, which can also represent the actual marketing results of the target power marketing behavior. The marketing behavior data is used to represent the behavior of the target power marketing behavior, that is, to reflect the situation of multiple marketing measures in the target power marketing behavior. The marketing environment data is used to represent the situation of the power marketing environment, which is generally determined based on the results of multiple power marketing behaviors. The marketing object set includes multiple marketing objects, and the object data set includes object data of multiple marketing objects. The object data is used to represent the object situation of the marketing objects. The marketing behavior feedback data includes feedback data of multiple marketing objects, which is used to represent the evaluation of the marketing objects on the target power marketing behavior. In some embodiments, the marketing target refers to the marketing user.

[0094] In a possible implementation, the server obtains first power marketing data, second power marketing data, marketing behavior data, and marketing environment data of the target power marketing behavior from the terminal. The server obtains an object data set of a marketing object set corresponding to the target power marketing behavior and marketing behavior feedback data from the terminal.

[0095] 302. The server determines a first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data through a first evaluation model.

[0096] Among them, the first marketing effect parameter is used to represent the marketing effect evaluation of the target power marketing behavior in the first aspect, and the first evaluation model is a machine learning model used to determine the first marketing effect parameter.

[0097] In a possible implementation, the server determines the first marketing sub-data of the target power marketing behavior in multiple dimensions based on the first power marketing data. The server determines the second marketing sub-data of the target power marketing behavior in multiple dimensions based on the second power marketing data. The server determines the first marketing effect parameter of the target power marketing behavior based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions.

[0098] Among them, multiple dimensions refer to multiple effect dimensions of the target marketing behavior, for example, one dimension is the marketing amount, and another dimension is the marketing income. The multiple dimensions are set by the technical personnel according to the actual situation, and the embodiments of the present application do not limit this. The first marketing sub-data of multiple dimensions include the first marketing sub-data of each dimension, that is, one dimension corresponds to one first marketing sub-data. Correspondingly, the second marketing sub-data of multiple dimensions include the second marketing sub-data of each dimension, that is, one dimension corresponds to one second marketing sub-data. The first marketing effect parameter is an evaluation of the target marketing behavior from the aspect of the difference between the estimated effect and the actual effect.

[0099] In this implementation, the first power marketing data is used to determine the first marketing sub-data in multiple dimensions, the second power marketing data is used to determine the second marketing sub-data in multiple dimensions, and the first marketing sub-data and the second marketing sub-data in multiple dimensions are used to determine the first marketing effect parameter, so as to evaluate the target marketing behavior from the perspective of estimated effect and actual effect.

[0100] In order to explain the above implementation more clearly, the above implementation will be explained in several parts below.

[0101] In the first part, the server determines first marketing sub-data of the target power marketing behavior in multiple dimensions based on the first power marketing data.

[0102] In a possible implementation, the server splits the first power marketing data using multiple dimensions to obtain first marketing sub-data of each dimension.

[0103] Part 2: The server determines second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data.

[0104] In a possible implementation, the server splits the second power marketing data using multiple dimensions to obtain second marketing sub-data of each dimension.

[0105] Part 3: The server determines a first marketing effect parameter of the target power marketing behavior based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions.

[0106] In a possible implementation, the server determines the difference of the first marketing data of the target power marketing behavior in each dimension based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions. The server determines the first marketing effect parameter of the target power marketing behavior based on the weight of each dimension and the difference of the first marketing data of the target power marketing behavior in each dimension.

[0107] For example, the server subtracts the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in each dimension to obtain the first marketing data difference of the target power marketing behavior in each dimension. The server uses the weight of each dimension to perform weighted fusion on the first marketing data difference of the target power marketing behavior in each dimension to obtain the first marketing effect parameter of the target power marketing behavior.

[0108] In order to explain the above implementation more clearly, the method for determining the weight of each dimension is explained below.

[0109] In a possible implementation, for any dimension among the multiple dimensions, the server obtains dimension description information of the dimension and determines the weight of the dimension based on the dimension description information of the dimension, the marketing environment data and the object data set.

[0110] The dimension description information is a description text that introduces the dimension.

[0111] For example, for any dimension of the multiple dimensions, the server obtains dimension description information of the dimension from the terminal. The server determines the marketing environment weight of the dimension based on the dimension description information of the dimension and the marketing environment data. The server determines the object weight of the dimension based on the dimension description information of the dimension and the object data set. The server merges the marketing environment weight of the dimension with the object weight to obtain the weight of the dimension.

