Information processing method and device and electronic equipment

Abstract

The invention discloses an information processing method and device and electronic equipment. The method comprises the steps that a performance query instruction sent by a target user is received, and first service information of an institution to which the target user belongs in a target time period is acquired; inputting the first service information into a weight calculation model, and calculating the weight of each service in the target time period according to the weight calculation model to obtain M weight values; obtaining the total integral of the mechanism, and distributing the total integral of the mechanism according to the M weight values to obtain M first integral values; obtaining second service information of the target user in a target time period according to the performance query instruction, calculating an integral value of the target user under each service according to the second service information and the first service information, and obtaining M second integral values; and generating performance information of the target user according to the M second integral values. According to the method and the device, the problem of low efficiency and accuracy of performance allocation through a manual calculation mode in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of big data, and more specifically, to an information processing method, device, and electronic device. Background Art

[0002] As the financial industry develops, financial institutions' business types continue to expand and innovate, resulting in a more complex and diverse range of performance indicators. Financial institutions evaluate employee performance through performance metrics, employing incentives to motivate employees and identifying performance issues to implement corrective measures.

[0003] However, since the existing performance distribution method still uses traditional manual calculation methods, and most financial institutions have a large number of employees and businesses, the efficiency and accuracy of the manually calculated performance distribution method have gradually decreased, making it difficult to meet the efficient and accurate management needs of financial institutions.

[0004] Currently, no effective solution has been proposed to address the low efficiency and accuracy of performance distribution through manual calculation in related technologies. Summary of the Invention

[0005] The main purpose of this application is to provide an information processing method, device and electronic device to solve the problem of low efficiency and accuracy of performance distribution through manual calculation in related technologies.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a method for processing information is provided. The method includes: receiving a performance query instruction sent by a target user, and obtaining first business information of the organization to which the target user belongs within a target time period, wherein the first business information includes first business data of M businesses, where M is a positive integer; inputting the first business information into a weight calculation model, and calculating the weight of each business within the target time period according to the weight calculation model to obtain M weight values; obtaining the total points of the organization, and distributing the total points of the organization according to the M weight values ​​to obtain M first point values; obtaining second business information of the target user within the target time period according to the performance query instruction, and calculating the point value of the target user under each business according to the second business information and the first business information to obtain M second point values, wherein the second business information includes second business data of the M businesses; and generating performance information of the target user according to the M second point values.

[0007] Optionally, obtaining the first business information of the organization to which the target user belongs within the target time period includes: obtaining M businesses processed by the organization within the target time period, and obtaining the first business data of each business respectively, to obtain M first business data, wherein the first business data includes: the total business volume of the M businesses, the business volume of the business corresponding to the first business data, and the target time period; and combining the M first business data into the first business information.

[0008] Optionally, the weight calculation model is trained in the following manner: obtaining historical business information of the institution in multiple historical time periods to obtain N historical business information, wherein the historical business information includes the total business volume of the institution in the historical time period, the business volume of each business, and the historical time period, and N is a positive integer; obtaining the historical weight value of each business in each historical business information to obtain multiple historical weight values; combining each historical business information with the corresponding multiple historical weight values ​​into a set of sample data to obtain multiple sets of sample data; training the convolutional neural network model with multiple sets of sample data to obtain a weight calculation model.

[0009] Optionally, the weight calculation model is a convolutional neural network model, which consists of an input layer, a shallow convolution layer, a deep convolution layer, an activation function and pooling layer, a feature fusion layer, a fully connected layer and an output layer, wherein the shallow convolution layer is used to extract shallow features in the input data, and the deep convolution layer is used to extract deep features from the shallow features.

[0010] Optionally, the total points of the organization are obtained, and the total points of the organization are distributed according to M weight values ​​to obtain M first point values, including: obtaining the weight value of each business, and multiplying the weight value of each business by the total points in turn to obtain the first point value of each business; determining the first point value of each business as M first point values.

[0011] Optionally, the point value of the target user under each business is calculated based on the second business information and the first business information to obtain M second point values, including: for the target business, obtaining the second business data under the target business from the second business information, and obtaining the first business data under the target business from the first business information; calculating the ratio of the second business data to the first business data to obtain the target ratio; obtaining the first point value of the target business, and multiplying the target ratio by the first point value to obtain the second point value under the target business.

[0012] Optionally, generating the performance information of the target user based on the M second point values ​​includes: adding the M second point values ​​to obtain the total point value of the target user; determining the point value interval in which the total point value is located to obtain the target point value interval; determining the performance level corresponding to the target point value interval according to a preset comparison table to obtain the performance level of the target user, and determining the total point value and the performance level as the performance information of the target user, wherein the preset comparison table includes multiple point value intervals and the performance level corresponding to each point value interval.

