Data processing method, device and system

Abstract

The present disclosure provides a data processing method, which is applied to the financial field, the computer technology field or other fields. The data processing method includes: obtaining different types of working time reference data of the target object from multiple working time management systems through an interface call method to obtain multiple types of working time reference data; determining acceptance working time data from multiple types of working time reference data; obtaining different types of work effectiveness reference data of the target object from multiple work effectiveness management systems through an interface call method to obtain multiple types of work effectiveness reference data; determining reward and punishment workload data based on the acceptance working time data and multiple types of work effectiveness reference data; and determining the actual workload data of the target object based on the reward and punishment workload data and the acceptance working time data. The present disclosure also provides a data processing device, a system, an electronic device, a computer-readable storage medium and a computer program product.

Description

Technical Field

[0001] The present disclosure relates to the fields of financial technology and computer technology, and more specifically, to a data processing method, device and system. Background Art

[0002] With the development of economy and society, there are more and more large-scale companies, which often need to accept workloads.

[0003] In the process of realizing the concept of the present disclosure, the inventors discovered that the workload was accepted manually in the related art, and this acceptance method had technical problems such as being too time-consuming and having deviations in the workload acceptance results. Summary of the invention

[0004] In view of this, the present disclosure provides a data processing method, device and system.

[0005] One aspect of the present disclosure provides a data processing method, comprising:

[0006] Obtain different types of working time reference data of target objects from multiple working time management systems through interface calls to obtain multiple types of working time reference data, wherein the different types of working time reference data are used to evaluate the working time performance of the target objects in different evaluation dimensions;

[0007] Determine acceptance working time data from the above-mentioned multiple types of working time reference data;

[0008] Obtain different types of work performance reference data of the target object from multiple work performance management systems through interface calls to obtain multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions;

[0009] Determine the workload data for rewards and punishments based on the above acceptance work duration data and the above various types of work performance reference data; and

[0010] The actual workload data of the above-mentioned target object is determined based on the above-mentioned reward and punishment workload data and the above-mentioned acceptance work duration data.

[0011] According to an embodiment of the present disclosure, the above-mentioned multiple types of working time reference data include standard working time reference data, first working time reference data and second working time reference data;

[0012] The above-mentioned acceptance working time data determined from the above-mentioned multiple types of working time reference data include:

[0013] Determine the minimum value among the standard working time reference data, the first working time reference data and the second working time reference data;

[0014] The working time reference data corresponding to the above minimum value is determined as the above acceptance working time data.

[0015] According to an embodiment of the present disclosure, the above-mentioned multiple types of work performance reference data include first work performance reference data, and the above-mentioned first work performance reference data includes performance reward reference data and performance deduction reference data.

[0016] According to an embodiment of the present disclosure, the first work performance reference data is obtained by the following operations:

[0017] Get multiple performance reward data;

[0018] Determine the performance reward reference data according to the plurality of performance reward data and the weight factors respectively corresponding to the plurality of performance reward data;

[0019] Get multiple performance deduction data;

[0020] Determining the performance deduction reference data according to the plurality of performance deduction data and the weight factors respectively corresponding to the plurality of performance deduction data;

[0021] The first work performance reference data is determined based on the performance reward reference data and the performance deduction reference data.

[0022] According to an embodiment of the present disclosure, the above-mentioned multiple types of work performance reference data also include second work performance reference data; the above-mentioned reward and punishment workload data include reward workload data or deduction workload data;

[0023] The above reward and punishment workload data is determined by the following operations:

[0024] Determine the data to be processed according to the first work performance reference data and the second work performance reference data;

[0025] Determine whether the data to be processed is less than a preset threshold;

[0026] In the case where the data to be processed is greater than or equal to the preset threshold, the reward and punishment workload data is determined as the reward workload data;

[0027] When the above-mentioned data to be processed is less than the above-mentioned preset threshold, the above-mentioned reward and punishment workload data is determined as the above-mentioned penalty workload data.