[0112] The marketing environment weight is used to indicate the importance of the dimension in the marketing environment indicated by the marketing environment data, and the object weight is used to indicate the importance of the dimension in the set of marketed objects.

[0113] For example, for any dimension among the multiple dimensions, the server obtains the dimension description information of the dimension from the terminal. The server inputs the dimension description information of the dimension and the marketing environment data into a first weight determination model, processes the dimension description information of the dimension and the marketing environment data through the first weight determination model, and obtains the marketing environment weight of the dimension. The server inputs the dimension description information of the dimension and the object data set into a second weight determination model, processes the dimension description information of the dimension and the object data set through the second weight determination model, and obtains the object weight of the dimension. The server merges the marketing environment weight and the object weight of the dimension to obtain the weight of the dimension.

[0114] The first weight determination model and the second weight determination model are both obtained through training and have the ability to determine the marketing environment weight and the object weight respectively.

[0115] 303. The server determines a second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data through a second evaluation model.

[0116] Among them, the second marketing effect parameter is used to represent the marketing effect evaluation of the target power marketing behavior in the second aspect, and the second evaluation model is a machine learning model used to determine the second marketing effect parameter.

[0117] In a possible implementation, the server determines the second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data. The server determines the benchmark marketing sub-data of the multiple dimensions based on the marketing environment data, and the benchmark marketing sub-data is the average of the marketing sub-data corresponding to the multiple power marketing behaviors. The server determines the second marketing effect parameter based on the second marketing sub-data of the target power marketing behavior in the multiple dimensions and the benchmark marketing sub-data.

[0118] Among them, for the power marketing behaviors that have been implemented, the implementation time of the multiple power marketing behaviors is in the same time period as the implementation time of the target power marketing behavior.

[0119] In order to explain the above implementation more clearly, the above implementation will be explained in several parts below.

[0120] In the first part, the server determines the second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data.

[0121] In a possible implementation, the server splits the second power marketing data using multiple dimensions to obtain second marketing sub-data of each dimension.

[0122] Part 2: The server determines the benchmark marketing sub-data of the multiple dimensions based on the marketing environment data, and the benchmark marketing sub-data is the average of the marketing sub-data corresponding to the multiple power marketing behaviors.

[0123] In a possible implementation, the server splits the marketing environment data using multiple dimensions to obtain marketing sub-data corresponding to multiple power marketing behaviors in each dimension. The server determines the mean of the marketing sub-data corresponding to the multiple power marketing behaviors in each dimension as the benchmark marketing sub-data of each dimension.

[0124] Part three: The server determines the second marketing effect parameter based on the second marketing sub-data of the target power marketing behavior in the multiple dimensions and the benchmark marketing sub-data.

[0125] In a possible implementation, the server determines the difference of the second marketing data of the target power marketing behavior in each dimension based on the benchmark marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions. The server determines the first marketing effect parameter of the target power marketing behavior based on the weight of each dimension and the difference of the second marketing data of the target power marketing behavior in each dimension.

[0126] For example, the server subtracts the benchmark marketing sub-data and the second marketing sub-data of the target power marketing behavior in each dimension to obtain the difference of the second marketing data of the target power marketing behavior in each dimension. The server uses the weight of each dimension to perform weighted fusion on the difference of the second marketing data of the target power marketing behavior in each dimension to obtain the second marketing effect parameter of the target power marketing behavior.

[0127] 304. The server determines a third marketing effect parameter of the target power marketing behavior through a third evaluation model based on the marketing behavior data, the object data set, and the marketing behavior feedback data.

[0128] Among them, the third marketing effect parameter is used to represent the marketing effect evaluation of the target power marketing behavior in the third aspect, and the third evaluation model is a machine learning model used to determine the third marketing effect parameter.

[0129] In a possible implementation, the server determines the matching parameter between the target marketing behavior and the marketing object set based on the marketing behavior data and the object data set. The server determines the evaluation parameter of the marketing object set for the target marketing behavior based on the object data set and the marketing behavior feedback data. The server determines the third marketing effect parameter based on the matching parameter and the evaluation parameter.

[0130] The matching parameters include matching parameters between the target marketing behavior and each marketing object in the marketing object set.

[0131] In order to explain the above implementation more clearly, the above implementation will be explained in several parts below.

[0132] Part 1: The server determines matching parameters between the target marketing behavior and the marketing object set based on the marketing behavior data and the object data set.

