Method, device, computer equipment and storage medium for generating data prediction curve

Abstract

The present application is applicable to the field of artificial intelligence technology, and provides a method, device, computer equipment and storage medium for generating a data prediction curve, wherein the method includes: based on the target person and the target product, selecting a product to be predicted that has a corresponding relationship with the target person from the target product; based on the target person and the product to be predicted, dividing to obtain a combination unit including different target persons and the products to be predicted that have the corresponding relationship with different target persons; based on the combination unit, respectively generating a periodic data curve for each target person in combination with different products to be predicted; superimposing different periodic data curves to obtain a target data prediction curve diagram. This solution can ensure the accuracy of data prediction while making the prediction strategy explainable and persuasive.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and in particular, relates to a method, device, computer equipment and storage medium for generating a data prediction curve. Background Art

[0002] Digital operation management is an important part of modern business operations. For almost all operations, observing operation data and making predictions based on observed data are becoming increasingly important. At present, with the improvement of traditional data mining technology and the development of deep learning, there are more and more solutions based on time series prediction, such as linear regression models, serialization models in deep learning, etc.

[0003] These models are usually applied to data prediction scenarios with complex data relationships and massive data scale. These models have powerful intelligent processing methods, but usually implement black-box data prediction processing, the model application process is complicated, and the prediction strategy lacks interpretability and persuasiveness. Summary of the invention

[0004] The embodiments of the present application provide a method, device, computer equipment and storage medium for generating a data prediction curve to solve the problems in the prior art that the data prediction model is a black box data prediction processing, the model application process is complex, and the prediction strategy lacks explainability and persuasiveness.

[0005] A first aspect of an embodiment of the present application provides a method for generating a data prediction curve, comprising:

[0006] Based on the target person and the target product, selecting a product to be predicted that has a corresponding relationship with the target person from the target products;

[0007] Based on the target person and the product to be predicted, each target person and each product to be predicted that has the corresponding relationship with the target person are combined to obtain a combination unit;

[0008] Based on the combination unit, a periodic data curve is generated for each target person in combination with different products to be predicted, wherein the periodic data curve is used to indicate the trend of the result data of the target person acting on the product to be predicted;

[0009] The different periodic data curves are superimposed to obtain a target data prediction curve diagram.

[0010] Optionally, based on the combination unit, generating a periodic data curve for each target person in combination with different products to be predicted includes:

[0011] Based on the combination unit, the time period during which the target personnel included in the same combination unit acted on the product to be predicted to produce results, the result value produced by the target personnel acting on the product to be predicted, and the stage value of the target personnel currently acting on the target product are obtained;

[0012] Obtaining a waveform period of a sine curve based on the time period, obtaining an amplitude value of the sine curve based on the gain value, and obtaining an initial phase value of the sine curve based on the stage value;

[0013] Based on the waveform period, the amplitude value and the initial phase value, the sinusoidal curve is generated as the periodic data curve.

[0014] Optionally, the step of obtaining the time period during which the target personnel, included in the same combination unit, produce results on the product to be predicted, the result value produced by the target personnel on the product to be predicted, and the stage value at which the target personnel currently acts on the target product comprises:

[0015] Based on the combination unit, determining the work number of the target personnel and the product number of the product to be predicted included in the same combination unit;

[0016] Based on the work number, searching a history database for first historical achievement data matching the work number, wherein the history database records historical achievement data corresponding to different personnel working on different products;

[0017] Based on the product number, selecting second historical achievement data matching the product number from the first historical achievement data;

[0018] Based on the second historical results data, the time period during which the target person produces results on the product to be predicted, the result value produced by the target person on the product to be predicted, and the stage value at which the target person is currently working on the target product are obtained.

[0019] Optionally, before respectively generating the periodic data curves of each target person in combination with different products to be predicted based on the combination unit, the method further includes:

[0020] Obtaining the work number of the target personnel and the product number of the product to be predicted contained in each of the combination units;

[0021] Based on the work number and the product number, primary key information for identifying each of the combination units is generated.

