Performance evaluation source data acquisition method and system, terminal and medium

Abstract

The invention relates to the field of performance evaluation, and particularly discloses a performance evaluation source data acquisition method and system, a terminal and a medium, performance evaluation source data is extracted from each source database in a preset time period, and one type of performance evaluation source data is extracted from each source database; preprocessing the performance evaluation source data; storing the preprocessed performance evaluation source data in each data recording library according to categories, wherein each data recording library stores one category of performance evaluation source data; when performance evaluation is carried out, performance evaluation source data are obtained from all the data record libraries. According to the method, the data acquisition efficiency is guaranteed, the statistical calculation efficiency of performance assessment is improved, and the working performance of the source database is prevented from being influenced.

Description

Technical Field

[0001] The present invention relates to the field of performance evaluation, and particularly to a method, a system, a terminal and a medium for obtaining source data of performance evaluation. Background Art

[0002] Currently, more and more enterprises are building one or more shared service centers. A shared service center is an organizational model that centralizes repetitive, general and standardizable business functions scattered in various business units or departments within an enterprise into an independent business unit for unified processing and management. In the operation and management of a shared service center, performance management is an important part.

[0003] The traditional way of performance management is to manually count the data of document processing. However, the statistical dimension is single and there is no index system. Usually, after exporting the document data, simple processing and analysis are carried out to obtain the performance appraisal data, resulting in low efficiency of performance appraisal and low credibility of the data. Building a sound performance appraisal system, based on the set appraisal parameters, extracting relevant data from the database in an automated manner, and then calculating the appraisal performance based on components can effectively solve the defects of manual statistics. However, when the relevant performance appraisal system conducts performance appraisal, it directly extracts from the source database that stores the original performance-related data generated when handling documents. This data acquisition method affects the statistical calculation efficiency of performance appraisal, burdens the source database, and even affects the working performance of the source database. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method, a system, a terminal and a medium for obtaining source data of performance evaluation, which can ensure the data acquisition efficiency, thereby improving the statistical calculation efficiency of performance appraisal and avoiding affecting the working performance of the source database.

[0005] In a first aspect, the technical solution of the present invention provides a method for obtaining source data of performance evaluation, including the following steps: Extract source data of performance evaluation from each source database respectively during a preset period, and each source database extracts one type of source data of performance evaluation; Preprocess the source data of performance evaluation; Store the preprocessed source data of performance evaluation into each data record library according to categories respectively, and each data record library stores one type of source data of performance evaluation; When conducting performance evaluation, obtain the source data of performance evaluation from each data record library respectively.

[0006] In an optional implementation manner, extracting source data of performance evaluation from each source database respectively specifically includes: Configure output parameters and unified transfer input parameters; the output parameters include multiple sub-output parameters; Issue a data extraction instruction to the source database by outputting output parameters and uniformly passing input parameters; Receive the source data packet fed back by the source database. The source database constructs the data corresponding to each sub-output parameter into a source data block, sorts the source data blocks in a pre-agreed order, and then constructs all the source data blocks into a source data packet; Decrypt the source data packet to obtain the performance evaluation source data.

[0007] In an optional implementation, decrypting the source data packet to obtain the performance evaluation source data specifically includes: Pre-map and associate the sub-output parameter names with the sorting of the source data blocks; Obtain the pre-stored private key, and each source database corresponds to a private key; Use the private key to decrypt the source data packet to obtain all the source data blocks in the source data packet, and parse out the sorting order of each source data block; According to the mapping association between the sub-output parameter name and the sorting of the source data blocks, obtain the sub-output parameter name corresponding to each source data block; Obtain the pre-stored first master key, and each source database corresponds to a first master key; Based on the first master key and the sub-output parameter name, generate the first sub-key for each source data block through a key derivation function; Use the first sub-key to decrypt the source data block to obtain the performance evaluation source data.

[0008] In an optional implementation, store the preprocessed performance evaluation source data into each data record library by category, specifically including: Perform block processing on the preprocessed performance evaluation source data, and construct the data corresponding to each preprocessed sub-output parameter into a target data block; Map and associate the sub-output parameter name with the target data block; Obtain the pre-stored second master key; Based on the second master key and the sub-output parameter name, generate the second sub-key for each target data block through a key derivation function; Use the second sub-key to encrypt the target data block; Construct the encrypted target data blocks into a target data packet, and use the pre-stored public key to encrypt the target data packet; each data record library corresponds to a public key; Transmit the encrypted target data packet to the corresponding data record library for storage.

[0009] In an optional implementation, the source database includes a historical processing task database, a task processing log database, a task rejection record, and a quality inspection log database; The source data for performance evaluation includes workload data, work timeliness data, and work quality data.