[0133] In a possible implementation, the server determines multiple target object types corresponding to the target marketing behavior based on the marketing behavior data. The server determines multiple reference object types corresponding to the marketing object set based on the object data set, where the reference object type is an object type of the marketing object in the marketing object set. The server determines matching parameters between the target marketing behavior and the marketing object set based on the multiple target object types and the multiple reference object types.

[0134] The target object type is an object type that has a high degree of matching with the target power marketing behavior. The reference object type is an object type of the marketed object in the marketed object set. The matching parameter is used to reflect the matching degree. In some embodiments, the matching parameter is positively correlated with the matching degree, that is, the larger the matching parameter, the higher the matching degree; the smaller the matching parameter, the lower the matching degree.

[0135] For example, the server inputs the marketing behavior data into the object type prediction model, and extracts features from the marketing behavior data through the object type prediction model to obtain the behavior data features of the marketing behavior data. The server determines the multiple target object types based on the behavior data features through the object type prediction model. The server inputs the object data set into the object classification model, and extracts features from the object data set through the object classification model to obtain multiple object data features, where one object data feature corresponds to one marketing object. The server determines the reference object type of each marketing object based on the multiple object data features through the object classification model. The server determines the matching parameters between the target marketing behavior and the marketing object set based on the object type overlap between the multiple target object types and the multiple reference object types.

[0136] The object type prediction model is trained based on multiple sample marketing behavior data and multiple labeled object types corresponding to each sample marketing behavior data, and has the ability to predict the object type corresponding to the marketing behavior data. The object classification model is trained based on multiple sample object data and labeled object types corresponding to each sample object data, and has the ability to predict the object type based on the object data. In some embodiments, the object type prediction model and the object classification model are jointly trained using a set of labeled data.

[0137] Part 2: The server determines evaluation parameters of the marketing object set for the target marketing behavior based on the object data set and the marketing behavior feedback data.

[0138] In a possible implementation, the server determines the feedback credibility of multiple marketing objects in the marketing object set based on the object data set. The server screens out target feedback data from the marketing behavior feedback data based on the feedback credibility of multiple marketing objects in the marketing object set, and the target feedback data includes sub-feedback data provided by marketing objects whose feedback credibility is greater than or equal to a credibility threshold. The server determines the evaluation parameters of the marketing object set for the target marketing behavior based on the target feedback data.

[0139] Among them, the credibility threshold is set by technical personnel according to actual conditions, and the embodiments of the present application do not limit this.

[0140] For example, the server inputs the object data set into a credibility prediction model, and extracts features from the object data set through the credibility prediction model to obtain multiple object data features. The server performs credibility prediction based on the multiple object data features through the credibility prediction model to obtain feedback credibility of each marketing object. Based on the feedback credibility of multiple marketing objects in the marketing object set, the server determines multiple target marketing objects from multiple marketing objects, and the target marketing objects are marketing objects whose feedback credibility is greater than or equal to the credibility threshold. The server filters out target feedback data of the target marketing objects from the marketing behavior feedback data. The server inputs the target feedback data into a semantic recognition model, and extracts semantic features from the target feedback data through the semantic recognition model to obtain semantic features of the target feedback data. The server maps the semantic features of the target feedback data to obtain the evaluation parameter.

[0141] For example, the server inputs the object data set into a credibility prediction model, and extracts features from the object data set through the credibility prediction model to obtain multiple object data features. The server uses the credibility prediction model to perform full connection and normalization based on the multiple object data features to obtain the feedback credibility of each marketing object. Based on the feedback credibility of multiple marketing objects in the marketing object set, the server determines multiple target marketing objects from multiple marketing objects, and the target marketing objects are marketing objects whose feedback credibility is greater than or equal to the credibility threshold. The server filters out the target feedback data of the target marketing object from the marketing behavior feedback data. The server inputs the target feedback data into a semantic recognition model, and extracts semantic features from the target feedback data through the semantic recognition model to obtain the semantic features of the target feedback data. The server performs full connection and normalization on the semantic features of the target feedback data to obtain the evaluation parameter.

[0142] Part three: the server determines the third marketing effect parameter based on the matching parameter and the evaluation parameter.

[0143] In a possible implementation, the server merges the matching parameter and the evaluation parameter to obtain the third marketing effect parameter.

[0144] 305. The server determines a target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter, and the third marketing effect parameter of the target power marketing behavior.

[0145] Among them, the target marketing effect parameter is the final marketing effect parameter, which can be regarded as the processing result of the marketing data of the target power marketing behavior.