[0028] According to an embodiment of the present disclosure, the determining of the data to be processed according to the first work performance reference data and the second work performance reference data includes:

[0029] The sum of the first work performance reference data and the second work performance reference data is used as the data to be processed.

[0030] According to an embodiment of the present disclosure, the above-mentioned reward data includes: any two or more combinations of the number of submitted lines of code, the difficulty of development requirements, and the number of submitted test questions;

[0031] The above deduction data include: any two or more combinations of defect density, number of production problems, and task overdue rate.

[0032] Another aspect of the present disclosure provides a data processing device, comprising:

[0033] A first acquisition module is used to acquire different types of working time reference data of target objects from multiple working time management systems through an interface call to obtain multiple types of working time reference data, wherein the different types of working time reference data are used to evaluate the working time performance of the target objects in different evaluation dimensions;

[0034] A first determination module is used to determine the acceptance working time data from the above-mentioned multiple types of working time reference data;

[0035] The second acquisition module is used to obtain different types of work performance reference data of the target object from multiple work performance management systems through an interface call to obtain multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions;

[0036] A second determination module is used to determine reward and punishment workload data based on the acceptance work duration data and the multiple types of work performance reference data; and

[0037] The third determination module is used to determine the actual workload data of the above-mentioned target object based on the above-mentioned reward and punishment workload data and the above-mentioned acceptance work duration data.

[0038] Another aspect of the present disclosure provides a data processing system, comprising:

[0039] Multiple working hours management systems, used to store different types of working hours reference data of target objects, wherein the different types of working hours reference data are used to evaluate the working hours performance of the target objects in different evaluation dimensions;

[0040] A plurality of work performance management systems, used to store different types of work performance reference data of target objects, wherein the different types of work performance reference data are used to evaluate the work performance of the target objects in different evaluation dimensions; and

[0041] Data processing means for:

[0042] Obtain different types of working time reference data of target objects from multiple working time management systems through interface calls to obtain multiple types of working time reference data, wherein the different types of working time reference data are used to evaluate the working time performance of the target objects in different evaluation dimensions;

[0043] Determine acceptance working time data from the above-mentioned multiple types of working time reference data;

[0044] Obtain different types of work performance reference data of the target object from multiple work performance management systems through interface calls to obtain multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions;

[0045] Determine the workload data for rewards and punishments based on the above acceptance work duration data and the above various types of work performance reference data; and

[0046] The actual workload data of the above-mentioned target object is determined based on the above-mentioned reward and punishment workload data and the above-mentioned acceptance work duration data.

[0047] Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more instructions, wherein when the one or more instructions are executed by the one or more processors, the one or more processors implement the method as described above.

[0048] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0049] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and when the instructions are executed, are used to implement the method as described above.

[0050] The disclosed embodiment obtains various types of work time reference data from multiple work time management systems, and various types of work performance reference data from multiple work performance management systems, and then determines the actual workload data of the target object based on the work time reference data and the work performance reference data, thereby achieving the technical effect of automatically determining the actual workload data of the target object, saving acceptance costs, being able to accurately accept the actual workload of the target object, and improving the rationality of workload acceptance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0052] Figure 1 An exemplary system architecture to which the data processing method, apparatus, and system according to an embodiment of the present disclosure can be applied is schematically shown;

[0053] Figure 2 A flowchart schematically shows a data processing method according to an embodiment of the present disclosure;

[0054] Figure 3 A flowchart of determining acceptance working time data from multiple types of working time reference data according to an embodiment of the present disclosure is schematically shown;

[0055] Figure 4 A flowchart of obtaining first work performance reference data according to an embodiment of the present disclosure is schematically shown;

[0056] Figure 5 A flowchart for determining reward and punishment workload data according to an embodiment of the present disclosure is schematically shown;

[0057] Figure 6 A block diagram schematically shows a data processing device according to an embodiment of the present disclosure;

[0058] Figure 7 A block diagram schematically shows a data processing system according to an embodiment of the present disclosure; and