[0010] In an alternative implementation, the unified input parameters for transmission include the type of evaluation object, the internal code of the evaluation object, the name of the evaluation object, and the evaluation period. The output parameters of the historical task processing database include task type, node name, processor, business group, and shared center. The output parameters of the task processing log database include task type, task entry time into the pool, task bill of lading time, and task processing time. The output parameters of the task rejection record and quality inspection log database include task type, whether rejected, rejected to node, rejecter, rejection time, number of rejections, whether quality inspection is compliant, reasons for non-compliance in quality inspection, quality inspection time, and quality inspector.

[0011] In an alternative implementation, preprocessing is performed on the source data for performance evaluation, specifically including: Performing missing value processing, outlier processing, and duplicate data deletion processing on the source data for performance evaluation.

[0012] In a second aspect, the technical solution of the present invention provides a system for obtaining source data for performance evaluation, including: A source data extraction module, configured to extract source data for performance evaluation from each source database respectively during a preset period, and extract one type of source data for performance evaluation from each source database. A source data preprocessing module, configured to perform preprocessing on the source data for performance evaluation. A source data storage module, configured to store the preprocessed source data for performance evaluation into each data record library according to categories, and each data record library stores one type of source data for performance evaluation. A source data acquisition module, configured to extract source data for performance evaluation from each data record library respectively for performance evaluation.

[0013] In a third aspect, the technical solution of the present invention provides a terminal, including: A memory, configured to store a program for obtaining source data for performance evaluation. A processor, configured to implement the steps of the method for obtaining source data for performance evaluation as described in any one of the above when executing the program for obtaining source data for performance evaluation.

[0014] In a fourth aspect, the technical solution of the present invention provides a computer-readable storage medium, on which a program for obtaining source data for performance evaluation is stored, and when the program for obtaining source data for performance evaluation is executed by a processor, the steps of the method for obtaining source data for performance evaluation as described in any one of the above are implemented.

[0015] A method, system, terminal, and medium for obtaining source data for performance evaluation provided by the present invention have the following beneficial effects compared with the prior art: First, relevant source data for performance evaluation are extracted from each source database during a preset period. Then, the source data is preprocessed, and the preprocessed source data is respectively stored in each data record library, and each data record library stores one type of source data. The present invention extracts source data for performance evaluation from the source database during a preset period, uniformly stores it in each data record library, and obtains the source data for performance evaluation from each data record library during performance evaluation, ensuring the data acquisition efficiency, thereby improving the statistical calculation efficiency of performance appraisal, reducing the burden on the source database, and avoiding affecting the working performance of the source database. At the same time, each data record library specifically stores one type of source data for performance evaluation, and the data storage structure is clear, which is easy to manage and maintain. When it is necessary to extract data for performance evaluation, the required data can be quickly located and obtained, improving the efficiency of data retrieval and extraction, reducing the time cost of data search and processing, and thereby improving the statistical calculation efficiency of performance appraisal. In addition, the source data for performance evaluation is extracted from each data record library respectively for performance evaluation. Since the data has been extracted, preprocessed, and classified and stored, the quality and usability of the data can be effectively guaranteed, and the accuracy of the statistical calculation of performance appraisal can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic flowchart of a method for obtaining source data for performance evaluation provided by an embodiment of the present invention.

[0018] Figure 2 It is a schematic block diagram of the structure of a system for obtaining source data for performance evaluation provided by an embodiment of the present invention.

[0019] Figure 3 It is a schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the specific embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the present invention herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention.

[0022] Figure 1 It is a schematic flowchart of a method for obtaining source data for performance evaluation provided by an embodiment of the present invention. Among them, Figure 1 The execution subject may be a system for obtaining source data for performance evaluation. The method for obtaining source data for performance evaluation provided by the embodiment of the present invention is executed by a computer device. Correspondingly, the system for obtaining source data for performance evaluation runs in the computer device. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0023] As Figure 1 shown, the method includes the following steps.

[0024] S1. Extract source data for performance evaluation from each source database respectively during a preset time period, and extract a category of source data for performance evaluation from each source database.

[0025] In this step, the preset time period is first configured. During the idle period of the source database, for different source databases, specific categories of source data for performance evaluation can be extracted respectively to achieve data collection. In this embodiment, data extraction is performed according to the preset time period, which avoids burdening the source database, and various categories of source data for performance evaluation can be collected according to requirements, reducing the redundancy and error probability of data acquisition, improving the data extraction efficiency, ensuring that the obtained data corresponds to the performance evaluation category, and providing a reliable data basis for subsequent processes.

[0026] S2. Preprocess the source data for performance evaluation.

[0027] In this step, preprocessing work is performed on the extracted source data for performance evaluation to improve data quality. The preprocessing includes processing missing values, outliers, and deleting duplicate data in the data. Through the preprocessing operation in this embodiment, errors and inconsistencies in the data are eliminated, the accuracy and availability of the data are improved, the performance evaluation deviation caused by data quality problems is reduced, and the reliability of subsequent analysis and evaluation is improved.

[0028] S3. Store the preprocessed source data for performance evaluation into each data record library according to categories, and each data record library stores a category of source data for performance evaluation.