[0146] In a possible implementation, the server performs weighted fusion on the first marketing effect parameter, the second marketing effect parameter, and the third marketing effect parameter of the target power marketing behavior to obtain a target marketing effect parameter of the target power marketing behavior.

[0147] Among them, the weights of weighted fusion are set by technical personnel according to actual conditions, and the embodiments of the present application do not limit this.

[0148] 306. The server generates modification suggestions for the target marketing behavior based on the marketing behavior data, the marketing environment data, and the target marketing effect parameters.

[0149] In a possible implementation, the server determines marketing adjustment data based on the marketing environment data and the target marketing effect parameter, and generates the modification suggestion based on the marketing adjustment data and the marketing behavior data.

[0150] For example, the server inputs the marketing environment data and the target marketing effect parameter into the adjustment data determination model, and extracts features from the marketing environment data and the target marketing effect parameter through the adjustment data determination model to obtain marketing adjustment data prediction features. The server determines the marketing adjustment data based on the marketing adjustment data prediction features through the adjustment data determination model. The server inputs the marketing adjustment data and the marketing behavior data into the suggestion generation model, and extracts features from the marketing adjustment data and the marketing behavior data through the suggestion generation model to obtain modification suggestion determination features. The server generates the modification suggestion based on the modification suggestion determination features through the suggestion generation model.

[0151] For example, the server inputs the marketing environment data and the target marketing effect parameter into the adjustment data determination model, and encodes the marketing environment data and the target marketing effect parameter based on the attention mechanism through the adjustment data determination model to obtain the marketing adjustment data prediction feature. The server performs multiple rounds of iterative decoding on the marketing adjustment data prediction feature based on the attention mechanism through the adjustment data determination model to obtain the marketing adjustment data. The server inputs the marketing adjustment data and the marketing behavior data into the suggestion generation model, and encodes the marketing adjustment data and the marketing behavior data based on the attention mechanism through the suggestion generation model to obtain the modification suggestion determination feature. The server performs multiple rounds of iterative decoding on the modification suggestion determination feature based on the attention mechanism through the suggestion generation model to obtain the modification suggestion.

[0152] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0153] Through the technical solution provided in the embodiment of the present application, the first power marketing data, the second power marketing data, the marketing behavior data and the marketing environment data of the target power marketing behavior are obtained. The set of marketed objects and the set of object data and the marketing behavior feedback data corresponding to the target power marketing behavior are obtained. The acquired data is processed by the first evaluation model, the second evaluation model and the third evaluation model to obtain the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, so as to realize the distributed processing of the power marketing data. Based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, the target marketing effect parameter of the target power marketing behavior is determined, and the processing of the power marketing data of the target power marketing behavior is completed, and the processing efficiency is high.

[0154] Figure 4 This is a schematic diagram of the structure of a power marketing data processing device based on big data and machine learning provided in an embodiment of the present application, see Figure 4 , the device comprises:

[0155] Acquisition module 401 is used to acquire first power marketing data, second power marketing data, marketing behavior data and marketing environment data of a target power marketing behavior, and acquire an object data set of a set of marketed objects corresponding to the target power marketing behavior and marketing behavior feedback data, wherein the first power marketing data is the estimated power marketing data before implementing the target power marketing behavior, and the second power marketing data is the actual power marketing data after implementing the target power marketing behavior.

[0156] The first marketing effect parameter determination module 402 is used to determine the first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data through a first evaluation model.

[0157] The second marketing effect parameter determination module 403 is used to determine the second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data through a second evaluation model.

[0158] The third marketing effect parameter determination module 404 is used to determine the third marketing effect parameter of the target power marketing behavior based on the marketing behavior data, the object data set and the marketing behavior feedback data through a third evaluation model.

[0159] The target marketing effect parameter determination module 405 is used to determine the target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior.

[0160] In a possible implementation, the first marketing effect parameter determination module 402 is used to determine the first marketing sub-data of the target power marketing behavior in multiple dimensions based on the first power marketing data. Determine the second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data. Determine the first marketing effect parameter of the target power marketing behavior based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in the multiple dimensions.

[0161] In a possible implementation, the first marketing effect parameter determination module 402 is used to determine the marketing data difference data of the target power marketing behavior in each dimension based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions. The first marketing effect parameter of the target power marketing behavior is determined based on the weight of each dimension and the marketing data difference data of the target power marketing behavior in each dimension.

[0162] In a possible implementation, the device further includes a weight determination module, which is used to obtain dimension description information of any dimension among the multiple dimensions, and determine the weight of the dimension based on the dimension description information of the dimension, the marketing environment data, and the object data set.

