CN115577915A - Operation data analysis method and device, storage medium and electronic device - Google Patents
Operation data analysis method and device, storage medium and electronic device Download PDFInfo
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Abstract
The application discloses an operation data analysis method, an operation data analysis device, a storage medium and an electronic device, and relates to the technical field of smart homes/smart families, wherein the method comprises the following steps: obtaining historical operation data of a plurality of product models to be analyzed; analyzing historical operation data of the plurality of product models to be analyzed based on a product operation flow to determine a plurality of candidate low-efficiency product models; analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model; and generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model. The method and the device provided by the application reduce the labor cost, reduce the analysis time of low-efficiency products and improve the analysis efficiency of the operation data.
Description
Technical Field
The application relates to the technical field of smart home/smart home, in particular to an operation data analysis method and device, a storage medium and an electronic device.
Background
Enterprises typically market and operate a number of different models of products that generate a large amount of operational data during the marketing process. For individual products, when the operation data such as sales volume, price and profit are low, the individual products become inefficient products operated by enterprises, and product improvement or market withdrawal treatment and the like are required.
The existing operation data analysis method usually depends on manual discovery or manual screening work after corresponding data are counted, and depends on the working experience of personnel, so that the labor cost is high, the time consumed for leakage of low-efficiency products is long, and the analysis efficiency of operation data is low.
Disclosure of Invention
The application provides an operation data analysis method, an operation data analysis device, a storage medium and an electronic device, which are used for solving the technical problem that the existing operation data analysis method is low in analysis efficiency.
The application provides an operation data analysis method, which comprises the following steps:
obtaining historical operation data of a plurality of product models to be analyzed;
analyzing historical operation data of the plurality of product models to be analyzed based on a product operation flow to determine a plurality of candidate inefficient product models;
analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model;
and generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
According to the operation data analysis method provided by the application, the step of analyzing historical operation data of the plurality of product models to be analyzed based on the product operation process to determine a plurality of candidate low-efficiency product models comprises the following steps:
determining a plurality of operation links and dimension analysis indexes corresponding to the operation links based on a product operation flow;
analyzing historical operation data corresponding to each product model to be analyzed in each operation link based on dimension analysis indexes corresponding to each operation link, and determining dimension analysis results of each product model to be analyzed in each operation link;
and performing funnel analysis on the dimension analysis result of each product model to be analyzed in each operation link to determine the plurality of candidate low-efficiency product models.
According to the operation data analysis method provided by the application, based on the dimension analysis indexes corresponding to the operation links, historical operation data corresponding to the product models to be analyzed in the operation links are analyzed, and dimension analysis results of the product models to be analyzed in the operation links are determined, and the method comprises the following steps:
determining a first evaluation weight of each dimension analysis index in any operation link;
determining an evaluation value of each product model to be analyzed under each dimension analysis index based on the corresponding historical operation data of each product model to be analyzed in any operation link and the dimension analysis index corresponding to any operation link;
and determining the dimension analysis result of each product model to be analyzed in any operation link based on the evaluation value of each product model to be analyzed under each dimension analysis index and the first evaluation weight of each dimension analysis index in any operation link.
According to the operation data analysis method provided by the application, the real-time operation data of each candidate low-efficiency product model in a preset time range is analyzed based on the classified operation indexes, and the low-efficiency analysis result of each candidate low-efficiency product model is determined, and the method comprises the following steps:
determining a preset time range, and the classified operation indexes in the preset time range and second evaluation weights corresponding to the classified operation indexes;
determining an evaluation value of each candidate low-efficiency product model under each classification operation index based on real-time operation data of each candidate low-efficiency product model in a preset time range and the classification operation index in the preset time range;
and determining the inefficiency analysis result of each candidate low-efficiency product model in the preset time range based on the evaluation value of each candidate low-efficiency product model under each classification operation index and the second evaluation weight corresponding to each classification operation index.
According to the operation data analysis method provided by the application, after generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model, the method comprises the following steps:
and sending the operation early warning information of each candidate low-efficiency product model to the production client corresponding to each candidate low-efficiency product model, so that the production client corresponding to each candidate low-efficiency product model adjusts the production operation plan of each candidate low-efficiency product model.