[0013] To achieve the above-mentioned purpose, according to another aspect of the present application, an information processing device is provided. The device includes: a receiving unit, configured to receive a performance query instruction sent by a target user, and obtain first business information of an organization to which the target user belongs within a target time period, wherein the first business information includes first business data of M businesses, where M is a positive integer; an input unit, configured to input the first business information into a weight calculation model, and calculate the weight of each business within the target time period according to the weight calculation model to obtain M weight values; a first acquisition unit, configured to obtain the total points of the organization, and distribute the total points of the organization according to the M weight values ​​to obtain M first point values; a second acquisition unit, configured to obtain second business information of the target user within the target time period according to the performance query instruction, and calculate the point value of the target user under each business according to the second business information and the first business information to obtain M second point values, wherein the second business information includes second business data of the M businesses; and a generation unit, configured to generate the performance information of the target user according to the M second point values.

[0014] In an embodiment of the present application, a performance query instruction sent by a target user is received, and the first business information of the organization to which the target user belongs within the target time period is obtained, wherein the first business information includes the first business data of M businesses, and M is a positive integer; the first business information is input into a weight calculation model, and the weight of each business within the target time period is calculated according to the weight calculation model to obtain M weight values; the total points of the organization are obtained, and the total points of the organization are distributed according to the M weight values ​​to obtain M first point values; the second business information of the target user within the target time period is obtained according to the performance query instruction, and the target is calculated according to the second business information and the first business information. The user's point value under each business is used to obtain M second point values, wherein the second business information includes the second business data of the M businesses; the performance information of the target user is generated according to the M second point values, by obtaining the first business information and using the weight calculation model to calculate the weight value of the business, and then performing point allocation, and obtaining the second business information, and determining the total points of the target user and the corresponding performance level through calculation, and finally obtaining the performance information of the target user, thereby solving the problem of low efficiency and accuracy of performance allocation through manual calculation, and thus achieving the technical effect of improving the efficiency and accuracy of performance allocation and shortening the performance allocation cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0016] Figure 1 A hardware structure block diagram of a computer terminal for implementing an information processing method is shown;

[0017] Figure 2 is a flowchart of a method for processing information provided in Example 1 of the present application;

[0018] Figure 3 This is a structural diagram of the weight calculation model provided in Example 1 of the present application;

[0019] Figure 4 is a schematic diagram of an information processing device according to Example 2 of the present application;

[0020] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] It should be noted that the information processing methods, devices, and electronic devices determined in the present disclosure can be used in the field of big data, and can also be used in any field other than the field of big data. The application fields of the information processing methods, devices, and electronic devices determined in the present disclosure are not limited.

[0025] It should be noted that the collected information, user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) used in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize use or refuse use. If the user chooses to refuse, the expert decision-making process will be entered. For example, an interface is set up between this system and relevant users or institutions. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or institution through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or institution.

[0026] The embodiments or examples of the present disclosure are not exhaustive, but are merely illustrations of some embodiments or examples, and are not intended to be specific limitations on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment or example can be implemented as an independent example, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be arbitrarily exchanged. In addition, the optional methods or optional examples in a certain embodiment or example can be arbitrarily combined; in addition, the various embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a certain embodiment or example can be arbitrarily combined with the optional methods or optional examples of other embodiments or examples.

[0027] For ease of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0028] CNN model: The CNN model, or Convolutional Neural Network, is a deep learning model commonly used for tasks such as image recognition and speech recognition. The core of the CNN model is the convolutional and pooling layers. Through these layers, the model extracts features from the input data, enabling efficient classification and recognition of complex data.

[0029] Example 1

[0030] According to an embodiment of the present application, an embodiment of a method for processing information is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing the information processing method is shown in FIG. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0032] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0033] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the information processing method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned information processing method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0035] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0036] Under the above operating environment, this application provides Figure 2 How the information shown is processed. Figure 2 This is a flowchart of the information processing method provided in Example 1 of the present application.

[0037] Step S201: receiving a performance query instruction sent by a target user, and obtaining first business information of the organization to which the target user belongs within a target time period, wherein the first business information includes first business data of M businesses, where M is a positive integer.

[0038] Specifically, the executing entity of this embodiment can be a performance processing system. During the process of processing performance information in this system, it will first receive a performance query instruction sent by the target user, such as an employee, etc. The performance query instruction can include corresponding query conditions, such as the target time period (one month, one quarter or one year, etc.), target user information, etc.

[0039] Furthermore, after receiving the query instruction, the organization to which the target user belongs can be determined by obtaining the user information of the target user, and the first business information of the organization within the target time period can be obtained, wherein the first business information obtained can be the business data of multiple businesses handled by the organization within the target time period. For example, the organization launched a total of 5 businesses in January, wherein the business data of business A can include the total business volume of business A, so that the first business data of each business within the target time period can be obtained from the database of the organization, and then the performance data that the target user needs to query can be calculated based on the first business data.