[0163] In a possible implementation, the second marketing effect parameter determination module 403 is used to determine the second marketing sub-data of the target power marketing behavior in the multiple dimensions based on the second power marketing data. Based on the marketing environment data, determine the benchmark marketing sub-data of the multiple dimensions, the benchmark marketing sub-data being the average of the marketing sub-data corresponding to the multiple power marketing behaviors. Determine the second marketing effect parameter based on the second marketing sub-data of the target power marketing behavior in the multiple dimensions and the benchmark marketing sub-data.

[0164] In a possible implementation, the third marketing effect parameter determination module 404 is used to determine the matching parameters between the target marketing behavior and the marketing object set based on the marketing behavior data and the object data set. Based on the object data set and the marketing behavior feedback data, determine the evaluation parameters of the marketing object set for the target marketing behavior. Based on the matching parameters and the evaluation parameters, determine the third marketing effect parameter.

[0165] In a possible implementation, the third marketing effect parameter determination module 404 is used to determine multiple target object types corresponding to the target marketing behavior based on the marketing behavior data. Based on the object data set, multiple reference object types corresponding to the marketing object set are determined, and the reference object type is the object type of the marketing object in the marketing object set. Based on the multiple target object types and the multiple reference object types, a matching parameter between the target marketing behavior and the marketing object set is determined.

[0166] In a possible implementation, the third marketing effect parameter determination module 404 is used to determine the feedback credibility of multiple marketing objects in the marketing object set based on the object data set. Based on the feedback credibility of multiple marketing objects in the marketing object set, target feedback data is screened out from the marketing behavior feedback data, and the target feedback data includes sub-feedback data provided by marketing objects whose feedback credibility is greater than or equal to the credibility threshold. Based on the target feedback data, an evaluation parameter of the marketing object set for the target marketing behavior is determined.

[0167] In a possible implementation, the target marketing effect parameter determination module 405 is used to perform weighted fusion on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior to obtain the target marketing effect parameter of the target power marketing behavior.

[0168] In a possible implementation, the device further includes:

[0169] The modification suggestion generating module is used to generate modification suggestions for the target marketing behavior based on the marketing behavior data, the marketing environment data and the target marketing effect parameters.

[0170] It should be noted that: the power marketing data processing device based on big data and machine learning provided in the above embodiment only uses the division of the above functional modules as an example when processing power marketing data. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the power marketing data processing device based on big data and machine learning provided in the above embodiment belongs to the same concept as the power marketing data processing method embodiment based on big data and machine learning. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0171] Through the technical solution provided in the embodiment of the present application, the first power marketing data, the second power marketing data, the marketing behavior data and the marketing environment data of the target power marketing behavior are obtained. The set of marketed objects and the set of object data and the marketing behavior feedback data corresponding to the target power marketing behavior are obtained. The acquired data is processed by the first evaluation model, the second evaluation model and the third evaluation model to obtain the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, so as to realize the distributed processing of the power marketing data. Based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter, the target marketing effect parameter of the target power marketing behavior is determined, and the processing of the power marketing data of the target power marketing behavior is completed, and the processing efficiency is high.

[0172] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. The computer device 500 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 501 and one or more memories 502, wherein at least one computer program is stored in the one or more memories 502, and the at least one computer program is loaded and executed by the one or more processors 501 to implement the methods provided in the above-mentioned various method embodiments. Of course, the computer device 500 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The computer device 500 may also include other components for implementing device functions, which will not be described in detail here.

[0173] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program, and the computer program can be executed by a processor to complete the power marketing data processing method based on big data and machine learning in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory, CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.

[0174] In an exemplary embodiment, a computer program product or a computer program is also provided, which includes a program code, and the program code is stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computer device executes the above-mentioned power marketing data processing method based on big data and machine learning.

[0175] In some embodiments, the computer program involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected by a communication network. Multiple computer devices distributed at multiple locations and interconnected by a communication network may constitute a blockchain system.

[0176] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0177] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for processing power marketing data based on big data and machine learning, characterized in that: The method comprises: Acquire first power marketing data, second power marketing data, marketing behavior data, and marketing environment data of a target power marketing behavior, and acquire an object data set of a set of marketed objects corresponding to the target power marketing behavior and marketing behavior feedback data, wherein the first power marketing data is estimated power marketing data before implementing the target power marketing behavior, and the second power marketing data is actual power marketing data after implementing the target power marketing behavior; Determining, by a first evaluation model, a first marketing effect parameter of the target power marketing behavior based on the first power marketing data and the second power marketing data; Determining, by a second evaluation model, a second marketing effect parameter of the target power marketing behavior based on the second power marketing data and the marketing environment data; Determining, by a third evaluation model, a third marketing effect parameter of the target power marketing behavior based on the marketing behavior data, the object data set, and the marketing behavior feedback data; Based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior, a target marketing effect parameter of the target power marketing behavior is determined.