[0059] Figure 8 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0060] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0061] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0062] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0063] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0064] The present disclosure provides a data processing method, which is applied to the financial field, the computer technology field or other fields. The data processing method includes: obtaining different types of working time reference data of the target object from multiple working time management systems through an interface call method to obtain multiple types of working time reference data; determining acceptance working time data from multiple types of working time reference data; obtaining different types of work effectiveness reference data of the target object from multiple work effectiveness management systems through an interface call method to obtain multiple types of work effectiveness reference data; determining reward and punishment workload data based on the acceptance working time data and multiple types of work effectiveness reference data; and determining the actual workload data of the target object based on the reward and punishment workload data and the acceptance working time data. The present disclosure also provides a data processing device, a system, an electronic device, a computer-readable storage medium and a computer program product.

[0065] Figure 1 The exemplary system architecture 100 to which the data processing method, apparatus and system according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0066] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0067] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0068] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0069] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0070] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data processing device and system provided in the embodiment of the present disclosure can generally be set in the server 105. The data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Correspondingly, the data processing device and system provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Alternatively, the data processing method provided in the embodiment of the present disclosure can also be executed by the terminal devices 101, 102, or 103, or can also be executed by other terminal devices different from the terminal devices 101, 102, or 103. Accordingly, the data processing apparatus and system provided by the embodiments of the present disclosure may also be disposed in the terminal device 101 , 102 , or 103 , or in other terminal devices different from the terminal device 101 , 102 , or 103 .

[0071] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0072] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0073] like Figure 2 As shown, the method includes operations S201 to S205.

[0074] In operation S201, different types of work hour reference data of the target object are respectively obtained from multiple work hour management systems through interface calling, so as to obtain multiple types of work hour reference data, wherein the different types of work hour reference data are used to evaluate the work hour performance of the target object in different evaluation dimensions.

[0075] According to an embodiment of the present disclosure, the working hours management system may include, for example, an attendance system and a workload management system.

[0076] According to an embodiment of the present disclosure, the workload in the workload management system may be the workload measured in working hours.

[0077] In operation S202, acceptance working time data is determined from a plurality of types of working time reference data.

[0078] In operation S203, different types of work performance reference data of the target object are respectively obtained from multiple work performance management systems through interface calling, so as to obtain multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions.

[0079] According to an embodiment of the present disclosure, a plurality of work performance management systems may include, for example, a code management system and a problem management system.

[0080] In operation S204, reward and punishment workload data is determined based on the acceptance work duration data and various types of work performance reference data.

[0081] In operation S205, actual workload data of the target object is determined according to the reward and punishment workload data and the acceptance work duration data.

[0082] The disclosed embodiment obtains various types of work time reference data from multiple work time management systems, and various types of work performance reference data from multiple work performance management systems, and then determines the actual workload data of the target object based on the work time reference data and the work performance reference data, thereby achieving the technical effect of automatically determining the actual workload data of the target object, saving acceptance costs, being able to accurately accept the actual workload of the target object, and improving the rationality of workload acceptance.

[0083] It should be noted that the data processing method provided in the embodiment of the present disclosure can be used to obtain the actual workload data of the target object for one day, one week, one month, one quarter, or one year, and can be set according to the needs of the actual application situation.

[0084] Reference below Figure 3 to Figure 5 , combined with specific embodiments Figure 2 The method shown is further explained.

[0085] According to an embodiment of the present disclosure, the multiple types of working duration reference data include standard working duration reference data, first working duration reference data and second working duration reference data.

[0086] Figure 3The flowchart of determining acceptance working time data from multiple types of working time reference data according to an embodiment of the present disclosure is schematically shown.

[0087] like Figure 3 As shown, the method includes operations S301-S302.

[0088] It should be noted that, unless it is explicitly stated that there is a sequence of execution between different operations shown in the flowchart in the embodiments of the present disclosure, or there is a sequence of execution between different operations in technical implementation, otherwise, the execution order of multiple operations may not be prioritized, and multiple operations may also be executed simultaneously.