[0029] In this step, the preprocessed data is stored in the corresponding data record libraries according to categories respectively, constructing an ordered data storage system. In this embodiment, by constructing a clear data storage structure, the convenience of data management and maintenance is improved. When it is necessary to obtain certain types of performance evaluation source data, it can be quickly located and retrieved, improving the data query efficiency and reducing the time cost of data extraction. For example, when conducting work quality evaluation, data can be quickly obtained from the corresponding work quality data record library.

[0030] S4. When conducting performance evaluation, obtain the performance evaluation source data from each data record library respectively.

[0031] In this step, when conducting performance evaluation, extract the required performance evaluation source data from each data record library to ensure the data supply for the evaluation work. In this embodiment, in the performance evaluation link, various types of required performance evaluation source data can be obtained conveniently and efficiently, ensuring the efficient progress of the evaluation work. Since the data has been extracted, preprocessed and stored classified in advance, there is no need to search and organize data in the source database, and the processed and classified data can be directly used for analysis, improving the efficiency and accuracy of performance evaluation.

[0032] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, specific examples of relevant steps are provided.

[0033] In some alternative implementation manners, step S1 extracts the performance evaluation source data from each source database respectively, which specifically includes the following steps.

[0034] S1.1, Configure the output parameters and the unified transmission input parameters; the output parameters include multiple sub-output parameters.

[0035] S1.2, Send a data extraction instruction to the source database through the output parameters and the unified transmission input parameters.

[0036] S1.3, Receive the source data packet feedback from the source database. The source database constructs each piece of data corresponding to a sub-output parameter into a source data block, and after sorting the source data blocks in a pre-agreed order, constructs all the source data blocks into a source data packet.

[0037] S1.4, Decrypt the source data packet to obtain the performance evaluation source data.

[0038] In some alternative embodiments, the source database includes a historical task processing database, a task processing log database, a task rejection record, and a quality inspection log database. The corresponding performance evaluation source data includes workload data, work timeliness data, and work quality data. That is, the workload data, work timeliness data, and work quality data are extracted from the historical task processing database, the task processing log database, the task rejection record, and the quality inspection log database respectively.

[0039] In some alternative embodiments, unified passing input parameters are configured for the historical task processing database, the task processing log database, the task rejection record, and the quality inspection log database, specifically including the assessment object type, the internal code of the assessment object, the name of the assessment object, and the assessment period. This enables each source database to perform data extraction based on these unified conditions to achieve data matching.

[0040] The historical task processing database, the task processing log database, the task rejection record, and the quality inspection log database respectively record different source data and accordingly configure different output parameters. Specifically, the output parameters of the historical task processing database include the task type, node name, processor, business group, and shared center; the output parameters of the task processing log database include the task type, task pool entry time, task bill of lading time, and task processing time; the output parameters of the task rejection record and the quality inspection log database include the task type, whether it is rejected, the node to which it is rejected, the rejecter, the rejection time, the number of rejections, whether the quality inspection is compliant, the reason for non-compliance in quality inspection, the quality inspection time, and the quality inspector.

[0041] Specifically, the steps for extracting the workload data, work timeliness data, and work quality data from the historical task processing database, the task processing log database, the task rejection record, and the quality inspection log database respectively are as follows.

[0042] Step 1, configure unified passing input parameters.

[0043] Step 2, configure the first output parameter, the second output parameter, and the third output parameter for the historical task processing database, the task processing log database, the task rejection record, and the quality inspection log database respectively; the first output parameter, the second output parameter, and the third output parameter respectively contain multiple sub-output parameters.

[0044] Step 3, construct the first data extraction instruction, the second data extraction instruction, and the third data extraction instruction by combining the unified passing input parameters with the first output parameter, the second output parameter, and the third output parameter respectively.

[0045] Step 4, send the first data extraction instruction, the second data extraction instruction, and the third data extraction instruction to the historical task processing database, the task processing log database, the task rejection record, and the quality inspection log database respectively.

[0046] Step 5: Receive the source data packets respectively feedback by the historical task handling database, task handling log database, task rejection record, and quality inspection log database.

[0047] Step 6: Parse the source data packets to obtain workload data, work efficiency data, and work quality data.

[0048] In these alternative embodiments, by configuring the output output parameters and uniformly passing the input parameters, it is possible to specify the data fields (sub-output parameters) to be extracted and the conditions for extracting data (uniformly passing the input parameters) according to different performance evaluation requirements. For example, for different types of assessment objects, assessment periods, etc., uniformly passing the input parameters are set, and each source database extracts data based on these clear conditions, so that the extracted data can accurately match the requirements of performance evaluation, avoid extracting irrelevant data, and improve the pertinence and effectiveness of the data. In addition, by configuring unified passing input parameters (type of assessment object, internal code of assessment object, name of assessment object, assessment period) for each source database, the data extracted from different source databases can be matched based on the same conditions, which helps to ensure that different types of performance evaluation source data (workload data, work efficiency data, work quality data) are consistent in key dimensions such as assessment object and assessment period, providing a reliable data basis for subsequent performance evaluation and avoiding evaluation deviations caused by inconsistent data.