2. The method according to claim 1, characterized in that The determining, based on the first power marketing data and the second power marketing data, a first marketing effect parameter of the target power marketing behavior includes: Based on the first power marketing data, determining first marketing sub-data of the target power marketing behavior in multiple dimensions; Based on the second power marketing data, determining second marketing sub-data of the target power marketing behavior in the multiple dimensions; Based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions, a first marketing effect parameter of the target power marketing behavior is determined.

3. The method according to claim 2, characterized in that The determining of a first marketing effect parameter of the target power marketing behavior based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions includes: Determining marketing data difference data of the target power marketing behavior in each dimension based on the first marketing sub-data and the second marketing sub-data of the target power marketing behavior in multiple dimensions; Based on the weights of the various dimensions and the marketing data difference data of the target power marketing behavior in the various dimensions, a first marketing effect parameter of the target power marketing behavior is determined.

4. The method according to claim 3, characterized in that The methods for determining the weights of each dimension include: For any dimension among the multiple dimensions, obtaining dimension description information of the dimension; The weight of the dimension is determined based on the dimension description information of the dimension, the marketing environment data, and the object data set.

5. The method according to claim 1, characterized in that The determining, based on the second power marketing data and the marketing environment data, a second marketing effect parameter of the target power marketing behavior includes: Based on the second power marketing data, determining second marketing sub-data of the target power marketing behavior in the multiple dimensions; Based on the marketing environment data, determining the benchmark marketing sub-data of the multiple dimensions, wherein the benchmark marketing sub-data is the average of the marketing sub-data corresponding to the multiple power marketing behaviors; The second marketing effect parameter is determined based on the second marketing sub-data of the target power marketing behavior in the multiple dimensions and the benchmark marketing sub-data.

6. The method according to claim 1, characterized in that The determining, based on the marketing behavior data, the object data set, and the marketing behavior feedback data, a third marketing effect parameter of the target power marketing behavior includes: Based on the marketing behavior data and the object data set, determining a matching parameter between the target marketing behavior and the marketing object set; Determining, based on the object data set and the marketing behavior feedback data, an evaluation parameter of the marketing object set on the target marketing behavior; Based on the matching parameters and the evaluation parameters, the third marketing effect parameter is determined.

7. The method according to claim 6, characterized in that The determining, based on the marketing behavior data and the object data set, a matching parameter between the target marketing behavior and the marketing object set includes: Based on the marketing behavior data, determining multiple target object types corresponding to the target marketing behavior; Based on the object data set, determining a plurality of reference object types corresponding to the marketing object set, the reference object types being object types of the marketing objects in the marketing object set; Based on the multiple target object types and the multiple reference object types, a matching parameter between the target marketing behavior and the set of marketed objects is determined.

8. The method according to claim 6, characterized in that The step of determining the evaluation parameters of the marketing object set on the target marketing behavior based on the object data set and the marketing behavior feedback data includes: Determining, based on the object data set, feedback credibility of a plurality of marketing objects in the marketing object set; Based on the feedback credibility of multiple marketing objects in the marketing object set, target feedback data is screened out from the marketing behavior feedback data, wherein the target feedback data includes sub-feedback data provided by marketing objects whose feedback credibility is greater than or equal to a credibility threshold; Based on the target feedback data, an evaluation parameter of the marketing object set on the target marketing behavior is determined.

9. The method according to claim 1, characterized in that: The determining, based on the first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior, a target marketing effect parameter of the target power marketing behavior comprises: The first marketing effect parameter, the second marketing effect parameter and the third marketing effect parameter of the target power marketing behavior are weightedly fused to obtain the target marketing effect parameter of the target power marketing behavior.

10. The method according to claim 1, characterized in that After determining the target marketing effect parameter of the target power marketing behavior based on the first marketing effect parameter, the second marketing effect parameter, and the third marketing effect parameter of the target power marketing behavior, the method further includes: Based on the marketing behavior data, the marketing environment data and the target marketing effect parameters, a modification suggestion for the target marketing behavior is generated.

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