According to the operation data analysis method provided by the application, before the historical operation data of a plurality of models of products to be analyzed is acquired, the method comprises the following steps:
preprocessing historical operation data of the product models to be analyzed; the preprocessing comprises data cleaning, data integration, data transformation and data reduction.
According to the operation data analysis method provided by the application, the dimension analysis index comprises at least one of a brand dimension index, an area dimension index, a sales dimension index and a profit dimension index.
The application provides an operation data analysis device, includes:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring historical operation data of a plurality of product models to be analyzed;
the first analysis unit is used for analyzing historical operation data of the plurality of models of the products to be analyzed based on the product operation flow and determining a plurality of candidate inefficient product models;
the second analysis unit is used for analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classified operation indexes and determining a low-efficiency analysis result of each candidate low-efficiency product model;
and the early warning unit is used for generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
The present application provides a computer-readable storage medium including a stored program, wherein the program executes the operational data analysis method.
The application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the operation data analysis method through the computer program.
The operation data analysis method, the operation data analysis device, the storage medium and the electronic device obtain historical operation data of a plurality of product models to be analyzed; analyzing historical operation data of a plurality of product models to be analyzed according to a product operation flow to determine a plurality of candidate inefficient product models; analyzing real-time operation data of each candidate low-efficiency product model within a preset time range according to the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model; generating operation early warning information of each candidate low-efficiency product model according to the low-efficiency analysis result of each candidate low-efficiency product model; the automatic analysis of the operation data according to the product models is realized, the low-efficiency product models can be determined timely and accurately without depending on the working experience of personnel, the labor cost is reduced, the analysis time of the low-efficiency products is shortened, and the analysis efficiency of the operation data is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an operation data analysis method provided in the present application;
fig. 2 is a second schematic flowchart of the operation data analysis method provided in the present application;
fig. 3 is a schematic structural diagram of an operation data analysis apparatus provided in the present application;
FIG. 4 is a schematic diagram of a hardware environment of an operation data analysis method provided in the present application;
fig. 5 is a schematic structural diagram of an electronic device provided in the present application.
Reference numerals:
401: a terminal device; 402: and (4) a server.
Detailed Description
In order to make the technical solutions of the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first", "second", and the like in this application are used for distinguishing similar objects, and do not necessarily have to be used for describing a particular order or sequence. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic flowchart of an operation data analysis method provided in the present application, and as shown in fig. 1, the method includes step 110, step 120, step 130, and step 140.
Specifically, an execution subject of the operation data analysis method provided by the embodiment of the present application is an operation data analysis device. The operation data analysis device can be embodied in the form of software, such as operation data analysis software, which runs in the data server; the operation data analysis method may also be implemented by hardware devices, including a mobile terminal, a tablet computer, a desktop computer, a server, and the like.
The operation data analysis method provided by the embodiment of the application is suitable for analyzing the operation data of a plurality of different types of products operated by enterprises in the market and determining the models of inefficient products. The inefficient product models are low in indexes such as sales volume, price and profit, and therefore enterprises can obtain low-profit product models. Operation is the planning, organization, implementation and control of the operation process, and is a general term for various management works closely related to product production and service creation. From another perspective, operational management may also refer to management work for designing, operating, evaluating, and improving systems that produce and provide company-major products and services.
The product model refers to number information on a product for identifying the product. The product models of different products are different. For example, for a home manufacturing enterprise, the enterprise may operate multiple types of home devices in different regions, with different home devices including multiple product models. The household appliances comprise an intelligent refrigerator, an intelligent air conditioner, an intelligent television, an intelligent bathroom heater, an intelligent lamp, an intelligent switch and the like. The intelligent refrigerator can be subdivided into single-door type and double-door type.
The model of the product to be analyzed is the model of the product which needs to be analyzed to determine whether the model of the product is low-efficiency. The historical operation data refers to various production data, sales data, customer evaluation data and the like which are generated in the operation process of the product to be analyzed. By analyzing the historical operation data, whether the model of the product to be analyzed belongs to the low-efficiency model can be determined.
And step 120, analyzing historical operation data of a plurality of product models to be analyzed based on the product operation flow, and determining a plurality of candidate low-efficiency product models.