[0049] In some alternative embodiments, to achieve secure data transmission, the source database constructs the data corresponding to each sub-output parameter into a source data block, sorts the source data blocks in a pre-agreed order, and then constructs all the source data blocks into a source data packet. Based on this, each source data block is encrypted, and the data packet is encrypted. During the encryption of the source data block, a key is generated according to the name of the sub-output parameter corresponding to the source data block for encryption. Correspondingly, step S1.4 decrypts the source data packet to obtain the performance evaluation source data, which specifically includes the following steps.

[0050] S1.4.1: Map and associate the sub-output parameter name with the source data block sorting in advance.

[0051] It should be noted that the sorting order of the source data blocks is agreed between the local and the source database. Based on this agreed sorting order, a mapping association between the sub-output parameter name and the source data block sorting is established locally. Exemplarily, a sub-output parameter storage table is constructed, the sub-output parameters are sorted according to the sorting order, and the sub-output parameter sorting serial number and the sub-output parameter name are added to the sub-output parameter storage table.

[0052] It should be noted that each source database corresponds to a sub-output parameter storage table.

[0053] S1.4.2: Obtain the pre-stored private key, and each source database corresponds to a private key.

[0054] S1.4.3, Decrypt the source data packet using the private key to obtain all the source data blocks in the source data packet, and parse out the sorting order of each source data block.

[0055] S1.4.4, According to the mapping association between the sub-output parameter names and the source data block sorting, obtain the sub-output parameter name corresponding to each source data block.

[0056] Exemplarily, obtain the source data block sorting serial number, and find the sub-output parameter name with the same sorting serial number from the sub-output parameter storage table, which is the sub-output parameter name corresponding to the source data block.

[0057] S1.4.5, Obtain the pre-stored first master key, and each source database corresponds to a first master key.

[0058] S1.4.6, Based on the first master key and the sub-output parameter name, generate the first sub-key for each source data block through a key derivation function.

[0059] S1.4.7, Decrypt the source data block using the first sub-key to obtain the performance evaluation source data.

[0060] In these alternative embodiments, the source database encrypts each source data block and also encrypts the data packet constructed from the source data blocks, forming a multi-level encryption protection system, improving the security of data during transmission, and effectively preventing the data from being stolen or tampered with during transmission. Moreover, when encrypting the source data block, a key is generated for encryption according to the sub-output parameter name corresponding to the source data block, and each source data block has its unique encryption key, increasing the complexity and security of encryption. Further, different sub-output parameter names correspond to different keys, and corresponding keys are generated based on specific business scenarios. This method combines with specific scenarios, further reducing the risk of data being cracked and improving the security of data transmission.

[0061] In these alternative embodiments, each source database corresponds to a sub-output parameter storage table, a private key, and a first master key, and each source database is independently managed. When a new source database needs to be added, only the corresponding sub-output parameter storage table, private key, and first master key need to be created for it, without affecting the management and operation of other databases.

[0062] In some alternative embodiments, it is adapted to extract workload data, work timeliness data, and work quality data from the historical task handling database, task handling log database, task rejection record, and quality inspection log database, and workload data record library, work timeliness data record library, and work quality data record library are set up. Correspondingly, storing the preprocessed performance evaluation source data into each data record library by category specifically includes: storing the preprocessed workload data, work timeliness data, and work quality data into the workload data record library, work timeliness data record library, and work quality data record library respectively.

[0063] In some alternative embodiments, when storing the preprocessed performance evaluation source data into each data record library by category, chunking processing and multi-layer encryption are also performed to improve the security of data transmission to the data record library. Specifically, step S3 of storing the preprocessed performance evaluation source data into each data record library by category includes the following steps.

[0064] S3.1, perform chunking processing on the preprocessed performance evaluation source data, and construct a target data block for the data corresponding to each preprocessed sub-output parameter.

[0065] S3.2, map and associate the sub-output parameter name with the target data block.

[0066] S3.3, obtain the pre-stored second master key.

[0067] S3.4, generate the second sub-key for each target data block based on the second master key and the sub-output parameter name through a key derivation function.

[0068] It should be noted that each target data block corresponds to a sub-output parameter name. For example, for the first target data block, use the second master key and the sub-output parameter name corresponding to the first target data block to generate the sub-key of the first target data block through a key derivation function, and use this sub-key to encrypt the first target data block.

[0069] S3.5, encrypt the target data block using the second sub-key.

[0070] S3.6, construct the encrypted target data block into a target data packet, and encrypt the target data packet using the pre-stored public key; each data record library corresponds to a public key.

[0071] S3.7, transmit the encrypted target data packet to the corresponding data record library for storage.

