Automatic report generation system and method for score of performance index
By collecting, preprocessing and quantitatively analyzing performance data, and using blockchain technology to encrypt and store evidence, the problem of insufficient data security and prediction accuracy in the existing technology is solved, and efficient and credible performance management and decision-making is achieved.
Patent Information
- Application Number
- CN202411952070.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-05-13
AI Technical Summary
The existing performance indicator scoring methods have shortcomings in data acquisition, preprocessing and quantitative analysis, resulting in low data security, completeness and accuracy of prediction results, and a lack of transparent audit trail.
The original performance data is collected through the data interface, combined with historical data for preprocessing and quantitative analysis, and used blockchain technology to encrypt and store evidence, and automatically generate comprehensive performance reports to record the generation, modification and distribution of reports.
It improves the efficiency of data processing and prediction accuracy, ensures data security and immutability, realizes transparency and traceability of report generation and distribution, and provides reliable audit support for enterprises.
Smart Images
Figure CN119990860A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent business management, and in particular to a system and method for automatically generating a report for performance indicator scoring. Background Art
[0002] With the rapid development of information technology, performance management has gradually become an important part of corporate operations. Traditional performance evaluation relies on manual aggregation, data integration and report generation, which has problems such as low efficiency, poor data reliability and cumbersome calculations. In recent years, performance evaluation methods based on big data analysis, machine learning and automation technology have gradually been applied to corporate management, especially through the integration of data interfaces and business platforms, which can collect and process original performance data in real time. At the same time, with the rise of blockchain technology, its unique advantages in data storage, encryption and verification have significantly improved the reliability and security of performance data, and can provide companies with more transparent and reliable performance reports.
[0003] Although the existing performance indicator scoring methods have covered the links of data collection, preprocessing and quantitative analysis, there are still many deficiencies in practical applications. The existing automated report generation system lacks sufficient verification and encryption of data, and it is difficult to guarantee data integrity and security, and fails to effectively prevent the risk of data tampering. Many traditional methods fail to combine historical performance data for effective multi-dimensional analysis during data processing, resulting in low accuracy of prediction results. The performance report generation and distribution process in the existing technology lacks transparent audit tracking and cannot provide users with a complete data modification and distribution history record. In view of the above technical limitations, by combining blockchain technology to encrypt and store performance data, and combining historical data for more accurate predictions, the automation level of report generation, data security and traceability can be effectively improved. Summary of the invention
[0004] In view of the above existing problems, the present invention is proposed.
[0005] Therefore, the present invention provides a method for automatically generating a report for performance indicator scores to solve the problem of low accuracy of prediction results of traditional methods.
[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:
[0007] In a first aspect, the present invention provides a method for automatically generating reports for performance indicator scores, which includes collecting original performance data from a business platform through a data interface, and preprocessing the data to generate preprocessed performance data; inputting the preprocessed performance data into a quantitative model, combining historical performance data, and predicting and generating performance indicator scores; verifying and encrypting the performance indicator scores and preprocessed performance data through a blockchain algorithm, and obtaining blockchain encrypted evidence data; automatically generating a comprehensive performance report based on the blockchain encrypted evidence data, and distributing the comprehensive performance report to different users; recording the generation, modification and distribution of the comprehensive performance report as log information, and recording and storing it through the blockchain.
[0008] As a preferred solution of the method for automatically generating reports for performance indicator scores described in the present invention, the business platform includes an enterprise resource planning platform, a customer relationship management platform, a sales data management platform and an enterprise attendance management platform.
[0009] As a preferred solution of the method for automatically generating a report for performance indicator scores of the present invention, the method collects original performance data from the business platform through a data interface, pre-processes the data, and generates pre-processed performance data. The specific steps are:
[0010] Select data interface standards based on the type of business data stored in each business platform;
[0011] Perform data interface connection and configuration based on the selected data interface standard;
[0012] Collect original performance data from the business platform through the configured data interface;
[0013] Fill missing values, remove duplicates and detect outliers on the collected original performance data;
[0014] Standardize raw performance data from different sources and formats into a unified data format, perform data fusion and conversion, and generate pre-processed performance data;
[0015] The pre-processed performance data is stored in a database.