[0022] Optionally, after superimposing the different periodic data curves to obtain a target data prediction curve graph, the method further includes:

[0023] When it is detected that there are resigned personnel among the target personnel, a target combination unit including the resigned personnel is determined, and a target periodic data curve corresponding to the target combination unit is determined;

[0024] The remaining periodic data curves after removing the target periodic data curve are superimposed to obtain the updated target data prediction curve graph.

[0025] Optionally, after superimposing the different periodic data curves to obtain a target data prediction curve graph, the method further includes:

[0026] When a new person is detected among the target persons, based on the new person and the product to be predicted, each of the new persons and each of the products to be predicted that have the corresponding relationship with the new person are combined to obtain a new combination unit;

[0027] Based on the newly added combination unit, a periodic data curve of each newly added person in a different combination with the product to be predicted is generated as a newly added periodic data curve;

[0028] The newly added periodic data curve is superimposed on the original periodic data curve used to generate the target data prediction curve graph to obtain the updated target data prediction curve graph.

[0029] Optionally, after superimposing the different periodic data curves to obtain a target data prediction curve graph, the method further includes:

[0030] Based on the target data prediction curve chart, target data prediction is performed in combination with the target personnel and the product to be predicted.

[0031] A second aspect of an embodiment of the present application provides a device for generating a data prediction curve, including:

[0032] A selection module, for selecting, based on a target person and a target product, a product to be predicted that has a corresponding relationship with the target person from among the target products;

[0033] A unit combination module, for combining each of the target persons and each of the products to be predicted that have the corresponding relationship with the target persons based on the target persons and the products to be predicted, to obtain a combination unit;

[0034] A first generating module is used to generate, based on the combination unit, a periodic data curve for each target person in combination with different products to be predicted, wherein the periodic data curve is used to indicate the trend of the result data of the target person acting on the product to be predicted;

[0035] The second generating module is used to superimpose different periodic data curves to obtain a target data prediction curve diagram.

[0036] A third aspect of an embodiment of the present application provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0037] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0038] A fifth aspect of the present application provides a computer program product, which, when executed on a terminal, enables the terminal to execute the steps of the method described in the first aspect.

[0039] As can be seen from the above, in the embodiments of the present application, an analysis and combination is performed based on the basic characteristic factors that affect the final results to form a measurement unit for result prediction, and data curves corresponding to different measurement units are introduced. Based on these data curves, a time domain curve graph for predicting the target data is formed, which ensures the accuracy of data prediction while making the prediction strategy explainable and convincing. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying any creative work.

[0041] Figure 1 This is a process of generating a data prediction curve provided by an embodiment of the present application. Figure 1 ;

[0042] Figure 2 It is a schematic diagram of superimposing a target data prediction curve based on different periodic data curves provided in an embodiment of the present application;

[0043] Figure 3 This is a process of generating a data prediction curve provided by an embodiment of the present application. Figure 2 ;

[0044] Figure 4 It is a structural diagram of a device for generating a data prediction curve provided in an embodiment of the present application;

[0045] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0047] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0048] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0049] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0050] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0051] In a specific implementation, the terminal described in the embodiments of the present application includes, but is not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and / or touch pads). It should also be understood that in some embodiments, the device is not a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., touch screen displays and / or touch pads).

[0052] In the following discussion, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse and / or joystick.

[0053] The terminal supports various applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk burning application, a spreadsheet application, a game application, a telephone application, a video conferencing application, an email application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital camcorder application, a web browsing application, a digital music player application, and / or a digital video player application.

[0054] Various applications that can be executed on the terminal can use at least one common physical user interface device such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and / or changed between applications and / or within corresponding applications. In this way, the common physical architecture of the terminal (e.g., the touch-sensitive surface) can support various applications with user interfaces that are intuitive and transparent to the user.