[0040] Step S202: input the first service information into a weight calculation model, and calculate the weight of each service in the target time period according to the weight calculation model to obtain M weight values.

[0041] Specifically, in the process of assigning weights to each business, factors such as business complexity, market position, performance contribution, and resource input can be comprehensively considered, and the weight of each business can be calculated through a weight calculation model. The weight calculation model can be a convolutional neural network model, such as a CNN model. Historical business information and its corresponding historical weight values ​​obtained by comprehensively considering various factors can be combined into multiple groups of sample data, and the convolutional neural network model can be trained until the model accuracy reaches the expected value to obtain a weight calculation model.

[0042] Furthermore, the first business data in the first business information, that is, the total business volume of M businesses, the business volume of the business corresponding to the first business data, and the target time period can be input into the weight calculation model. After the weight calculation model calculates the weights of each business in the target time period, M weight values ​​are finally obtained through the output results of the model.

[0043] Step S203: Obtain the total points of the organization, and distribute the total points of the organization according to M weight values ​​to obtain M first point values.

[0044] Specifically, the total points of the organization can be a fixed preset value, such as 100 points. After the system obtains the total points of the organization, it distributes the total points to each business by multiplying the weight value of each business output by the weight calculation model with the total points of the organization to obtain the first point value.

[0045] For example, the business types of an institution are A business, B business and C business, and the weight values ​​of the three businesses are 60%, 10% and 30% respectively. The total score of the institution is 100 points. Multiply the weight values ​​of the three businesses by the total score respectively, and the first score values ​​of A business, B business and C business are 60 points, 10 points and 30 points respectively.

[0046] Step S204: Obtain the second business information of the target user within the target time period according to the performance query instruction, and calculate the point value of the target user under each business according to the second business information and the first business information to obtain M second point values, wherein the second business information includes the second business data of M businesses.

[0047] Specifically, by accessing the module that stores and manages the business information of employees in the corresponding organization in the system, the business information in the system can be screened and filtered according to the query condition information such as the target time period and target user information contained in the performance query instruction, and the second business information of the target user within the target time period can be obtained, including the second business data of M businesses, wherein the second business data may include the corresponding business volume of the target user under each business within the target time period.

[0048] Furthermore, after obtaining the second business data, the business volume of the business corresponding to the first business data can be obtained from the first business information, and the ratio of the business volume of each business of the target user corresponding to the second business data in the target time period to the business volume of the business corresponding to the first business data can be calculated. The obtained ratio can be multiplied by the first integral value of each of the aforementioned businesses to obtain the second integral value of each business.

[0049] Step S205: Generate performance information of the target user according to the M second integral values.

[0050] Specifically, by adding the second point values ​​of the aforementioned businesses, the total point value of the target user can be obtained, and then the point value range in which the total point value is located can be determined. Furthermore, based on the point value range of the target user in the target time period, the performance level of the target user can be determined. The correspondence between the point value range and the performance level can be preset. For example, 1-3 points can be preset as level A, 4-6 points as level B, 7-9 points as level C..., and finally the total point value and performance level of the target user in the target time period are combined to determine the performance information of the target user.

[0051] The information processing method provided by the embodiment of the present application adopts the method of receiving a performance query instruction sent by a target user, and obtaining the first business information of the organization to which the target user belongs within a target time period, wherein the first business information includes the first business data of M businesses, and M is a positive integer; inputting the first business information into a weight calculation model, and calculating the weight of each business within the target time period according to the weight calculation model to obtain M weight values; obtaining the total points of the organization, and distributing the total points of the organization according to the M weight values ​​to obtain M first point values; obtaining the second business information of the target user within the target time period according to the performance query instruction, and distributing the second business information according to the second business information and the first business information The point value of the target user under each business is calculated to obtain M second point values, wherein the second business information includes the second business data of the M businesses; the performance information of the target user is generated according to the M second point values, by obtaining the first business information and using the weight calculation model to calculate the weight value of the business, and then performing point allocation, and obtaining the second business information, and determining the total points of the target user and the corresponding performance level through calculation, and finally obtaining the performance information of the target user, which solves the problem of low efficiency and accuracy of performance allocation through manual calculation, and thus achieves the technical effect of improving the efficiency and accuracy of performance allocation and shortening the performance allocation cycle.

[0052] Optionally, in the information processing method provided in the embodiment of the present application, obtaining the first business information of the organization to which the target user belongs within the target time period includes: obtaining M businesses processed by the organization within the target time period, and obtaining the first business data of each business respectively, to obtain M first business data, wherein the first business data includes: the total business volume of the M businesses, the business volume of the business corresponding to the first business data, and the target time period; and combining the M first business data into the first business information.