[0072] In these alternative embodiments, first, based on the second master key and the sub-output parameter name, the second sub-key of each target data block is generated through a key derivation function, and each target data block is encrypted, providing basic security protection for the data. Subsequently, the encrypted target data blocks are constructed into a target data packet, and the target data packet is encrypted again using the public key of the corresponding data record library. By means of double encryption, the difficulty of data cracking is increased, preventing the data from being stolen or tampered with during the transmission process, and improving the security of data transmission. Moreover, by using the key derivation function to generate the second sub-key based on the sub-output parameter name, each target data block has a unique encryption key according to the specific business scenario, and different sub-output parameters correspond to different keys, further enhancing the complexity and security of encryption.

[0073] Similarly, each data record library corresponds to a public key, and the second sub-key of each target data block is derived based on the second master key. The independent key management method only needs to assign a new public key to a new data record library when it is needed, without affecting the encryption and storage of other data record libraries. At the same time, if the key needs to be updated, the second master key or the public key can also be updated conveniently without making large-scale modifications to the entire system.

[0074] Based on the above-mentioned performance evaluation source data acquisition method of the embodiment, a performance evaluation method is provided to further understand the performance evaluation source data acquisition method. The performance evaluation method includes the following steps.

[0075] SS1. Set the performance appraisal frequency, appraisal scope, and appraisal index library to build a performance appraisal system.

[0076] The content that can be supported for setting at least includes: performance appraisal period (monthly / quarterly / yearly), performance indicators (qualitative and quantitative evaluation dimensions, calculation rules, calculation methods, etc.), performance influencing factors (document type influencing factors, task type influencing factors, etc.), performance plans (scope of application, calculation method, index weight, etc.), performance statistical objects (specific shared centers or business groups or operators), and performance indicator standard values (target values expected for different performance indicators). By setting the above content, the construction of the performance appraisal system is realized, the performance appraisal process is standardized and digitized, and the accuracy of performance appraisal data is improved.

[0077] SS2. Record the performance-related raw data generated when the performance appraisal object processes documents in real time to generate a data record library.

[0078] The workload data repository records the original data: It is applicable to the workload dimension indicators and is used to record the original data mainly related to the workload such as the quantity of tasks processed and the quantity of documents. The data source of this repository is the historical task processing database. Based on the data extraction module of the data integration platform, by passing in parameters such as the type of assessment object, the internal code of the assessment object, the name of the assessment object, and the assessment period, the original detailed workload data is obtained. Then, based on the data processing module, after performing missing value processing, outlier processing, and duplicate data deletion processing on the data, the data with output parameters such as task type, node name, handler, business group, and shared center is stored in the workload data repository.

[0079] The work timeliness data repository records the original data: It is applicable to the work timeliness dimension indicators and is used to record the original data mainly related to timeliness such as the completion time of processing, the order locking time, and the time of entering the pool. Additionally, in this type of data repository, the working calendar also needs to be obtained, and then based on the working calendar, the timeliness data including non-working days and the relevant original data excluding non-working days are recorded. The data source of this repository is the task processing log database. Based on the data extraction module of the data integration platform, by passing in parameters such as the type of assessment object, the internal code of the assessment object, the name of the assessment object, and the assessment period, the original detailed work timeliness data is obtained. Then, based on the data processing module, after performing missing value processing, outlier processing, and duplicate data deletion processing on the data, the data with output parameters such as task type, task entry time into the pool, task bill of lading time, and task processing time is stored in the work timeliness data repository.

[0080] The work quality data repository records the original data: It is applicable to the work quality dimension indicators and is used to record the original data mainly related to quality such as whether there is a rejected order and whether there is non-compliance in quality inspection. The data source of this repository is the task rejection record and quality inspection log database. Based on the data extraction module of the data integration platform, by passing in parameters such as the type of assessment object, the internal code of the assessment object, the name of the assessment object, and the assessment period, the original detailed work quality data is obtained. Then, based on the data processing module, after performing missing value processing, outlier processing, and duplicate data deletion processing on the data, the data with output parameters such as task type, whether it is rejected, the node to which it is rejected, the rejecter, the rejection time, the number of rejections, whether the quality inspection is compliant, the reason for non-compliance in quality inspection, the quality inspection time, and the quality inspector is stored in the work timeliness data repository.

[0081] Report and generate the original data of different types of assessment objects: The types of assessment objects for the performance assessment of the shared center services include the shared center, business groups, and operators. The performance record data of the operators needs to be reported and generated into the actual data of the corresponding business group for recording, and the actual record data of the business group needs to be reported and generated into the actual data of the corresponding shared center for recording.

[0082] This step is implemented based on the method for obtaining the source data of performance evaluation in the above-mentioned embodiment and will not be elaborated here.

[0083] For SS3, the actual data of each performance appraisal object on each performance indicator is calculated.

[0084] Calculation of the actual data of workload dimension indicators: Obtain relevant original data records from the workload data repository that meet the established calculation rules corresponding to each performance indicator. Based on these original data, calculate the actual data of relevant indicators according to the calculation rules, such as the task handling volume indicator, the document handling volume indicator, etc. When calculating the actual data of such indicators, it is generally calculated by the total amount, and the formula is as follows: Actual data of workload dimension indicators = Total workload in the original data records that meet the indicator calculation rule conditions.