[0055] It should be understood that the size of the serial numbers of the steps in this embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0056] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0057] See also Figure 1 , Figure 1 This is a process of generating a data prediction curve provided by an embodiment of the present application. Figure 1 .like Figure 1 As shown, a method for generating a data prediction curve comprises the following steps:

[0058] Step 101, based on the target person and the target product, select a product to be predicted that has a corresponding relationship with the target person from the target products.

[0059] Target products include industrial products, financial products, daily necessities, etc. Target personnel include sales personnel in the sales department, workers in the production department, buyers in the procurement department, etc.

[0060] The corresponding relationship is, for example, a sales relationship, a purchasing relationship, a production relationship, etc. When the target personnel acts on the product to be predicted with the corresponding relationship, certain achievement data can be generated, such as sales performance, purchase amount, output, etc.

[0061] Specifically, when statistics are collected on target personnel and target products, the statistics can be collected on departments that currently have performance forecasting needs. For example, statistics can be collected on all sales personnel and all sales products in the entire marketing department of a company, or statistics on sales personnel and sales products collected by a domestic sales department or an overseas sales department under the marketing department. Alternatively, statistics can be collected on service personnel who generate inquiries in the customer service department and the corresponding inquiries.

[0062] Among them, when selecting the products to be predicted that have a corresponding relationship with the target personnel from the target products, it is necessary to match and screen the products that the current target personnel are mainly responsible for. Based on the classification algorithm, the products to be predicted that have a corresponding relationship with different target personnel in the target products can be found.

[0063] Take sales staff as an example. To a certain extent, each sales staff will have several products that he or she is mainly responsible for, and has his or her own set of stable and regular sales strategies when selling products.

[0064] In the actual business of the sales industry, the sales department manages different numbers of sales personnel, and sales personnel can sell different types of products. The specific personnel allocation and specific sales products will be different in different departments or between different personnel. Therefore, it is necessary to select the products that each target salesperson is responsible for selling from all sales products as the products to be predicted, so as to predict the sales data based on the products to be predicted and the target salesperson.

[0065] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, 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.

[0066] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0067] Step 102 , based on the target personnel and the products to be predicted, each target personnel and each product to be predicted that has a corresponding relationship with the target personnel are combined to obtain a combination unit.

[0068] In this step, corresponding combination units are generated based on different target personnel and their corresponding products to be predicted.

[0069] Specifically, a combination unit includes a target person and a product to be predicted that has a corresponding relationship with the target person.

[0070] In actual applications, target personnel can be responsible for different types of target products and generate corresponding results data, and different target personnel spend different amounts of energy and time in producing corresponding results for the same target product.

[0071] For example, sales staff can sell different types of products. Different types of products will have different sales performance and will also take different amounts of time. Different sales staff will spend different amounts of energy and time selling the same product. We need to predict the performance of the sales department in the next stage under this complex situation.

[0072] To give a specific example, a salesperson sold a total of 5 different products. Due to the different difficulty of selling products, there may be small products that can sell dozens of units by organizing an event, or there may be more difficult products that require a lot of time to contact customers to sell. Assuming that the salesperson works very hard and tries his best to contact customers at the same time to try to sell difficult products and organize events to sell small products. Then, from the perspective of time extension, the time spent on the 5 different products sold by the salesperson is different. In the embodiment of the present application, the salesperson + each product is formed into a combined unit, and different measurement units for generating performance can be defined for each product.

[0073] Therefore, a target person and each product to be predicted form a combined unit, so as to measure as clearly as possible the time period, efficiency and other information that different types of products bring about performance under different target personnel. On the basis of the personnel factors and product factors that affect the performance, the original characteristic factors that affect the performance are analyzed and combined to realize the performance forecast of the business department.

[0074] Step 103: Based on the combination unit, generate periodic data curves for each target person in combination with different products to be predicted.

[0075] The periodic data curve is used to indicate the trend of the target personnel's results data on the product to be predicted.

[0076] The result data of the target personnel's action on the product to be predicted include, for example, the sales data generated by the target personnel's sales of the product to be predicted, the production volume data generated by the target personnel's production of the product to be predicted, the purchase amount data generated by the target personnel's purchase of the product to be predicted, etc.