[0053] It should be noted that the first business information of the institution to which the target user belongs within the target time period can be obtained by accessing the module that stores and manages the business information of the institution in the system, that is, the various business types handled by the institution, and the first business data corresponding to each business. The target time period can be one month, one quarter or one year, etc. The first business data can include the total business volume of each business, the business volume of the business corresponding to the first business data (that is, the business volume of each business within the target time period), and the target time period.

[0054] For example, the business information of an organization may be shown in the following table:

[0055] Table 1

[0056]

[0057] When the target time period is the second quarter, the business types obtained are A, B, and C. The total business volume of the three businesses in the first business data is the total business volume of the second quarter, which is 170,000. The business volume of the business corresponding to the first business data is 50,000 for business A, 40,000 for business B, and 80,000 for business C.

[0058] This embodiment obtains the first business information of the organization to which the target user belongs within the target time period, and thus can assign weights to various businesses according to the first business information, thereby ensuring the accuracy of the weight assignment.

[0059] Optionally, in the information processing method provided in the embodiment of the present application, the weight calculation model is trained in the following manner: obtaining historical business information of the organization in multiple historical time periods to obtain N historical business information, wherein the historical business information includes the total business volume of the organization in the historical time period, the business volume of each business, and the historical time period, and N is a positive integer; obtaining the historical weight value of each business in each historical business information to obtain multiple historical weight values; combining each historical business information with the corresponding multiple historical weight values ​​into a set of sample data to obtain multiple sets of sample data; training the convolutional neural network model through multiple sets of sample data to obtain a weight calculation model.

[0060] It should be noted that the weight calculation model can be obtained by training a convolutional neural network model using historical data, wherein the convolutional neural network model can be a two-layer CNN model. In the process of training the convolutional neural network model, the historical business information of the institution in multiple historical time periods can be obtained by first accessing the module that stores and manages the business information of the institution in the system. The content type of the historical business information corresponds to the content type of the aforementioned first business information, that is, it includes the total business volume of the institution in the historical time period, the business volume of each business, and the historical time period.

[0061] Furthermore, the historical weight value of each business corresponding to each historical business information can be obtained in the module for storing and managing the weight value of the organization's business information in the system, and then the historical business information is combined with the corresponding historical weight value to obtain multiple groups of sample data. By inputting the sample data into the convolutional neural network model for learning and training, when the prediction accuracy of the model reaches the expected value, a weight calculation model can be obtained, wherein the historical weight value can be assigned by comprehensively considering multiple factors such as business complexity, market position, performance contribution, resource input, etc. Therefore, in the process of calculating the weight value corresponding to the first business information using the trained weight calculation model, the model can comprehensively assign multiple factors and finally output the calculation result of the weight value.

[0062] It should be noted that when inputting the business volume of each business, the characteristic information of each business can also be associated with the business volume and input into the model, so that the model can further analyze the weight value of each business based on the characteristic information of the business, thereby improving the model's prediction accuracy of the weight value.

[0063] This embodiment improves the accuracy of weight calculation model in calculating weight values ​​by training the neural network model with multiple sets of historical data, thereby ensuring the accuracy of points distribution using weight values.

[0064] Optionally, in the information processing method provided in the embodiment of the present application, the weight calculation model is a convolutional neural network model, and the weight calculation model consists of an input layer, a shallow convolution layer, a deep convolution layer, an activation function and pooling layer, a feature fusion layer, a fully connected layer and an output layer, wherein the shallow convolution layer is used to extract shallow features in the input data, and the deep convolution layer is used to extract deep features from the shallow features.

[0065] It should be noted that Figure 3 This is a structural diagram of the weight calculation model provided in Example 1 of the present application, such as Figure 3 As shown in the figure, the structure of the weight calculation model (i.e., the two-layer CNN model) is as follows:

[0066] Input layer: As the starting point of the two-layer CNN model, the input layer is responsible for receiving raw image data. Data preprocessing (such as normalization and resizing) can be performed at this layer to ensure that the data is suitable for network learning.

[0067] Shallow convolution layer: Located after the input layer, the shallow convolution layer uses smaller convolution kernels to extract basic features in the image, such as edges, lines, etc. This layer usually contains fewer convolution kernels, and its output serves as the input of subsequent layers.

[0068] Deep convolutional layer: Located after the shallow convolutional layer, the deep convolutional layer uses larger convolution kernels or more convolution operations to extract more complex features. These features usually include higher-level image information such as shape and texture.

[0069] Activation function layer: Located after the deep convolutional layer, activation functions (such as ReLU, Sigmoid, or Softmax) are introduced to increase the nonlinearity of the network. Activation functions help the network learn more complex feature representations and enhance the expressive power of the model.

[0070] Pooling layer: Located after the activation function layer, it is used to downsample the feature map to reduce the spatial dimension and number of parameters of the data. Common pooling operations include maximum pooling and average pooling, which help extract key features and reduce the amount of computation.