[0085] Calculation of the actual data of work timeliness dimension indicators: Obtain relevant original data records from the work timeliness data repository that meet the established calculation rules corresponding to each performance indicator. Based on these original data, calculate the actual data of relevant indicators according to the calculation rules, such as the handling timeliness indicator, the average processing time per order indicator, etc. For such indicators, it is also necessary to obtain the working calendar, and then obtain the working day duration and non-working day duration data based on the working calendar. When calculating the actual data of such indicators, it is generally calculated by the average timeliness, and the formula is as follows: Actual data of work timeliness dimension indicators (including non-working days) = Total duration including non-working days in the original data records that meet the indicator calculation rule conditions ÷ Total number of original data records that meet the indicator calculation rule conditions.

[0086] Actual data of work timeliness dimension indicators (excluding non-working days) = Total duration excluding non-working days in the original data records that meet the indicator calculation rule conditions ÷ Total number of original data records that meet the indicator calculation rule conditions.

[0087] Calculation of the actual data of work quality dimension indicators: Obtain relevant original data records from the work quality data repository that meet the established calculation rules corresponding to each performance indicator. Based on these original data, calculate the actual data of relevant indicators according to the calculation rules, such as the order return rate indicator, the quality inspection compliance rate indicator, etc. When calculating the actual data of such indicators, it is generally calculated by ratio, and the proportion is calculated based on the number of order returns or non-compliances, and the formula is as follows: Actual data of work quality dimension indicators = Total number of order returns or non-compliances in the original data records that meet the indicator calculation rule conditions ÷ Total number of original data records that meet the indicator calculation rule conditions (including: order returns / non-compliances; non-order returns / compliances).

[0088] Report the actual data of indicators at different levels: For the actual data of indicators in each dimension as described in the above steps, first calculate the data at the operator level, and then report and generate the actual data of indicators at the business group level and the shared center level in sequence.

[0089] SS4. Calculate the converted data corresponding to each indicator according to the influencing factors and conversion coefficients of each performance indicator.

[0090] 1) Calculation of the converted data of indicators in the workload dimension: The converted data of the indicator = the actual workload data that meets the influencing factors in the workload data repository multiplied by the conversion coefficient + the actual indicator data that does not meet the influencing factors.

[0091] 2) Calculation of the converted data of indicators in the work timeliness dimension: The converted data of the indicator (including non-working days) = (the actual working hours data that meets the influencing factors and includes non-working days in the work timeliness data repository multiplied by the conversion coefficient + the actual duration data that does not meet the influencing factors and includes non-working days) ÷ the total amount of data in the work timeliness repository that meets the indicator calculation rule conditions.

[0092] The calculation logic of the converted data of the indicator (excluding non-working days) is the same as above.

[0093] 3) Calculation of the converted data of indicators in the work quality dimension: The converted data of the indicator = (the actual number of rejected orders or non-compliances that meet the influencing factors in the workload data repository multiplied by the conversion coefficient + the actual number of rejected orders or non-compliances that do not meet the influencing factors) ÷ (the actual number of compliant and non-compliant cases that meet the influencing factors in the workload data repository multiplied by the conversion coefficient + the actual number of compliant and non-compliant cases that do not meet the influencing factors).

[0094] 4) Report and generate the converted data of indicators at different levels: For the converted data of indicators in each dimension as described in the above steps, first calculate the data at the operator level, and then report and generate the converted data of indicators at the business group level and the shared center level in sequence.

[0095] SS5. Conduct performance appraisals according to the target standard values of each performance indicator corresponding to each performance statistical object by performance period, and obtain the performance scores, performance levels, performance rankings, etc. of each performance statistical object.

[0096] Calculate the difference value between the actual value and the converted data of the performance indicator for comparison, that is, the converted data of the indicator minus the actual value of the indicator.

[0097] Calculate the difference value between the standard value and the converted data of the performance indicator for comparison, that is, the converted data of the indicator minus the standard value of the indicator.

[0098] For positive indicators (such as the workload of handling), when the converted data is greater than the standard value, points should be added, and vice versa, points should be deducted. The rules for adding or deducting points can be processed in two ways: 'points scored per * unit' or 'one-time assessment score'.

[0099] For negative indicators (such as the average handling time per order), when the converted data is greater than the standard value, points should be deducted, and vice versa, points should be added.

[0100] Based on the standard score line, points are added or deducted in the above steps to obtain the performance score of the statistical object (shared center organization or personnel) through assessment.

[0101] Based on the performance score obtained through assessment, the adjusted score is automatically generated relying on the performance adjustment rule system.

[0102] Based on the mapping relationship between the adjusted performance score and the performance level, the performance level of the statistical object is obtained through assessment.

[0103] According to dimensions such as unit, department, or group, the adjusted performance assessment scores and assessment levels of all statistical objects are compared to obtain the performance ranking.