[0077] The periodic data curve can be based on the historical results data generated by the target personnel on the product to be predicted, including the time interval for generating the results data, the results value corresponding to a product to be predicted, etc., and define the moment when the performance is generated as the peak to generate a periodic data curve.

[0078] Step 104, superimposing different periodic data curves to obtain a target data prediction curve graph.

[0079] Because different products to be predicted bring different results data, and different target personnel have different cycles in producing results for a product to be predicted, we use the method of superimposing periodic data curves of corresponding unit quantities to generate the final time domain curve and obtain the target data prediction curve.

[0080] In this process, the combination of target personnel and different products to be predicted is used as a measurement unit for data prediction. In the measurement process, the spectrum analysis method can be added to generate the corresponding data trend curve based on each measurement unit, and the final target data time domain curve chart can be generated based on different data trend curves. Through the target data time domain curve chart, the trend of the entire department's performance data can be displayed and predicted, ensuring the intuitiveness of the performance trend display and the accuracy of the prediction results.

[0081] Specifically, when different periodic data curves are superimposed to obtain a target data prediction curve diagram, Fourier transform can be used to achieve it.

[0082] Fourier transform means that a function that satisfies certain conditions can be expressed as a linear combination of trigonometric functions (sine and / or cosine functions) or their integrals, such as Figure 2 As shown, different periodic data curves are superimposed to obtain a time domain curve graph, for example, a time domain function curve generated by superimposing several sinusoidal wave curves corresponding to different combination units (target personnel + product to be predicted) is used as a target data prediction curve graph.

[0083] In the embodiment of the present application, an analysis and combination is performed based on the basic characteristic factors that affect the final results to form a measurement unit for result prediction, and data curves corresponding to different measurement units are introduced. Based on these data curves, a time domain curve graph for predicting the target data is formed, which ensures the accuracy of data prediction while making the prediction strategy explainable and convincing.

[0084] Next, further optional implementation methods of the above processing are described.

[0085] In one embodiment, after superimposing different periodic data curves to obtain a target data prediction curve diagram, the method further includes:

[0086] When it is monitored that there are resigned personnel among the target personnel, the target combination unit including the resigned personnel and the target periodic data curve corresponding to the target combination unit are determined; the remaining periodic data curves after eliminating the target periodic data curve are superimposed to obtain an updated target data prediction curve chart.

[0087] Because there may be changes in personnel such as new people joining or leaving. For example, in the actual business of insurance sales, the sales department manages a different number of insurance agents, and insurance agents join and leave.

[0088] Therefore, when making data forecasts, if there are personnel resignations among the registered target personnel, it is necessary to eliminate the target periodic data curves corresponding to the resigned personnel, superimpose the remaining target periodic data curves after eliminating the target periodic data curves corresponding to the resigned personnel, and update the target data forecast curve chart.

[0089] In this process, the factor of the target personnel's employment status is introduced. When someone leaves, the sine wave corresponding to the combination unit of the resigned personnel + the product to be predicted is filtered out. For the remaining employees in office, the normal plan is used to perform periodic data curve aggregation to update the target data prediction curve chart, so as to realize the prediction of future target data.

[0090] In one embodiment, after superimposing different periodic data curves to obtain a target data prediction curve graph, the method further includes:

[0091] When new personnel are monitored among the target personnel, based on the new personnel and the products to be predicted, each new personnel and each product to be predicted that has a corresponding relationship with the new personnel are combined to obtain a new combination unit; based on the new combination unit, a periodic data curve of each new personnel in different combinations with the products to be predicted is generated as a new periodic data curve; the new periodic data curve is superimposed with the original periodic data curve used to generate the target data prediction curve diagram to obtain an updated target data prediction curve diagram.