[0089] In operation S301, a minimum value among standard working duration reference data, first working duration reference data, and second working duration reference data is determined.

[0090] According to an embodiment of the present disclosure, the first working time reference data may be the attendance days of the target object within a preset acceptance period.

[0091] According to an embodiment of the present disclosure, the second working time reference data may be the completed workload data of the target object within a preset acceptance period.

[0092] According to an embodiment of the present disclosure, the standard working hour reference data may be the statutory working hours within a preset acceptance period. For example, the preset acceptance period is one month, and the statutory normal working hours in a month are 20 days, then the standard working hour data may be 20 days, for example.

[0093] According to an embodiment of the present disclosure, for example, when the preset acceptance period is one month, the first working hour reference data of the target object A is 19 days, the second working hour reference data is 19.5 days, and the standard working hour reference data is 20 days, then the minimum value can be determined to be 19 days of the first working hour.

[0094] In operation S302, the working time reference data corresponding to the minimum value is determined as the acceptance working time data.

[0095] According to an embodiment of the present disclosure, the acceptance work time data can represent the workload base of the target object within a preset acceptance period, so that the target object can be rewarded or punished based on the acceptance work time data.

[0096] According to an embodiment of the present disclosure, by taking the minimum value among the standard working time reference data, the first working time reference data and the second working time reference data as the acceptance working time data, the workload base of the target object can be objectively measured.

[0097] According to an embodiment of the present disclosure, the multiple types of work performance reference data include first work performance reference data, and the first work performance reference data includes performance reward reference data and performance deduction reference data.

[0098] Figure 4 The flowchart of obtaining the first work performance reference data according to an embodiment of the present disclosure is schematically shown.

[0099] like Figure 4 As shown, the method includes operations S401 to S405.

[0100] In operation S401 , a plurality of achievement reward data are obtained.

[0101] In operation S402 , performance reward reference data is determined according to a plurality of performance reward data and weight factors respectively corresponding to the plurality of performance reward data.

[0102] According to an embodiment of the present disclosure, while obtaining multiple performance reward data, weight factors corresponding to each dimensional data can be obtained according to the numerical values ​​of the performance reward data. The weight factors can be preset coefficients. For example, different weight factors can be set for the difficulty of the target object to complete a certain task. The weight factors can be any numerical value between 0 and 1.

[0103] The numerical values ​​corresponding to the multiple performance reward data are multiplied by the corresponding weight factors, and then all the data can be summed up to obtain the performance reward reference data.

[0104] In operation S403, a plurality of performance deduction data is obtained.

[0105] In operation S404 , performance deduction reference data is determined according to the plurality of performance deduction data and the weight factors respectively corresponding to the plurality of performance deduction data.

[0106] Similar to determining the reference data for performance rewards, while obtaining multiple performance deduction data, it is also possible to obtain the weight factors corresponding to the data of each dimension based on the values ​​corresponding to the performance deduction data, multiply the values ​​corresponding to the multiple performance deduction data by the corresponding weight factors, and then sum up all the data to obtain the performance deduction reference data.

[0107] In operation S405, first work performance reference data is determined according to the performance reward reference data and the performance deduction reference data.

[0108] According to an embodiment of the present disclosure, for example, the effectiveness deduction reference data may be first negated, and then the effectiveness reward reference data and the negated effectiveness deduction reference data may be summed to obtain the first work effectiveness reference data.

[0109] According to an embodiment of the present disclosure, for example, the first work performance reference data may be obtained by subtracting the performance reward reference data from the performance deduction reference data.

[0110] According to an embodiment of the present disclosure, the first work performance reference data can objectively evaluate the work quality of the target object within a preset acceptance period. For example, when the first work performance reference data is a negative value, it indicates that the target object's performance deduction reference data is greater than the performance reward reference data, that is, the target object's work quality is poor.