Specifically, the product operation flow can represent the change of indexes such as cost, profit, sales volume and the like of the model of the product to be analyzed in each link of design, production, sales, evaluation, improvement and the like. Therefore, historical operation data of a plurality of product models to be analyzed can be analyzed according to the product operation flow, and candidate low-efficiency product models can be determined in the plurality of product models to be analyzed. For example, a plurality of screening conditions may be set in the product operation flow to screen the historical operation data of each product model to be analyzed, thereby determining candidate inefficient product models.
The candidate inefficient product models may be true inefficient product models or spurious inefficient product models. For example, for new product models that are soon to appear on the market, businesses may adopt a thin and expensive strategy with the goal of quickly taking market share out of inefficient product models in order to gain more customer attention. For a new product model, only through the analysis of historical operation data generated shortly after being marketed, a wrong analysis result may be obtained.
Therefore, further analytical validation of candidate inefficient product models is required.
And step 130, analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classification operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model.
Specifically, the preset time range may be determined after the candidate inefficient product model is determined. And determining a plurality of classified operation indexes for analyzing real-time operation data generated by each candidate low-efficiency product model within the preset time range. The preset time range may be set as needed, for example, 1 month, 3 months, or 6 months.
The classified operation index can be used for analyzing operation data generated by the product model in a more subdivided field. For example, the classification operation index may be an area sales index below the total sales index, or the like.
And the low-efficiency analysis result is the result of analyzing the real-time operation data of each candidate low-efficiency product model. For example, the inefficiency analysis result may be yes or no for any candidate inefficient product model. If the result of the low-efficiency analysis of the candidate low-efficiency product model is yes, the candidate low-efficiency product model is a real low-efficiency product model; and if the result of the inefficiency analysis of the candidate inefficient product model is negative, indicating that the candidate inefficient product model is a false inefficient product model.
And 140, generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
Specifically, after determining the inefficiency analysis result of each candidate inefficient product model, operation warning information of each candidate inefficient product model may be generated. The operation early warning information is used for providing early warning information for relevant departments or personnel of the enterprise, so that the relevant departments or personnel can find and solve the problems of the inefficient product models in operation.
The operation data analysis method provided by the embodiment of the application obtains historical operation data of a plurality of models of products to be analyzed; analyzing historical operation data of a plurality of product models to be analyzed according to a product operation flow to determine a plurality of candidate inefficient product models; analyzing real-time operation data of each candidate low-efficiency product model within a preset time range according to the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model; generating operation early warning information of each candidate low-efficiency product model according to the low-efficiency analysis result of each candidate low-efficiency product model; the automatic analysis of the operation data according to the product models is realized, the low-efficiency product models can be determined timely and accurately without depending on the working experience of personnel, the labor cost is reduced, the analysis time of the low-efficiency products is shortened, and the analysis efficiency of the operation data is improved.
Based on the above embodiment, step 120 includes:
determining a plurality of operation links and dimension analysis indexes corresponding to the operation links based on a product operation flow;
analyzing historical operation data corresponding to each product model to be analyzed in each operation link based on dimension analysis indexes corresponding to each operation link, and determining dimension analysis results of each product model to be analyzed in each operation link;
and performing funnel analysis on the dimension analysis result of each product model to be analyzed in each operation link to determine a plurality of candidate low-efficiency product models.
Specifically, the funnel analysis method may be adopted to analyze historical operation data of each product model to be analyzed, and determine candidate inefficient product models. The funnel analysis method is essentially a set of process analysis method, and is mainly suitable for scenes with long processes, more links and changes along with the links.
Firstly, the product operation process can be analyzed, and a plurality of operation links and dimension analysis indexes corresponding to the operation links are determined. For example, for a plurality of product models of an intelligent refrigerator sold in the market, the product operation process can be divided into operation links such as research and development, production and manufacturing, logistics transportation, marketing, after-sales service and the like. And determining the corresponding dimension analysis index of each operation link according to the characteristics of the product model in each operation link. For example, in the production and manufacturing link, the dimension analysis index can be determined as the generation cost; in the logistics transportation link, dimension analysis indexes can be determined as transportation cost and storage cost; in the marketing link, dimension analysis indexes such as selling price, selling quantity, selling profit and the like can be determined. For each operation link, one or more dimension analysis indexes can be set.