[0092] In this process, the factor of the target personnel's employment status is introduced. When someone is newly employed, a sine wave corresponding to the combination unit of the new employee + the product to be predicted is added. For all employed personnel, the normal plan is used to perform periodic data curve aggregation to update the target data prediction curve chart, so as to realize the prediction of future target data.

[0093] In one embodiment, after superimposing different periodic data curves to obtain a target data prediction curve diagram, the method further includes:

[0094] Based on the target data prediction curve, target data prediction is performed in combination with the target personnel and the products to be predicted.

[0095] When making performance forecasts based on the target data prediction curve, since different periodic data curves are superimposed, the time axis is used as the horizontal measurement axis, and the achievement values ​​corresponding to the target personnel combined with the target products at different time points are used as the vertical measurement axis. Therefore, based on the generated target data prediction curve, it is possible to predict the future trend of the achievement data, the outbreak point of the achievement value, or the total value that will be reached in the target period, etc.

[0096] The above scheme realizes performance forecasting based on the obtained target data forecast curve chart, ensures the accuracy of the forecast results, and greatly improves the scientific rationality of the forecasting strategy.

[0097] The examples of the present application also provide different implementations of the method for generating a data prediction curve.

[0098] See also Figure 3 , Figure 3 This is a process of generating a data prediction curve provided by an embodiment of the present application. Figure 2 .like Figure 3 As shown, a method for generating a data prediction curve comprises the following steps:

[0099] Step 301: Based on the target person and the target product, a product to be predicted that has a corresponding relationship with the target person is selected from the target products.

[0100] The implementation process of this step is the same as the implementation process of step 101 in the aforementioned embodiment, and will not be repeated here.

[0101] Step 302 : Based on the target personnel and the products to be predicted, each target personnel and each product to be predicted that has a corresponding relationship with the target personnel are combined to obtain a combination unit.

[0102] The implementation process of this step is the same as the implementation process of step 102 in the aforementioned embodiment, and will not be repeated here.

[0103] Step 303, based on the combination unit, obtain the time period during which the target personnel in the same combination unit produce results on the product to be predicted, the result value produced by the target personnel on the product to be predicted, and the stage value of the target personnel currently acting on the target product.

[0104] The determination of the stage value of the target person's current action on the target product can be as follows: based on the length of time that the target person is currently acting on the target product, the length of time that has been generated is mapped to the time period for the target person to act on the product to be predicted to produce results as the waveform period and the value of the result produced by the target person's action on the product to be predicted as the waveform peak value, and based on the waveform curve, the value corresponding to the length of time that has been generated in the waveform curve is determined as the stage value of the target person's current action on the target product.

[0105] The determination of the time period during which the target personnel act on the product to be predicted to produce results can be: taking an average value based on statistical historical results data records to obtain the time period during which the target personnel act on the product to be predicted to produce results.

[0106] Among them, as an optional implementation, the acquisition of the time period during which the target personnel in the same combination unit act on the product to be predicted to produce results, the result value produced by the target personnel acting on the product to be predicted, and the stage value of the target personnel currently acting on the target product includes:

[0107] Based on the combination unit, determine the work number of the target personnel and the product number of the product to be predicted included in the same combination unit;

[0108] Based on the work number, searching a historical database for first historical achievement data matching the work number, wherein the historical achievement data corresponding to different personnel working on different products is recorded in the historical database;

[0109] Based on the product number, selecting second historical achievement data matching the product number from the first historical achievement data;

[0110] Based on the second historical achievement data, the time period during which the target personnel's action on the product to be predicted produces the results, the result value produced by the target personnel's action on the product to be predicted, and the stage value at which the target personnel is currently acting on the target product are obtained.

[0111] Each combination unit may be provided with a unique identification mark, for example, each combination unit may be provided with primary key information.

[0112] Specifically, before generating periodic data curves for each target person in combination with different products to be predicted based on the combination units, the work number of the target person and the product number of the product to be predicted contained in each combination unit are obtained; based on the work number and the product number, primary key information for identifying each combination unit is generated.