[0111] According to an embodiment of the present disclosure, the multiple types of work performance reference data also include second work performance reference data.

[0112] According to an embodiment of the present disclosure, the second work performance reference data may include overtime data of the target object within a preset acceptance period.

[0113] According to an embodiment of the present disclosure, the second work performance reference data may also include the workload saturation of the target object within a preset acceptance period.

[0114] According to an embodiment of the present disclosure, work saturation may be calculated by dividing actual attendance hours by statutory attendance hours.

[0115] According to an embodiment of the present disclosure, the reward and punishment workload data includes reward workload data or deduction workload data.

[0116] Figure 5 The flowchart of determining reward and punishment workload data according to an embodiment of the present disclosure is schematically shown.

[0117] like Figure 5 As shown, the method includes operations S501 to S504.

[0118] In operation S501, data to be processed is determined according to the first work performance reference data and the second work performance reference data.

[0119] In operation S502, it is determined whether the data to be processed is less than a preset threshold. If the data to be processed is greater than or equal to the preset threshold, operation S503 is performed; otherwise, operation S504 is performed.

[0120] In operation S503, the reward and punishment workload data is determined as reward workload data.

[0121] According to an embodiment of the present disclosure, the size of the reward workload data can be determined by the difference between the data to be processed and a preset threshold.

[0122] According to an embodiment of the present disclosure, multiple difference intervals can be preset, each of which has a corresponding size of reward work data. After determining the difference between the data to be processed and the preset threshold, the size of the reward workload data is determined by the difference interval corresponding to the difference.

[0123] In operation S504, the reward and punishment workload data is determined as penalty workload data.

[0124] According to an embodiment of the present disclosure, the size of the penalty workload data may be determined by the difference between the data to be processed and a preset threshold.

[0125] According to an embodiment of the present disclosure, multiple difference intervals can be preset, each of which has a corresponding size of penalty work data. After determining the difference between the data to be processed and the preset threshold, the size of the penalty workload data is determined by the difference interval corresponding to the difference.

[0126] According to the embodiments of the present disclosure, the preset threshold value can be flexibly set by those skilled in the art with reference to actual application conditions, and the embodiments of the present disclosure do not specifically limit the preset threshold value.

[0127] According to an embodiment of the present disclosure, after determining the reward and punishment workload data through the first work performance reference data and the second work performance reference data, the reward and punishment workload data and the acceptance work duration data can be summed to finally obtain the actual workload data of the target object within the preset acceptance period.

[0128] According to an embodiment of the present disclosure, determining the data to be processed according to the first work performance reference data and the second work performance reference data includes the following operations.

[0129] The sum of the first work performance reference data and the second work performance reference data is used as the data to be processed.

[0130] According to an embodiment of the present disclosure, the reward data includes: a combination of any two or more of the number of submitted lines of code, the difficulty of development requirements, and the number of submitted test questions.

[0131] The deduction data includes any combination of two or more of defect density, number of production problems, and task overdue rate.

[0132] According to an embodiment of the present disclosure, the defect density may be determined by the following equation.

[0133] Defect density = (number of R&D issues + number of adaptability issues) / full-caliber scale*1000.

[0134] According to an embodiment of the present disclosure, defect density, that is, the number of problems per unit workload, can characterize the work quality of a target object.

[0135] According to an embodiment of the present disclosure, the task overdue rate may be determined by the following equation.

[0136] Task overdue rate = (completed overdue records + uncompleted overdue records) / (completed records + uncompleted overdue records).

[0137] Figure 6 The block diagram schematically shows a data processing device according to an embodiment of the present disclosure.

[0138] like Figure 6 As shown, the data processing device 600 includes a first acquisition module 610 , a first determination module 620 , a second acquisition module 630 , a second determination module 640 and a third determination module 650 .

[0139] The first acquisition module 610 is used to obtain different types of work time reference data of the target object from multiple work time management systems through interface calls, and obtain multiple types of work time reference data, wherein different types of work time reference data are used to evaluate the work time performance of the target object in different evaluation dimensions.