[0071] Feature fusion layer: Located after the pooling layer, it is used to fuse the outputs of different convolutional layers or pooling layers. Feature fusion helps combine feature information from different levels, improving the generalization ability and accuracy of the model.

[0072] Fully connected layer: Located after the feature fusion layer, it is responsible for integrating and classifying the features extracted by the previous layers, thereby further extracting features and preparing the final output.

[0073] Output layer: As the endpoint of the two-layer CNN model, the output layer is responsible for outputting the final prediction results. For classification tasks, the output layer typically uses a softmax function to convert the output of the fully connected layer into a probability distribution. For regression tasks, linear regression or other methods can be used to convert the output into continuous values, thereby obtaining more accurate output results.

[0074] The weight calculation model of this embodiment realizes the complete process from raw data to final prediction output through the mutual collaboration of input layer, shallow convolution layer, deep convolution layer, activation function, pooling layer, feature fusion layer, fully connected layer and output layer, ensuring the accuracy of the eigenvalue calculation results.

[0075] Optionally, in the information processing method provided in the embodiment of the present application, the total points of the organization are obtained, and the total points of the organization are distributed according to M weight values ​​to obtain M first point values, including: obtaining the weight value of each business, and multiplying the weight value of each business by the total points in turn to obtain the first point value of each business; determining the first point value of each business as M first point values.

[0076] It should be noted that by inputting the aforementioned first business information into the weight calculation model, the weight value of each business can be obtained in the output result of the model. The total points of the organization can be a fixed preset value, such as 100 points. After the system obtains the weight value of each business and the total points of the organization, it can obtain the first point value of each business by multiplying the weight value of each business by the total points of the organization in turn.

[0077] For example, as shown in Table 1, the business types of the institution include business A, business B and business C. Assuming that the target time period is the second quarter, the first business information corresponding to the target time period, that is, the total business volume of the three businesses in the second quarter is 170,000 and the business volume of each business in the second quarter is 50,000, 40,000 and 80,000, is input into the weight calculation model. The weight values ​​of the three businesses may be 30%, 20% and 50% respectively. The total score of the institution is 100 points. The weight values ​​of the three businesses are multiplied by the total score respectively, and the first score values ​​of business A, business B and business C can be obtained as 30 points, 20 points and 50 points respectively.

[0078] This embodiment obtains the first score value of each business by calculating the product of the weight value of each business and the total score of the organization, thereby ensuring the accuracy of the distribution of the total score of the organization to each business.

[0079] Optionally, in the information processing method provided in the embodiment of the present application, the point value of the target user under each business is calculated based on the second business information and the first business information to obtain M second point values, including: for the target business, obtaining the second business data under the target business from the second business information, and obtaining the first business data under the target business from the first business information; calculating the ratio of the second business data to the first business data to obtain the target ratio; obtaining the first point value of the target business, and multiplying the target ratio by the first point value to obtain the second point value under the target business.

[0080] It should be noted that in the process of calculating the point value of the target user under each business, you can first access the module that stores and manages the business information of the organization's employees in the system, and based on the query condition information such as the target time period and target user information contained in the performance query instruction, you can screen and filter the business information in the system to obtain the second business information of the target user within the target time period, including the second business data of each business, where the second business data can include the business volume of the target user under each business within the target time period.

[0081] Furthermore, the ratio of the business volume in the second business data of each business to the first business data can be calculated in turn to obtain the target ratio of each business. The target ratio of each business can be multiplied by the aforementioned first integral value to obtain the second integral value of each business.

[0082] For example, the second business information of the target user in the second quarter of the target time period may be shown in Table 2:

[0083] Table 2

[0084]

[0085] According to the business volume of each business in the second quarter in the first business data of Table 1: 50,000 for business A, 40,000 for business B, and 80,000 for business C, the ratio of the business volume in the second business data to the first business data is calculated, and the ratios of the three businesses are 0.1, 0, and 0, respectively. According to the first integral values ​​of 30 points, 20 points, and 50 points for the three businesses A, B, and C obtained above, the second integral value of the target user under business A is 3 points, and the second integral value under businesses B and C is 0, thereby completing the calculation operation of the integral value of each business of the target user according to the above technical process.

[0086] This embodiment calculates the ratio of the second service data to the first service data and then multiplies the ratio by the first integral value to obtain the second integral value of the target user in each service, thereby ensuring the accuracy of the distribution of each service integral to the user.

[0087] Optionally, in the information processing method provided in the embodiment of the present application, generating the performance information of the target user based on M second point values ​​includes: adding the M second point values ​​to obtain the total point value of the target user; determining the point value interval in which the total point value is located to obtain the target point value interval; determining the performance level corresponding to the target point value interval according to a preset comparison table to obtain the performance level of the target user, and determining the total point value and the performance level as the performance information of the target user, wherein the preset comparison table includes multiple point value intervals and the performance level corresponding to each point value interval.