[0113] When generating primary key information for identifying each combination unit based on the work number and product number, the work number and product number can be directly concatenated to obtain the primary key information, or part of the work number and product number can be concatenated to obtain the primary key information, or the work number and product number can be calculated to generate the primary key information.

[0114] For example, if the work number is 2011321 and the product number is T123-0, the primary key information can be 2011321T123-0, that is, the primary key information is obtained by concatenating the codes of the work number and the product number, or the primary key information is obtained by concatenating a part of the values ​​of the work number and the product number, or the primary key information is obtained by concatenating the work number and the product number and performing compression operation.

[0115] After obtaining the unique identification mark of the combination unit, the unique identification mark of the combination unit is associated and stored with the work number of the target personnel included in the combination unit and the product number of the product to be predicted, and a corresponding relationship between these data is established, so that based on the combination unit, the work number of the target personnel included in the same combination unit and the product number of the product to be predicted included can be determined, and then the work number and product number are used to match the target historical achievement data corresponding to the current combination unit from the historical achievement data, and based on the target historical achievement data, the time period for the target personnel to act on the product to be predicted to produce the results, the result value produced by the target personnel's action on the product to be predicted, and the stage value of the target personnel's current action on the target product are obtained.

[0116] Step 304, obtaining the waveform period of the sine curve based on the time period, obtaining the amplitude value of the sine curve based on the gain value, and obtaining the initial phase value of the sine curve based on the phase value.

[0117] Step 305 , generating a sinusoidal curve as a periodic data curve based on the waveform period, amplitude value and initial phase value.

[0118] Specifically, the periodic data curve may be a sine curve.

[0119] A sine curve corresponds to a sine function, which is a function of the form A is the amplitude of the curve, T = 2π / ω is the waveform period of the curve, It's called phase. It is called the initial phase.

[0120] The time period during which the target personnel's action on the product to be predicted produces results is taken as the waveform period of the sine curve, the numerical value of the results produced by the target personnel's action on the product to be predicted is taken as the amplitude of the sine curve, and the stage value is taken as the initial phase value of the sine curve.

[0121] After the amplitude value, initial phase value and waveform period are obtained, the corresponding sine curve can be obtained as the periodic data curve corresponding to the current combination unit, thereby realizing the rapid fitting of the waveform curve.

[0122] The above steps 303 to 305 implement the processing process of generating the periodic data curve of each target person in combination with different products to be predicted based on the combination unit.

[0123] The periodic data curve is used to indicate the trend of the results data of the target personnel acting on the product to be predicted.

[0124] Step 306, superimposing different periodic data curves to obtain a target data prediction curve graph.

[0125] The implementation process of this step is the same as the implementation process of step 104 in the aforementioned embodiment, and will not be repeated here.

[0126] In the embodiment of the present application, an analysis and combination is performed based on the basic characteristic factors that affect the final results to form a measurement unit for the result prediction, and sinusoidal curves corresponding to different measurement units are introduced as periodic data curves. Based on these periodic data curves, a time domain curve graph for predicting the target data is formed, which ensures the accuracy of data prediction while making the prediction strategy explainable and persuasive, thereby improving the feasibility of the solution.

[0127] See also Figure 4 , Figure 4 This is a structural diagram of a data prediction curve generation device provided in an embodiment of the present application. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0128] The data prediction curve generating device 400 comprises:

[0129] A selection module 401 is used to select a product to be predicted that has a corresponding relationship with the target person from the target products based on the target person and the target product;

[0130] A unit combination module 402 is used to combine each of the target persons and each of the products to be predicted that have the corresponding relationship with the target persons based on the target persons and the products to be predicted to obtain a combination unit;

[0131] A first generating module 403 is used to generate, based on the combination unit, a periodic data curve for each target person in combination with different products to be predicted, wherein the periodic data curve is used to indicate the trend of the result data of the target person acting on the product to be predicted;

[0132] The second generating module 404 is used to superimpose different periodic data curves to obtain a target data prediction curve diagram.