[0140] The first determination module 620 is used to determine the acceptance working time data from multiple types of working time reference data.

[0141] The second acquisition module 630 is used to obtain different types of work performance reference data of the target object from multiple work performance management systems through interface calls, and obtain multiple types of work performance reference data, wherein different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions.

[0142] The second determination module 640 is used to determine the reward and punishment workload data based on the acceptance work time data and various types of work performance reference data.

[0143] The third determination module 650 is used to determine the actual workload data of the target object according to the reward and punishment workload data and the acceptance work duration data.

[0144] According to an embodiment of the present disclosure, the multiple types of working duration reference data include standard working duration reference data, first working duration reference data and second working duration reference data.

[0145] According to an embodiment of the present disclosure, the first acceptance module 620 includes a first determination unit and a second determination unit.

[0146] The first determining unit is used to determine the minimum value among the standard working time reference data, the first working time reference data and the second working time reference data.

[0147] The second determining unit is used to determine the working time reference data corresponding to the minimum value as the acceptance working time data.

[0148] According to an embodiment of the present disclosure, the multiple types of work performance reference data include first work performance reference data, and the first work performance reference data includes performance reward reference data and performance deduction reference data.

[0149] According to an embodiment of the present disclosure, the second acquisition module 630 includes a first acquisition unit, a third determination unit, a second acquisition unit, a fourth determination unit, and a fifth determination unit.

[0150] The first acquisition unit is used to acquire a plurality of performance reward data.

[0151] The third determining unit is used to determine the performance reward reference data according to the plurality of performance reward data and the weight factors respectively corresponding to the plurality of performance reward data.

[0152] The second acquisition unit is used to acquire a plurality of performance deduction data.

[0153] The fourth determining unit is used to determine the performance deduction reference data according to the plurality of performance deduction data and the weight factors respectively corresponding to the plurality of performance deduction data.

[0154] The fifth determining unit is used to determine the first work performance reference data according to the performance reward reference data and the performance deduction reference data.

[0155] According to an embodiment of the present disclosure, the multiple types of work performance reference data also include second work performance reference data; the reward and punishment workload data include reward workload data or deduction workload data.

[0156] According to an embodiment of the present disclosure, the second determining module 640 includes a sixth determining unit, a seventh determining unit, and an eighth determining unit.

[0157] The sixth determining unit is used to determine the data to be processed according to the first work performance reference data and the second work performance reference data.

[0158] Determine whether the data to be processed is less than a preset threshold.

[0159] The seventh determination unit is used to determine the reward and punishment workload data as reward workload data when the data to be processed is greater than or equal to a preset threshold.

[0160] The eighth determination unit is used to determine the reward and punishment workload data as penalty workload data when the data to be processed is less than a preset threshold.

[0161] According to an embodiment of the present disclosure, the eighth determining unit includes a first determining subunit.

[0162] The first determining subunit is used to take the sum of the first work performance reference data and the second work performance reference data as the data to be processed.

[0163] According to an embodiment of the present disclosure, the reward data includes: a combination of any two or more of the number of submitted lines of code, the difficulty of development requirements, and the number of submitted test questions.

[0164] The deduction data includes any combination of two or more of defect density, number of production problems, and task overdue rate.

[0165] Figure 7 A block diagram of a data processing system according to an embodiment of the present disclosure is schematically shown.

[0166] like Figure 7 As shown, the data processing system 700 includes multiple work time management systems 710 , multiple work performance management systems 720 and a data processing device 730 .

[0167] Multiple working hour management systems 710 are used to store different types of working hour reference data of target objects, wherein different types of working hour reference data are used to evaluate the working hour performance of target objects in different evaluation dimensions.

[0168] Multiple work performance management systems 720 are used to store different types of work performance reference data of target objects, wherein different types of work performance reference data are used to evaluate the work performance of target objects in different evaluation dimensions.