Secondly, according to the dimension analysis indexes corresponding to the operation links, historical operation data corresponding to the product models to be analyzed in the operation links are analyzed, and dimension analysis results of the product models to be analyzed in the operation links are determined. The dimension analysis result is used for representing the analysis result of the model of the product to be analyzed under any dimension analysis index in any operation link. The dimension analysis index can be embodied in the form of a threshold value. For example, in a marketing link, a threshold value can be set for the sales profit in the dimensional analysis index, which is used to represent the expected value of the business for the model of the product to be analyzed on the sales profit. When the sales profit of the model of the product to be analyzed in the historical operation data in the marketing link is larger than the threshold value, determining that the dimension analysis result of the model of the product to be analyzed in the marketing link is qualified; when the sales profit of the model of the product to be analyzed in the historical operation data in the marketing link is smaller than the threshold value, the dimension analysis result of the model of the product to be analyzed in the marketing link can be determined to be unqualified.
And finally, performing funnel analysis on the dimension analysis result of each product model to be analyzed in each operation link to determine a plurality of candidate low-efficiency product models. According to the arrangement sequence of each operation link in the product operation process, a gradual leakage method is adopted, and products to be analyzed with unqualified dimension analysis results are screened out and determined as candidate low-efficiency product models.
According to the operation data analysis method, the funnel analysis method is adopted, historical operation data of a plurality of product models to be analyzed are analyzed according to the product operation process, a plurality of candidate low-efficiency product models are determined, and the accuracy of candidate low-efficiency product model analysis is improved.
Based on any of the above embodiments, based on the dimension analysis index corresponding to each operation link, analyzing the historical operation data corresponding to each product model to be analyzed in each operation link, and determining the dimension analysis result of each product model to be analyzed in each operation link, includes:
determining a first evaluation weight of each dimension analysis index in any operation link;
determining an evaluation value of each product model to be analyzed under each dimension analysis index based on historical operation data corresponding to each product model to be analyzed in any operation link and the dimension analysis index corresponding to any operation link;
and determining the dimension analysis result of each product model to be analyzed in any operation link based on the evaluation value of each product model to be analyzed in each dimension analysis index and the first evaluation weight of each dimension analysis index in any operation link.
Specifically, if a plurality of dimension analysis indexes exist in one operation link, the dimension analysis result of the model of the product to be analyzed in the operation link can be determined in a weighted summation mode.
First, in any operation link, a first evaluation weight of each dimension analysis index in the operation link may be determined according to the importance degree or attention degree of each dimension analysis index. For example, in a marketing link, the dimension analysis index may include 3 dimension analysis indexes, which are a sales price, a sales volume, and a sales profit. For businesses, sales profits are more important than sales volumes, which are more important than sales prices. Therefore, the selling price, the sales volume, and the sales profit can be given first evaluation weights of 20%,30%, and 50%, respectively.
And secondly, determining the evaluation value of each product model to be analyzed under each dimension analysis index according to the corresponding historical operation data of each product model to be analyzed in the operation link and the dimension analysis index corresponding to the operation link. For example, a plurality of threshold values of different degrees may be set for the sales profits, different score values are respectively given when the sales profits in the historical operation data of the model of the product to be analyzed reach or satisfy the threshold values of different degrees, and these score values are taken as the evaluation values of the model of the product to be analyzed under the dimension analysis index.
And finally, determining the dimension analysis result of each product model to be analyzed in the operation link according to the evaluation value of each product model to be analyzed under each dimension analysis index and the first evaluation weight of each dimension analysis index in the operation link. For example, in the marketing link, the evaluation values of the model of the product to be analyzed under the conditions of the sales price, the sales volume and the sales profit are 60, 80 and 90, and then the dimension analysis result of the model of the product to be analyzed in the marketing link can be determined to be 81 according to the first evaluation weights respectively corresponding to the sales price, the sales volume and the sales profit.