[0104] SS6, based on the relevant data of performance assessment, intelligently generates the performance report for the corresponding period.

[0105] In the above text, an embodiment of a method for obtaining source data for performance evaluation has been described in detail. Based on the method for obtaining source data for performance evaluation described in the above embodiment, an embodiment of the present invention also provides a system for obtaining source data for performance evaluation corresponding to this method.

[0106] Figure 2 The following is a schematic block diagram of the structure of a system for obtaining source data for performance evaluation provided by an embodiment of the present invention. The system 200 for obtaining source data for performance evaluation can be divided into multiple functional modules according to the functions it executes. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory.

[0107] The source data extraction module 210 is used to extract the source data for performance evaluation from each source database respectively during a preset period, and each source database extracts one type of source data for performance evaluation.

[0108] The source data preprocessing module 220 is used to preprocess the source data for performance evaluation.

[0109] The source data storage module 230 is used to store the preprocessed source data for performance evaluation into each data record library according to categories, and each data record library stores one type of source data for performance evaluation.

[0110] The source data acquisition module 240 is used to extract the performance evaluation source data from each data record library respectively for performance evaluation.

[0111] In some alternative embodiments, the source data extraction module 210 extracts the performance evaluation source data from each source database respectively, specifically including: configuring output parameters and uniformly transmitting input parameters; the output parameters include multiple sub-output parameters; sending a data extraction instruction to the source database through the output parameters and the uniformly transmitted input parameters; receiving the source data packet feedback by the source database, and the source database constructs the data corresponding to each sub-output parameter into a source data block, and after sorting the source data blocks in a pre-agreed order, constructs all the source data blocks into a source data packet; decrypting the source data packet to obtain the performance evaluation source data.

[0112] In some alternative embodiments, the source data extraction module 210 decrypts the source data packet to obtain the performance evaluation source data, specifically including: mapping and associating the sub-output parameter names with the source data block sorting in advance; obtaining the pre-stored private key, and each source database corresponds to a private key; using the private key to decrypt the source data packet to obtain all the source data blocks in the source data packet and parsing out the sorting order of each source data block; obtaining the sub-output parameter name corresponding to each source data block according to the mapping association between the sub-output parameter name and the source data block sorting; obtaining the pre-stored first master key, and each source database corresponds to a first master key; generating the first sub-key of each source data block through a key derivation function based on the first master key and the sub-output parameter name; using the first sub-key to decrypt the source data block to obtain the performance evaluation source data.

[0113] In some alternative embodiments, the source data storage module 230 stores the preprocessed performance evaluation source data into each data record library respectively according to categories, specifically including: performing block processing on the preprocessed performance evaluation source data, and constructing the data corresponding to each preprocessed sub-output parameter into a target data block; mapping and associating the sub-output parameter name with the target data block; obtaining the pre-stored second master key; generating the second sub-key of each target data block through a key derivation function based on the second master key and the sub-output parameter name; using the second sub-key to encrypt the target data block; constructing the encrypted target data blocks into a target data packet and encrypting the target data packet with the pre-stored public key; each data record library corresponds to a public key; transmitting the encrypted target data packet to the corresponding data record library for storage.

[0114] In some alternative embodiments, the source data preprocessing module 220 preprocesses the performance evaluation source data, specifically including: performing missing value processing, outlier processing, and duplicate data deletion processing on the performance evaluation source data.

[0115] The performance evaluation source data acquisition system of this embodiment is used to implement the aforementioned performance evaluation source data acquisition method. Therefore, the specific implementation in this system can be seen in the embodiment part of the performance evaluation source data acquisition method in the previous text. Therefore, its specific implementation can refer to the descriptions of the corresponding individual part embodiments and will not be elaborated here.

[0116] In addition, since the performance evaluation source data acquisition system of this embodiment is used to implement the aforementioned performance evaluation source data acquisition method, its function corresponds to the function of the above method and will not be elaborated here.

[0117] Figure 3 FIG. 7 is a schematic structural diagram of a terminal 300 provided by an embodiment of the present invention, including: a processor 310, a memory 320, and a communication unit 330. When the processor 310 is used to implement the performance evaluation source data acquisition program stored in the memory 320, the following steps are implemented: Extract performance evaluation source data from each source database respectively during a preset period, and extract one type of performance evaluation source data from each source database; Preprocess the performance evaluation source data; Store the preprocessed performance evaluation source data into each data record library respectively according to the category, and each data record library stores one type of performance evaluation source data; When performing performance evaluation, obtain performance evaluation source data from each data record library respectively.

[0118] The present invention also provides a computer storage medium, and the storage medium here may be a magnetic disk, an optical disk, a read-only memory (abbreviation in English: read-only memory, ROM for short), or a random access memory (abbreviation in English: random access memory, RAM for short), etc.