[0169] The data processing device 730 is used to perform the following operations.

[0170] Different types of work hour reference data of the target object are respectively obtained from multiple work hour management systems through interface calling, so as to obtain multiple types of work hour reference data, wherein the different types of work hour reference data are used to evaluate the work hour performance of the target object in different evaluation dimensions.

[0171] Determine acceptance working time data from multiple types of working time reference data.

[0172] Different types of work performance reference data of the target object are respectively obtained from multiple work performance management systems through interface calling, so as to obtain multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions.

[0173] Determine the workload data for rewards and punishments based on the acceptance work duration data and various types of work performance reference data.

[0174] Determine the actual workload data of the target object based on the reward and punishment workload data and acceptance work duration data.

[0175] According to the embodiments of the present disclosure, referring to Figure 7 The data processing device can obtain working time reference data from any one or more of the working time management system 7101, the working time management system 7102 and the time management system 7103; the data processing device can obtain work performance reference data from any one or more of the work performance management system 7201, the work performance management system 7202 and the performance management system 7203.

[0176] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0177] For example, any multiple of the first acquisition module 610, the first determination module 620, the second acquisition module 630, the second determination module 640, the third determination module 650, and the data processing device 730 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more of the modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the first acquisition module 610, the first determination module 620, the second acquisition module 630, the second determination module 640, the third determination module 650, and the data processing device 730 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware and firmware or by an appropriate combination of any of them. Alternatively, at least one of the first acquisition module 610, the first determination module 620, the second acquisition module 630, the second determination module 640, the third determination module 650, and the data processing device 730 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0178] It should be noted that the data processing device and system part in the embodiments of the present disclosure correspond to the data processing method part in the embodiments of the present disclosure. The description of the data processing device and system part specifically refers to the data processing method part and will not be repeated here.

[0179] Figure 8 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0180] like Figure 8As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0181] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0182] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage portion 808 as needed.

[0183] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0184] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0185] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0186] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 802 and / or the RAM 803 described above and / or one or more memories other than the ROM 802 and the RAM 803 .

[0187] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the character recognition model training method and character recognition method provided by the embodiment of the present disclosure.

[0188] When the computer program is executed by the processor 801, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0189] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0190] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0191] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0192] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, comprising: Obtain different types of working time reference data of the target object from multiple working time management systems through an interface call to obtain multiple types of working time reference data, wherein the different types of working time reference data are used to evaluate the working time performance of the target object in different evaluation dimensions; Determine acceptance working time data from the multiple types of working time reference data; Obtaining different types of work performance reference data of the target object from multiple work performance management systems respectively through interface calling, thereby obtaining multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions; Determine reward and punishment workload data based on the acceptance work duration data and the multiple types of work performance reference data; and Determine the actual workload data of the target object according to the reward and punishment workload data and the acceptance work duration data; The multiple types of working time reference data include standard working time reference data, first working time reference data and second working time reference data; the standard working time reference data, the first working time reference data and the second working time reference data are respectively the statutory working hours, attendance days and completed workload data of the target object within a preset acceptance period; The determining of the acceptance working time data from the multiple types of working time reference data comprises: Determine a minimum value among the standard working time reference data, the first working time reference data and the second working time reference data; The working time reference data corresponding to the minimum value is determined as the acceptance working time data.

2. The method according to claim 1, wherein: The multiple types of work performance reference data include first work performance reference data, and the first work performance reference data includes performance reward reference data and performance deduction reference data.

3. The method according to claim 2, wherein: The first work performance reference data is obtained by the following operations: Get multiple performance reward data; Determining the performance reward reference data according to the plurality of performance reward data and the weight factors respectively corresponding to the plurality of performance reward data; Get multiple performance deduction data; Determining the performance deduction reference data according to the plurality of performance deduction data and the weight factors respectively corresponding to the plurality of performance deduction data; The first work performance reference data is determined according to the performance reward reference data and the performance deduction reference data.