Based on any of the above embodiments, step 130 includes:
determining a preset time range, and classifying operation indexes in the preset time range and second evaluation weights corresponding to the classifying operation indexes;
determining an evaluation value of each candidate low-efficiency product model under each classification operation index based on real-time operation data of each candidate low-efficiency product model in a preset time range and the classification operation index in the preset time range;
and determining the inefficiency analysis result of each candidate low-efficiency product model in a preset time range based on the evaluation value of each candidate low-efficiency product model under each classification operation index and the second evaluation weight corresponding to each classification operation index.
Specifically, the real-time operation data of each candidate inefficient product model within the preset time range is analyzed according to the classification operation index, and the following method may be adopted:
first, a preset time range is determined. And determining the classified operation indexes and second evaluation weights corresponding to the classified operation indexes according to the preset time range.
Compared with the dimension analysis index, the classification operation index is a more subdivided evaluation index. For example, for sales volume, the classification operation index may be sales volume of new products, sales volume of old products, and the like; and also sales volumes in different sales regions, etc. The second evaluation weight of each of the classification operation indexes may be determined according to the importance degree or the attention degree of each of the classification operation indexes.
Secondly, determining the evaluation value of each candidate low-efficiency product model under each classification operation index according to the real-time operation data of each candidate low-efficiency product model in the preset time range and the classification operation index in the preset time range.
And finally, determining the inefficiency analysis result of each candidate low-efficiency product model within a preset time range according to the evaluation value of each candidate low-efficiency product model under each classification operation index and the second evaluation weight corresponding to each classification operation index in a weighted summation mode.
The low-efficiency analysis result is obtained by analyzing real-time operation data of each candidate low-efficiency product model in a preset time range through the classified operation indexes, which is equivalent to re-verifying the candidate low-efficiency product model through the real-time operation data in the preset time range, so that the low-efficiency product model can be more accurately identified.
Based on any of the above embodiments, step 140 then includes:
and sending the operation early warning information of each candidate low-efficiency product model to the production client corresponding to each candidate low-efficiency product model, so that the production client corresponding to each candidate low-efficiency product model adjusts the production operation plan of each candidate low-efficiency product model.
Specifically, after operation early warning information of each candidate low-efficiency product model is generated according to the low-efficiency analysis result of each candidate low-efficiency product model, the operation early warning information can be sent to the production client corresponding to each candidate low-efficiency product model, so that the prompt for relevant departments and relevant personnel in an enterprise is realized. After receiving the instructions of the relevant department or the relevant personnel, the production client corresponding to each candidate inefficient product model can adjust the production operation plan of each candidate inefficient product model.
For example, if the operation warning information of any candidate low-efficiency product model indicates a production planning staff, and the candidate low-efficiency product is a real low-efficiency product, the production planning staff may need to operate a production client to improve the production process or the production cost of the candidate low-efficiency product.
Based on any of the above embodiments, step 110 may be preceded by:
preprocessing historical operation data of a plurality of product models to be analyzed; the preprocessing includes data cleaning, data integration, data transformation and data reduction.
Specifically, the historical operation data of each model of product to be analyzed may come from different data servers, and the data formats of the data servers may be different. Therefore, the historical operation data of the product model to be analyzed can be preprocessed before the operation data analysis is carried out. The pre-processing may include data cleaning, data integration, data transformation, and data reduction.
The data cleaning comprises the steps of identifying and processing missing values of the data and removing or correcting abnormal values in the data. The missing value processing method comprises deletion, replacement, interpolation and the like. Data integration involves merging multiple data sources. The data transformation is used for transforming data according to a functional relation or a rule expression. Data reduction includes attribute reduction and numerical reduction.
By preprocessing the historical operation data, invalid data can be reduced, and the analysis efficiency of the operation data is improved.
In any of the above embodiments, the dimensional analysis index includes at least one of a brand dimensional index, an area dimensional index, a sales dimensional index, and a profit dimensional index.
Specifically, the brand dimension index is used for making inefficient judgment on the model of the product to be analyzed from the brand perspective, and may include brand awareness and the like. The regional dimension index is used for carrying out low-efficiency judgment on the model of the product to be analyzed from the point of sale region, and can comprise a sale country, a sale region and the like. The sales dimension index is used for carrying out low-efficiency judgment on the model of the product to be analyzed from the point of sales quantity, and can comprise annual sales quantity, quarterly sales quantity, monthly sales quantity and the like. The profit dimension index is used for carrying out low-efficiency judgment on the model of the product to be analyzed from the product profit angle, and can comprise gross profit, net profit and the like.