[0133] The first generating module 403 is specifically used for:

[0134] Based on the combination unit, the time period during which the target personnel included in the same combination unit acted on the product to be predicted to produce results, the result value produced by the target personnel acting on the product to be predicted, and the stage value of the target personnel currently acting on the target product are obtained;

[0135] Obtaining a waveform period of a sine curve based on the time period, obtaining an amplitude value of the sine curve based on the gain value, and obtaining an initial phase value of the sine curve based on the stage value;

[0136] Based on the waveform period, the amplitude value and the initial phase value, the sinusoidal curve is generated as the periodic data curve.

[0137] The first generating module 403 is more specifically used for:

[0138] Based on the combination unit, determining the work number of the target personnel and the product number of the product to be predicted included in the same combination unit;

[0139] Based on the work number, searching a history database for first historical achievement data matching the work number, wherein the history database records historical achievement data corresponding to different personnel working on different products;

[0140] Based on the product number, selecting second historical achievement data matching the product number from the first historical achievement data;

[0141] Based on the second historical results data, the time period during which the target person produces results on the product to be predicted, the result value produced by the target person on the product to be predicted, and the stage value at which the target person is currently working on the target product are obtained.

[0142] The data prediction curve generating device 400 also includes:

[0143] A third generating module is used to obtain the work number of the target personnel and the product number of the product to be predicted contained in each of the combination units;

[0144] Based on the work number and the product number, primary key information for identifying each of the combination units is generated.

[0145] The data prediction curve generating device 400 also includes:

[0146] The first update module is used to:

[0147] When it is detected that there are resigned personnel among the target personnel, a target combination unit including the resigned personnel is determined, and a target periodic data curve corresponding to the target combination unit is determined;

[0148] The remaining periodic data curves after removing the target periodic data curve are superimposed to obtain the updated target data prediction curve graph.

[0149] The data prediction curve generating device 400 also includes:

[0150] The second update module is used to:

[0151] When a new person is detected among the target persons, based on the new person and the product to be predicted, each of the new persons and each of the products to be predicted that have the corresponding relationship with the new person are combined to obtain a new combination unit;

[0152] Based on the newly added combination unit, a periodic data curve of each newly added person in a different combination with the product to be predicted is generated as a newly added periodic data curve;

[0153] The newly added periodic data curve is superimposed on the original periodic data curve used to generate the target data prediction curve graph to obtain the updated target data prediction curve graph.

[0154] The data prediction curve generating device 400 also includes:

[0155] The data prediction module is used to perform target data prediction based on the target data prediction curve chart in combination with the target personnel and the product to be predicted.

[0156] The data prediction curve generation device provided in the embodiment of the present application can implement each process of the embodiment of the above-mentioned data prediction curve generation method and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0157] Figure 5 is a structural diagram of a computer device provided in an embodiment of the present application. As shown in the figure, the computer device 5 of this embodiment includes: at least one processor 50 ( Figure 5 Only one is shown in the figure), a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, wherein the processor 50 implements the steps of any of the above-mentioned method embodiments when executing the computer program 52.

[0158] The computer device 5 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 5 It is only an example of the computer device 5 and does not constitute a limitation of the computer device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0159] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0160] The memory 51 may be an internal storage unit of the computer device 5, such as a hard disk or memory of the computer device 5. The memory 51 may also be an external storage device of the computer device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the computer device 5. The memory 51 is used to store the computer program and other programs and data required by the computer device. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0161] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0162] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0163] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0164] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0168] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be implemented through a computer program product. When the computer program product runs on a terminal, the terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0169] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for generating a data prediction curve, characterized in that: include: Based on the target person and the target product, selecting a product to be predicted that has a corresponding relationship with the target person from the target products; Based on the target person and the product to be predicted, each target person and each product to be predicted that has the corresponding relationship with the target person are combined to obtain a combination unit; Based on the combination unit, a periodic data curve is generated for each target person in combination with different products to be predicted, and the periodic data curve is used to indicate the trend of the result data of the target person acting on the product to be predicted; the periodic data curve is generated based on the setting data, and the setting data includes the time interval for generating the result data and the result value corresponding to the product to be predicted; The different periodic data curves are superimposed to obtain a target data prediction curve diagram.