4. The method according to claim 3, wherein: The multiple types of work performance reference data also include second work performance reference data; the reward and punishment workload data include reward workload data or punishment workload data; The reward and punishment workload data is determined by the following operations: Determining data to be processed according to the first work performance reference data and the second work performance reference data; Determine whether the data to be processed is less than a preset threshold; In the case where the data to be processed is greater than or equal to the preset threshold, determining the reward and punishment workload data as the reward workload data; When the data to be processed is less than the preset threshold, the reward and punishment workload data is determined as the penalty workload data.

5. The method according to claim 4, wherein: The determining of the data to be processed according to the first work performance reference data and the second work performance reference data comprises: The sum of the first work performance reference data and the second work performance reference data is used as the data to be processed.

6. The method according to claim 3, wherein: The performance reward data includes: any combination of two or more of the number of submitted lines of code, the difficulty of development requirements, and the number of submitted test questions; The performance deduction data includes: any two or more combinations of defect density, number of production problems, and task overdue rate.

7. A data processing device, comprising: A first acquisition module is used to acquire different types of working time reference data of a target object from multiple working time management systems through an interface call to obtain multiple types of working time reference data, wherein the different types of working time reference data are used to evaluate the working time performance of the target object in different evaluation dimensions; A first determining module, configured to determine acceptance working time data from the multiple types of working time reference data; A second acquisition module is used to acquire different types of work performance reference data of the target object from multiple work performance management systems through an interface call to obtain multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions; A second determination module is used to determine reward and punishment workload data according to the acceptance work duration data and the multiple types of work performance reference data; and A third determination module is used to determine the actual workload data of the target object according to the reward and punishment workload data and the acceptance work duration data; The multiple types of working time reference data include standard working time reference data, first working time reference data and second working time reference data; the standard working time reference data, the first working time reference data and the second working time reference data are respectively the statutory working hours, attendance days and completed workload data of the target object within a preset acceptance period; The first determining module is further used for: Determine a minimum value among the standard working time reference data, the first working time reference data and the second working time reference data; The working time reference data corresponding to the minimum value is determined as the acceptance working time data.

8. A data processing system comprising: Multiple working hours management systems, used to store different types of working hours reference data of target objects, wherein the different types of working hours reference data are used to evaluate the working hours performance of the target objects in different evaluation dimensions; A plurality of work performance management systems, used to store different types of work performance reference data of target objects, wherein the different types of work performance reference data are used to evaluate the work performance of the target objects in different evaluation dimensions; and Data processing means for: Obtain different types of working time reference data of the target object from multiple working time management systems through an interface call to obtain multiple types of working time reference data, wherein the different types of working time reference data are used to evaluate the working time performance of the target object in different evaluation dimensions; Determine acceptance working time data from the multiple types of working time reference data; Obtaining different types of work performance reference data of the target object from multiple work performance management systems respectively through interface calling, thereby obtaining multiple types of work performance reference data, wherein the different types of work performance reference data are used to evaluate the work performance of the target object in different evaluation dimensions; Determine reward and punishment workload data based on the acceptance work duration data and the multiple types of work performance reference data; and Determine the actual workload data of the target object according to the reward and punishment workload data and the acceptance work duration data; The multiple types of working time reference data include standard working time reference data, first working time reference data and second working time reference data; the standard working time reference data, the first working time reference data and the second working time reference data are respectively the statutory working hours, attendance days and completed workload data of the target object within a preset acceptance period; The determining of the acceptance working time data from the multiple types of working time reference data comprises: Determine a minimum value among the standard working time reference data, the first working time reference data and the second working time reference data; The working time reference data corresponding to the minimum value is determined as the acceptance working time data.

9. An electronic device, comprising: one or more processors; A memory for storing one or more instructions, Wherein, when the one or more instructions are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 6.

11. A computer program product, comprising computer executable instructions, wherein the instructions are used to implement the method according to any one of claims 1 to 6 when executed.

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