Based on any of the above embodiments, fig. 2 is a second schematic flow chart of the operation data analysis method provided in the present application, and as shown in fig. 2, the method is applied to product operation of a home appliance enterprise, and includes:
And screening, filtering and combining the data, and reserving the data which accords with the leakage logic and the effective data to participate in the calculation of the low-efficiency data model.
And filtering data of models in the aspects of sales volume and area to adapt to a real-time inefficient detection model.
And (3) performing regular algorithm analysis on the model data and judging classified low-efficiency information by using a low-efficiency data model algorithm to obtain low-efficiency type information of each low-efficiency model.
And calculating the real-time model by using the inefficient model type data and the alarm information through the real-time monitoring model to obtain accurate early warning push information of each inefficient model.
And pushing the calculated real-time low-efficiency early warning pushing information to a client through an interface so that the market, a supply chain and planning personnel can receive the prompt of the early warning information in time and actively carry out low-efficiency model operation work in time.
By the method, in a massive model data calculation project, the operation condition of each model needs to be calculated timely and accurately according to model sales information, if a low-efficiency model is not monitored in real time, a task of monitoring the low-efficiency model in real time on the whole network model is difficult to realize, and the intelligent requirement of a user on the low-efficiency model operation cannot be met. Therefore, the real-time monitoring of the inefficient model information model is constructed to enhance the initiative of the operation intellectualization of the inefficient model, and the problems of difficult discovery of the inefficient model, long screening time and the like are solved.
Based on any of the above embodiments, fig. 3 is a schematic structural diagram of an operation data analysis apparatus provided in the present application, and as shown in fig. 3, the apparatus includes:
an obtaining unit 310, configured to obtain historical operation data of a plurality of models of products to be analyzed;
a first analyzing unit 320, configured to analyze historical operation data of multiple product models to be analyzed based on a product operation flow, and determine multiple candidate inefficient product models;
the second analysis unit 330 is configured to analyze real-time operation data of each candidate inefficient product model within a preset time range based on the classification operation index, and determine an inefficient analysis result of each candidate inefficient product model;
and the early warning unit 340 is configured to generate operation early warning information for each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
The operation data analysis device provided by the embodiment of the application acquires historical operation data of a plurality of models of products to be analyzed; analyzing historical operation data of a plurality of product models to be analyzed according to the product operation flow, and determining a plurality of candidate low-efficiency product models; analyzing real-time operation data of each candidate low-efficiency product model within a preset time range according to the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model; generating operation early warning information of each candidate low-efficiency product model according to the low-efficiency analysis result of each candidate low-efficiency product model; the method and the device have the advantages that the operation data according to the product models can be automatically analyzed, the low-efficiency product models can be timely and accurately determined, the working experience of personnel is not needed, the labor cost is reduced, the analysis time of the low-efficiency products is shortened, and the analysis efficiency of the operation data is improved.
Based on any of the above embodiments, the first analysis unit is specifically configured to:
determining a plurality of operation links and dimension analysis indexes corresponding to the operation links based on a product operation flow;
analyzing historical operation data corresponding to each product model to be analyzed in each operation link based on the dimension analysis index corresponding to each operation link, and determining the dimension analysis result of each product model to be analyzed in each operation link;
and performing funnel analysis on the dimension analysis result of each product model to be analyzed in each operation link to determine a plurality of candidate low-efficiency product models.
Based on any of the above embodiments, the first analysis unit is specifically configured to:
determining a first evaluation weight of each dimension analysis index in any operation link;
determining an evaluation value of each product model to be analyzed under each dimension analysis index based on historical operation data corresponding to each product model to be analyzed in any operation link and the dimension analysis index corresponding to any operation link;
and determining the dimension analysis result of each product model to be analyzed in any operation link based on the evaluation value of each product model to be analyzed under each dimension analysis index and the first evaluation weight of each dimension analysis index in any operation link.