2. The method according to claim 1, characterized in that Based on the combination unit, generating periodic data curves of each target person under different combinations of the to-be-predicted product respectively includes: Based on the combination unit, the time period during which the target personnel included in the same combination unit acted on the product to be predicted to produce results, the result value produced by the target personnel acting on the product to be predicted, and the stage value of the target personnel currently acting on the target product are obtained; Obtaining a waveform period of a sine curve based on the time period, obtaining an amplitude value of the sine curve based on the result value, and obtaining an initial phase value of the sine curve based on the stage value; Based on the waveform period, the amplitude value and the initial phase value, the sinusoidal curve is generated as the periodic data curve.

3. The method according to claim 2, characterized in that The step of obtaining, based on the combination unit, a time period during which the target personnel included in the same combination unit act on the product to be predicted to produce results, a result value produced by the target personnel acting on the product to be predicted, and a stage value at which the target personnel currently acts on the target product, includes: Based on the combination unit, determining the work number of the target personnel and the product number of the product to be predicted included in the same combination unit; Based on the work number, searching a history database for first historical achievement data matching the work number, wherein the history database records historical achievement data corresponding to different personnel working on different products; Based on the product number, selecting second historical achievement data matching the product number from the first historical achievement data; Based on the second historical results data, the time period during which the target person produces results on the product to be predicted, the result value produced by the target person on the product to be predicted, and the stage value at which the target person is currently working on the target product are obtained.

4. The method according to claim 1, characterized in that: Before respectively generating the periodic data curves of each target person under the combination with different products to be predicted based on the combination unit, the method further includes: Obtaining the work number of the target personnel and the product number of the product to be predicted contained in each of the combination units; Based on the work number and the product number, primary key information for identifying each of the combination units is generated.

5. The method according to claim 1, characterized in that After the different periodic data curves are superimposed to obtain a target data prediction curve graph, the method further includes: When it is detected that there are resigned personnel among the target personnel, a target combination unit including the resigned personnel is determined, and a target periodic data curve corresponding to the target combination unit is determined; The remaining periodic data curves after removing the target periodic data curve are superimposed to obtain the updated target data prediction curve graph.

6. The method according to claim 1, characterized in that After the different periodic data curves are superimposed to obtain a target data prediction curve graph, the method further includes: When a new person is detected among the target persons, based on the new person and the product to be predicted, each of the new persons and each of the products to be predicted that have the corresponding relationship with the new person are combined to obtain a new combination unit; Based on the newly added combination unit, a periodic data curve of each newly added person in a different combination with the product to be predicted is generated as a newly added periodic data curve; The newly added periodic data curve is superimposed on the original periodic data curve used to generate the target data prediction curve graph to obtain the updated target data prediction curve graph.

7. The method according to claim 1, characterized in that After the different periodic data curves are superimposed to obtain a target data prediction curve graph, the method further includes: Based on the target data prediction curve chart, target data prediction is performed in combination with the target personnel and the product to be predicted.

8. A device for generating a data prediction curve, characterized in that: include: A selection module, for selecting, based on a target person and a target product, a product to be predicted that has a corresponding relationship with the target person from among the target products; A unit combination module, for combining each of the target persons and each of the products to be predicted that have the corresponding relationship with the target persons based on the target persons and the products to be predicted, to obtain a combination unit; A first generating module is used to generate, based on the combination unit, a periodic data curve for each target person in combination with different products to be predicted, wherein the periodic data curve is used to indicate the trend of the result data of the target person acting on the product to be predicted; the periodic data curve is generated based on setting data, wherein the setting data includes a time interval for generating the result data and a result value corresponding to the product to be predicted; The second generating module is used to superimpose different periodic data curves to obtain a target data prediction curve diagram.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images