[0088] It should be noted that by adding up the second point values ​​of the aforementioned target users under various services, the total point value of the target user within the target time period can be obtained, and then the target point value range of the target user is determined based on the range of the total point value. In the preset point value range and performance level comparison table, the performance level corresponding to the target point value can be determined to obtain the performance level of the target user, and finally the total point value and performance level of the target user are determined as the performance information of the target user.

[0089] For example, the table comparing the score range and performance level may be shown in Table 3:

[0090] Table 3

[0091] Integral value range 1-3 4-6 7-9 … Performance Level a-level B level C-level …

[0092] According to the above, the second integral value of the target user under business A in the second quarter is 3 points, and the second integral value under businesses B and C is 0. The total integral of the target user in the second quarter is 3 points. According to the comparison table, it can be determined that the performance level of the target user in the second quarter is level a, thereby completing the determination operation of the target user's performance data and ensuring the accuracy of the performance data.

[0093] This embodiment calculates the total points of the target user and determines the performance level corresponding to the total points range in a preset comparison table, and finally obtains the performance level of the target user in the target time period, thereby ensuring the efficiency and accuracy of performance allocation.

[0094] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0095] Example 2

[0096] The present application also provides an information processing device. It should be noted that the information processing device of the present application can be used to execute the information processing method provided in the present application. The information processing device provided in the present application is introduced below.

[0097] According to an embodiment of the present application, a device for implementing the above-mentioned information processing method is also provided. Figure 4 is a schematic diagram of an information processing device according to Example 2 of the present application, such as Figure 4 As shown, the device includes:

[0098] The receiving unit 41 is configured to receive a performance query instruction sent by a target user and obtain first business information of the organization to which the target user belongs within a target time period, wherein the first business information includes first business data of M businesses, where M is a positive integer.

[0099] The input unit 42 is configured to input the first service information into the weight calculation model, and calculate the weight of each service in the target time period according to the weight calculation model to obtain M weight values.

[0100] The first acquisition unit 43 is used to acquire the total points of the organization and distribute the total points of the organization according to M weight values ​​to obtain M first score values.

[0101] The second acquisition unit 44 is used to obtain the second business information of the target user within the target time period according to the performance query instruction, and calculate the point value of the target user under each business according to the second business information and the first business information to obtain M second point values, wherein the second business information includes the second business data of M businesses.

[0102] The generating unit 45 is configured to generate the performance information of the target user according to the M second integral values.

[0103] The information processing device provided by the embodiment of the present application adopts a receiving unit 41 to receive a performance query instruction sent by a target user, and obtains the first business information of the organization to which the target user belongs within the target time period, wherein the first business information includes the first business data of M businesses, and M is a positive integer; the input unit 42 inputs the first business information into a weight calculation model, and calculates the weight of each business within the target time period according to the weight calculation model to obtain M weight values; the first acquisition unit 43 obtains the total points of the organization, and distributes the total points of the organization according to the M weight values ​​to obtain M first point values; the second acquisition unit 44 obtains the second business information of the target user within the target time period according to the performance query instruction, and distributes the second business information according to the weight calculation model. The second business information and the first business information are used to calculate the point value of the target user under each business to obtain M second point values, wherein the second business information includes the second business data of the M businesses; the generation unit 45 generates the performance information of the target user according to the M second point values, obtains the first business information, and uses the weight calculation model to calculate the weight value of the business, and then distributes points, and obtains the second business information, and determines the total points of the target user and the corresponding performance level through calculation, and finally obtains the performance information of the target user, which solves the problem of low efficiency and accuracy of performance distribution through manual calculation, and thus achieves the technical effect of improving the efficiency and accuracy of performance distribution and shortening the performance distribution cycle.

[0104] Optionally, in the information processing device provided in the embodiment of the present application, the receiving unit 41 includes: a first acquisition module, used to acquire M businesses processed by the organization within the target time period, and respectively acquire the first business data of each business to obtain M first business data, wherein the first business data includes: the total business volume of the M businesses, the business volume of the business corresponding to the first business data, and the target time period; a combination module, used to combine the M first business data into first business information.

[0105] Optionally, in the information processing device provided in the embodiment of the present application, the weight calculation model is trained in the following manner: a third acquisition unit is used to obtain historical business information of the organization in multiple historical time periods to obtain N historical business information, wherein the historical business information includes the total business volume of the organization in the historical time period, the business volume of each business, and the historical time period, and N is a positive integer; a fourth acquisition unit is used to obtain the historical weight value of each business in each historical business information to obtain multiple historical weight values; a combination unit is used to combine each historical business information with the corresponding multiple historical weight values ​​into a set of sample data to obtain multiple sets of sample data; a training unit is used to train the convolutional neural network model through multiple sets of sample data to obtain a weight calculation model.