Based on any of the above embodiments, the second analysis unit is specifically configured to:
determining a preset time range, and classifying operation indexes in the preset time range and second evaluation weights corresponding to the classifying operation indexes;
determining an evaluation value of each candidate low-efficiency product model under each classification operation index based on real-time operation data of each candidate low-efficiency product model in a preset time range and the classification operation index in the preset time range;
and determining the inefficiency analysis result of each candidate low-efficiency product model in a preset time range based on the evaluation value of each candidate low-efficiency product model under each classification operation index and the second evaluation weight corresponding to each classification operation index.
Based on any embodiment above, still include:
and the sending unit is used for sending the operation early warning information of each candidate low-efficiency product model to the production client corresponding to each candidate low-efficiency product model so that the production client corresponding to each candidate low-efficiency product model can adjust the production operation plan of each candidate low-efficiency product model.
Based on any embodiment above, still include:
the system comprises a preprocessing unit, a data processing unit and a data processing unit, wherein the preprocessing unit is used for preprocessing historical operation data of a plurality of product models to be analyzed; the preprocessing includes data cleaning, data integration, data transformation and data reduction.
In any of the above embodiments, the dimensional analysis index includes at least one of a brand dimensional index, an area dimensional index, a sales dimensional index, and a profit dimensional index.
Based on any one of the above embodiments, the present application further provides an operation data analysis method. The operation data analysis method is widely applied to full-House intelligent digital control application scenes such as intelligent homes (Smart Home), intelligent homes, intelligent Home equipment ecology, intelligent House (Intelligent House) ecology and the like. In this embodiment, fig. 4 is a schematic diagram of a hardware environment of the operation data analysis method provided in this application, and the operation data analysis method may be applied to the hardware environment formed by the terminal device 401 and the server 402 shown in fig. 4. The server 402 is connected to the terminal device 401 through a network, and may be configured to provide services (such as application services) for the terminal or a client installed on the terminal, provide a database on the server or independent of the server, provide a data storage service for the server 402, and configure a cloud computing and/or edge computing service on the server or independent of the server, and provide a data operation service for the server 402.
The network may include, but is not limited to, at least one of: wired networks, wireless networks. The wired network may include, but is not limited to, at least one of: wide area networks, metropolitan area networks, local area networks, which may include, but are not limited to, at least one of the following: WIFI (Wireless Fidelity ), bluetooth. Terminal equipment 401 can be but not limited to be PC, the cell-phone, the panel computer, intelligent air conditioner, intelligent cigarette machine, intelligent refrigerator, intelligent oven, intelligent kitchen range, intelligent washing machine, intelligent water heater, intelligent washing equipment, intelligent dish washer, intelligent projection equipment, intelligent TV, intelligent clothes hanger, intelligent (window) curtain, intelligence audio-visual, smart jack, intelligent stereo set, intelligent audio amplifier, intelligent new trend equipment, intelligent kitchen guarding equipment, intelligent bathroom equipment, intelligence robot of sweeping the floor, intelligence robot of wiping the window, intelligence robot of mopping the ground, intelligent air purification equipment, intelligent steam ager, intelligent microwave oven, intelligent kitchen treasure, intelligent clarifier, intelligent water dispenser, intelligent lock etc..
Based on any one of the above embodiments, fig. 5 is a schematic structural diagram of an electronic device provided in the present application, and as shown in fig. 5, the electronic device may include: a Processor (Processor) 510, a communication Interface (Communications Interface) 520, a Memory (Memory) 530, and a communication Bus (Communications Bus) 540, wherein the Processor 510, the communication Interface 520, and the Memory 530 communicate with each other via the communication Bus 540. Processor 510 may call logical commands in memory 530 to perform the following method:
obtaining historical operation data of a plurality of product models to be analyzed; analyzing historical operation data of a plurality of product models to be analyzed based on a product operation flow to determine a plurality of candidate inefficient product models; analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model; and generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
In addition, the logic commands in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic commands are sold or used as independent products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including commands for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The processor in the electronic device provided in the embodiment of the present application may call a logic instruction in a memory to implement the method, and a specific implementation manner of the method is consistent with the implementation manner of the method, and may achieve the same beneficial effects, which are not described herein again.