[0119] The computer storage medium stores a performance evaluation source data acquisition program, and when the performance evaluation source data acquisition program is executed by a processor, the following steps are implemented: Extract performance evaluation source data from each source database respectively during a preset period, and extract one type of performance evaluation source data from each source database; Preprocess the performance evaluation source data; Store the preprocessed performance evaluation source data into each data record library respectively according to the category, and each data record library stores one type of performance evaluation source data; When performing performance evaluation, obtain performance evaluation source data from each data record library respectively.

[0120] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for obtaining source data of performance evaluation, characterized in that, It includes the following steps: Extract performance evaluation source data from each source database during a preset period, and extract one type of performance evaluation source data from each source database; Preprocess the performance evaluation source data; Store the preprocessed performance evaluation source data into each data record library by category, and each data record library stores one type of performance evaluation source data; When performing performance evaluation, obtain performance evaluation source data from each data record library respectively.

2. The method for obtaining performance evaluation source data according to claim 1, wherein, Extract performance evaluation source data from each source database respectively, specifically including: Configure output parameters and unified transmission input parameters; the output parameters include multiple sub-output parameters; Send a data extraction instruction to the source database through the output parameters and the unified transmission input parameters; Receive the source data packet feedback by the source database. The source database constructs the data corresponding to each sub-output parameter into a source data block, sorts the source data blocks in a pre-agreed order, and then constructs all the source data blocks into a source data packet; Decrypt the source data packet to obtain the performance evaluation source data.

3. The method for obtaining performance evaluation source data according to claim 2, wherein Decrypt the source data packet to obtain the performance evaluation source data, specifically including: Pre-map the sub-output parameter names to the source data block sorting; Obtain the pre-stored private key, and each source database corresponds to a private key; Use the private key to decrypt the source data packet to obtain all the source data blocks in the source data packet, and parse out the sorting order of each source data block; According to the mapping association between the sub-output parameter name and the source data block sorting, obtain the sub-output parameter name corresponding to each source data block; Obtain the pre-stored first master key, and each source database corresponds to a first master key; Based on the first master key and the sub-output parameter name, generate the first sub-key for each source data block through a key derivation function; Use the first sub-key to decrypt the source data block to obtain the performance evaluation source data.

4. The method for obtaining performance evaluation source data according to claim 3, wherein Store the preprocessed performance evaluation source data into each data record library by category, specifically including: Perform block processing on the preprocessed performance evaluation source data, and construct the data corresponding to each preprocessed sub-output parameter into a target data block; Map the sub-output parameter name to the target data block; Obtain the pre-stored second master key; Based on the second master key and the sub-output parameter name, generate the second sub-key for each target data block through a key derivation function; Use the second sub-key to encrypt the target data block; Construct the encrypted target data blocks into a target data packet, and encrypt the target data packet with the pre-stored public key; each data record library corresponds to a public key; Transmit the encrypted target data packet to the corresponding data record library for storage.

5. The method for obtaining performance evaluation source data according to any one of claims 2 to 4, characterized in that The source database includes a historical task handling database, a task handling log database, a task rejection record, and a quality inspection log database; The performance evaluation source data includes workload data, work timeliness data, and work quality data.

6. The method for obtaining performance evaluation source data according to claim 5, characterized in that The unified transmission input parameters include the assessment object type, the internal code of the assessment object, the name of the assessment object, and the assessment period; The output parameters of the historical task handling database include the task type, node name, handler, business group, and shared center; The output parameters of the task handling log database include the task type, task pool entry time, task bill of lading time, and task handling time; The output parameters of the task rejection record and quality inspection log database include task type, whether it is rejected, the node to which it is rejected, the rejecter, the rejection time, the number of rejections, whether the quality inspection is compliant, the reasons for non-compliance in quality inspection, the quality inspection time, and the quality inspector.

7. The method for obtaining performance evaluation source data according to claim 6, wherein Preprocess the source data for performance evaluation, specifically including: Perform missing value processing, outlier processing, and duplicate data deletion processing on the source data for performance evaluation.

8. A performance evaluation source data acquisition system, characterized in that, Including: A source data extraction module for extracting source data for performance evaluation from each source database at preset time intervals, and extracting one type of source data for performance evaluation from each source database; A source data preprocessing module for preprocessing the source data for performance evaluation; A source data storage module for storing the preprocessed source data for performance evaluation into each data record library by category, and storing one type of source data for performance evaluation in each data record library; A source data acquisition module for extracting source data for performance evaluation from each data record library for performance evaluation.

9. A terminal, characterized in that, Including: A memory for storing a program for acquiring source data for performance evaluation; A processor for implementing the steps of the method for acquiring source data for performance evaluation as described in any one of claims 1 to 7 when executing the program for acquiring source data for performance evaluation.

10. A computer-readable storage medium, characterized in that, A program for acquiring source data for performance evaluation is stored on the readable storage medium, and when the program for acquiring source data for performance evaluation is executed by the processor, the steps of the method for acquiring source data for performance evaluation as described in any one of claims 1 to 7 are implemented.

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