[0106] Optionally, in the information processing device provided in the embodiment of the present application, the weight calculation model is a convolutional neural network model, and the weight calculation model consists of an input layer, a shallow convolution layer, a deep convolution layer, an activation function and pooling layer, a feature fusion layer, a fully connected layer and an output layer, wherein the shallow convolution layer is used to extract shallow features in the input data, and the deep convolution layer is used to extract deep features from the shallow features.

[0107] Optionally, in the information processing device provided in the embodiment of the present application, the first acquisition unit 43 includes: a second acquisition module, used to obtain the weight value of each business, and use the weight value of each business to multiply the total score in turn to obtain the first score value of each business; a first determination module, used to determine the first score value of each business as M first score values.

[0108] Optionally, in the information processing device provided in the embodiment of the present application, the second acquisition unit 44 includes: a third acquisition module, used to obtain, for the target business, second business data under the target business from the second business information, and obtain first business data under the target business from the first business information; a first calculation module, used to calculate the ratio of the second business data to the first business data to obtain a target ratio; a fourth acquisition module, used to obtain a first integral value of the target business, and multiply the target ratio by the first integral value to obtain a second integral value under the target business.

[0109] Optionally, in the information processing device provided in the embodiment of the present application, the generation unit 45 includes: a second calculation module, used to add M second integral values ​​to obtain the total integral value of the target user; a second determination module, used to determine the integral value interval in which the total integral value is located, and obtain the target integral value interval; a third determination module, used to determine the performance level corresponding to the target integral value interval according to a preset comparison table, obtain the performance level of the target user, and determine the total integral value and the performance level as the performance information of the target user, wherein the preset comparison table includes multiple integral value intervals and the performance level corresponding to each integral value interval.

[0110] It should be noted that the receiving unit 41, input unit 42, first acquisition unit 43, second acquisition unit 44, and generation unit 45 correspond to steps S201 to S205 in Example 1. The receiving unit 41, input unit 42, first acquisition unit 43, second acquisition unit 44, and generation unit 45 implement the same examples and application scenarios as the corresponding steps, but are not limited to the contents disclosed in Example 1. It should be noted that the modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The modules can also be part of a device and can be run in the computer terminal 10 provided in Example 1.

[0111] Example 3

[0112] An embodiment of the present application may provide an electronic device, Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 Only one is shown) processor 1002, memory 1004, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0113] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the information processing method and device in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned information processing method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0114] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: receive the performance query instruction sent by the target user, and obtain the first business information of the organization to which the target user belongs within the target time period, wherein the first business information includes the first business data of M businesses, and M is a positive integer; input the first business information into the weight calculation model, and calculate the weight of each business within the target time period according to the weight calculation model to obtain M weight values; obtain the total points of the organization, and distribute the total points of the organization according to the M weight values ​​to obtain M first point values; obtain the second business information of the target user within the target time period according to the performance query instruction, and calculate the point value of the target user under each business according to the second business information and the first business information to obtain M second point values, wherein the second business information includes the second business data of M businesses; generate the performance information of the target user according to the M second point values.

[0115] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: obtain M businesses processed by the organization within the target time period, and obtain the first business data of each business respectively to obtain M first business data, wherein the first business data includes: the total business volume of the M businesses, the business volume of the business corresponding to the first business data, and the target time period; combine the M first business data into the first business information.

[0116] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain the historical business information of the organization in multiple historical time periods to obtain N historical business information, wherein the historical business information includes the total business volume of the organization in the historical time period, the business volume of each business, and the historical time period, and N is a positive integer; obtain the historical weight value of each business in each historical business information to obtain multiple historical weight values; combine each historical business information with the corresponding multiple historical weight values ​​into a set of sample data to obtain multiple sets of sample data; train the convolutional neural network model through multiple sets of sample data to obtain a weight calculation model.

[0117] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: the weight calculation model is a convolutional neural network model, and the weight calculation model consists of an input layer, a shallow convolution layer, a deep convolution layer, an activation function and pooling layer, a feature fusion layer, a fully connected layer and an output layer, wherein the shallow convolution layer is used to extract shallow features in the input data, and the deep convolution layer is used to extract deep features from the shallow features.

[0118] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: obtain the weight value of each business, and multiply the weight value of each business by the total score in turn to obtain the first score value of each business; determine the first score value of each business as M first score values.

[0119] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: for the target business, obtain the second business data under the target business from the second business information, and obtain the first business data under the target business from the first business information; calculate the ratio of the second business data to the first business data to obtain the target ratio; obtain the first integral value of the target business, and multiply the target ratio by the first integral value to obtain the second integral value under the target business.