The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the methods provided by the above embodiments.
The specific implementation manner is the same as the implementation manner of the method, and the same beneficial effects can be achieved, which is not described herein again.
Embodiments of the present application provide a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method is implemented as described above.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. An operational data analysis method, comprising:
obtaining historical operation data of a plurality of product models to be analyzed;
analyzing historical operation data of the plurality of product models to be analyzed based on a product operation flow to determine a plurality of candidate inefficient product models;
analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classified operation indexes, and determining a low-efficiency analysis result of each candidate low-efficiency product model;
and generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
2. The operational data analysis method of claim 1, wherein the analyzing historical operational data of the plurality of product models to be analyzed based on the product operational flow to determine a plurality of candidate inefficient product models comprises:
determining a plurality of operation links and dimension analysis indexes corresponding to the operation links based on a product operation flow;
analyzing historical operation data corresponding to each product model to be analyzed in each operation link based on dimension analysis indexes corresponding to each operation link, and determining dimension analysis results of each product model to be analyzed in each operation link;
and performing funnel analysis on the dimension analysis result of each product model to be analyzed in each operation link to determine the plurality of candidate low-efficiency product models.
3. The operation data analysis method according to claim 2, wherein the analyzing historical operation data corresponding to each product model to be analyzed in each operation link based on the dimension analysis index corresponding to each operation link to determine the dimension analysis result of each product model to be analyzed in each operation link includes:
determining a first evaluation weight of each dimension analysis index in any operation link;
determining an evaluation value of each product model to be analyzed under each dimension analysis index based on the corresponding historical operation data of each product model to be analyzed in any operation link and the dimension analysis index corresponding to any operation link;
and determining the dimension analysis result of each product model to be analyzed in any operation link based on the evaluation value of each product model to be analyzed under each dimension analysis index and the first evaluation weight of each dimension analysis index in any operation link.
4. The operational data analysis method of claim 1, wherein the analyzing the real-time operational data of each candidate inefficient product model within a preset time range based on the classification operation index to determine the inefficiency analysis result of each candidate inefficient product model comprises:
determining a preset time range, and the classified operation indexes in the preset time range and second evaluation weights corresponding to the classified operation indexes;
determining an evaluation value of each candidate low-efficiency product model under each classification operation index based on real-time operation data of each candidate low-efficiency product model in a preset time range and the classification operation index in the preset time range;
and determining the inefficiency analysis result of each candidate low-efficiency product model in the preset time range based on the evaluation value of each candidate low-efficiency product model under each classification operation index and the second evaluation weight corresponding to each classification operation index.
5. The operation data analysis method according to any one of claims 1 to 4, wherein after generating the operation warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model, the method comprises:
and sending the operation early warning information of each candidate low-efficiency product model to the production client corresponding to each candidate low-efficiency product model, so that the production client corresponding to each candidate low-efficiency product model adjusts the production operation plan of each candidate low-efficiency product model.
6. The operation data analysis method according to any one of claims 1 to 4, wherein before obtaining the historical operation data of a plurality of models of products to be analyzed, the method comprises:
preprocessing historical operation data of the plurality of product models to be analyzed; the preprocessing comprises data cleaning, data integration, data transformation and data reduction.
7. The operational data analysis method of claim 3, wherein the dimensional analysis index comprises at least one of a brand dimensional index, an area dimensional index, a sales dimensional index, and a profit dimensional index.
8. An operational data analysis apparatus, comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring historical operation data of a plurality of product models to be analyzed;
the first analysis unit is used for analyzing historical operation data of the plurality of models of the products to be analyzed based on the product operation flow and determining a plurality of candidate inefficient product models;
the second analysis unit is used for analyzing real-time operation data of each candidate low-efficiency product model within a preset time range based on the classified operation indexes and determining a low-efficiency analysis result of each candidate low-efficiency product model;
and the early warning unit is used for generating operation early warning information of each candidate low-efficiency product model based on the low-efficiency analysis result of each candidate low-efficiency product model.
9. A computer-readable storage medium, comprising a stored program, wherein the program is operable to perform the operational data analysis method of any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the operation data analysis method according to any one of claims 1 to 7 by the computer program.
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