[0120] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: add M second integral values ​​to obtain the total integral value of the target user; determine the integral value interval in which the total integral value is located to obtain the target integral value interval; determine the performance level corresponding to the target integral value interval according to the preset comparison table to obtain the performance level of the target user, and determine the total integral value and performance level as the performance information of the target user, wherein the preset comparison table includes multiple integral value intervals and the performance level corresponding to each integral value interval.

[0121] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID), a PAD, or other terminal devices. Figure 5 It does not limit the structure of the above electronic device. For example, the electronic device may also include Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 5 Different configurations shown.

[0122] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0123] Example 4

[0124] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the information processing method provided in the first embodiment.

[0125] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0126] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the information processing method.

[0127] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0128] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, 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 units or modules, which can be electrical or other forms.

[0130] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0132] If the integrated 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 technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0133] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing information, characterized in that: include: Receive a performance query instruction sent by a target user, and obtain first business information of an organization to which the target user belongs within a target time period, wherein the first business information includes first business data of M businesses, where M is a positive integer; Inputting the first service information into a weight calculation model, and calculating the weight of each service in the target time period according to the weight calculation model to obtain M weight values; Obtaining the total score of the institution, and distributing the total score of the institution according to the M weight values ​​to obtain M first score values; Acquire the second service information of the target user in the target time period according to the performance query instruction, and calculate the score value of the target user under each service according to the second service information and the first service information to obtain M second score values, wherein the second service information includes the second service data of the M services; The performance information of the target user is generated according to the M second integral values.

2. The method according to claim 1, characterized in that Acquiring the first business information of the organization to which the target user belongs within the target time period includes: Acquire M businesses processed by the institution within the target time period, and respectively acquire first business data of each business to obtain M first business data, wherein the first business data includes: a total business volume of the M businesses, a business volume of the business corresponding to the first business data, and the target time period; The M first service data are combined into the first service information.

3. The method according to claim 1, characterized in that The weight calculation model is trained in the following way: Acquire historical business information of the institution in multiple historical time periods to obtain N historical business information, wherein the historical business information includes the total business volume of the institution in the historical time period, the business volume of each business, and the historical time period, and N is a positive integer; Obtaining the historical weight value of each business in each historical business information to obtain multiple historical weight values; Combining each historical business information with the corresponding multiple historical weight values ​​into a set of sample data to obtain multiple sets of sample data; The convolutional neural network model is trained using the multiple groups of sample data to obtain the weight calculation model.

4. The method according to claim 1, characterized in that: The weight calculation model is a convolutional neural network model, which consists of an input layer, a shallow convolution layer, a deep convolution layer, an activation function and pooling layer, a feature fusion layer, a fully connected layer and an output layer, wherein the shallow convolution layer is used to extract shallow features in the input data, and the deep convolution layer is used to extract deep features from the shallow features.

5. The method according to claim 1, characterized in that Obtaining the total score of the institution, and distributing the total score of the institution according to the M weight values ​​to obtain the M first score values ​​includes: Obtaining a weight value of each business, and multiplying the weight value of each business by the total score in turn to obtain a first score value of each business; The first integral value of each of the services is determined as the M first integral values.

6. The method according to claim 1, characterized in that Calculating the score of the target user under each service according to the second service information and the first service information to obtain M second score values ​​includes: For a target service, obtaining second service data under the target service from the second service information, and obtaining first service data under the target service from the first service information; Calculating a ratio of the second service data to the first service data to obtain a target ratio; A first integral value of the target business is acquired, and the target ratio is multiplied by the first integral value to obtain a second integral value of the target business.

7. The method according to claim 1, characterized in that Generating the performance information of the target user according to the M second integral values ​​includes: Add the M second integral values ​​to obtain a total integral value of the target user; Determine the integral value interval in which the total integral value is located to obtain a target integral value interval; The performance level corresponding to the target point value interval is determined according to a preset comparison table to obtain the performance level of the target user, and the total point value and the performance level are determined as the performance information of the target user, wherein the preset comparison table includes multiple point value intervals and the performance level corresponding to each point value interval.

8. An information processing device, characterized in that: include: A receiving unit, configured to receive a performance query instruction sent by a target user, and obtain first business information of an organization to which the target user belongs within a target time period, wherein the first business information includes first business data of M businesses, where M is a positive integer; An input unit, used to input the first service information into a weight calculation model, and calculate the weight of each service in the target time period according to the weight calculation model to obtain M weight values; A first acquisition unit is used to acquire the total score of the institution and distribute the total score of the institution according to the M weight values ​​to obtain M first score values; A second acquisition unit is configured to acquire second service information of the target user within the target time period according to the performance query instruction, and calculate the score value of the target user under each service according to the second service information and the first service information to obtain M second score values, wherein the second service information includes the second service data of the M services; A generating unit is used to generate the performance information of the target user according to the M second integral values.

9. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor, used to run the program, wherein the program executes the information processing method described in any one of claims 1 to 7 when running.

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