[0016] As a preferred solution of the method for automatically generating a report for performance indicator scores of the present invention, the pre-processed performance data is input into the quantitative model, combined with the historical performance data, to predict and generate the performance indicator scores, the specific steps are as follows:
[0017] Extract preprocessed performance data from the database, use principal component analysis algorithm to screen features, and generate combined features through feature interaction;
[0018] The features are standardized and normalized through the Z-score algorithm and the min-max algorithm, and the processed features are formatted to generate a performance feature matrix;
[0019] Based on the deep neural network architecture and introducing an adaptive weighting mechanism, a performance indicator scoring quantitative model is constructed by combining the performance feature matrix and historical performance data;
[0020] Based on the constructed performance indicator score quantification model, the mean square error is selected as the loss function, and the gradient descent method is used for model training;
[0021] The preprocessed performance data is input into the trained performance indicator score quantification model to predict the performance indicator score.
[0022] As a preferred solution of the method for automatically generating a report for performance indicator scores of the present invention, the specific steps of verifying and encrypting the performance indicator scores and pre-processed performance data through a blockchain algorithm to obtain blockchain encrypted evidence data are as follows:
[0023] Use the SHA-256 hash algorithm to verify the performance indicator score and pre-processed performance data and calculate the data hash value;
[0024] Use RSA asymmetric encryption algorithm to encrypt performance indicator scores and pre-processed performance data;
[0025] Create new transactions in the blockchain through smart contracts, packaging data hashes, encrypted data and other related information into transactions;
[0026] Use the blockchain data interface to send the transaction to the blockchain node in the network for verification and obtain the block hash value;
[0027] Based on the block hash value, the encrypted data and the data hash value are combined to generate blockchain encrypted evidence data records.
[0028] As a preferred solution of the method for automatically generating a report for performance indicator scores described in the present invention, the comprehensive performance report is automatically generated based on the encrypted evidence data of the blockchain, and the comprehensive performance report is distributed to different users. The specific steps are as follows:
[0029] Use the query function of the blockchain smart contract to retrieve the stored data hash value and encrypted data through the block hash value;
[0030] The performance index score and pre-processed performance data are decrypted through the RSA asymmetric decryption algorithm, and the integrity and authenticity of the decrypted data are verified through the data hash value;
[0031] Based on the decrypted data, the comprehensive performance index is calculated;
[0032] Generate a comprehensive performance report based on the calculated comprehensive performance index;
[0033] Use smart contracts to allocate user rights and distribute comprehensive performance reports to different users based on user rights and needs.
[0034] As a preferred solution of the method for automatically generating a report for performance indicator scores of the present invention, the generation, modification and distribution of the comprehensive performance report is recorded as log information, which is recorded and stored through the blockchain. The specific steps are:
[0035] Define the log information recording structure, record the behavior and generate log information when generating, modifying and distributing behaviors;
[0036] Based on the generated log information, a hash value is calculated by a hash algorithm, and the log information is encrypted;
[0037] Through smart contracts, encrypted log information and hash values are stored in the blockchain network to obtain transaction hash values.
[0038] In a second aspect, the present invention provides a system for automatically generating reports for performance indicator scores, comprising a data acquisition module, a quantitative model module, a block verification module, a report processing module and an audit tracing module; the data acquisition module is used to collect original performance data from a business platform through a data interface, and pre-process the data to generate pre-processed performance data; the quantitative model module is used to input the pre-processed performance data into a quantitative model, and predict and generate performance indicator scores in combination with historical performance data; the block verification module is used to verify and encrypt the performance indicator scores and pre-processed performance data through a blockchain algorithm, and obtain blockchain encrypted evidence data; the report processing module is used to automatically generate a comprehensive performance report based on the blockchain encrypted evidence data, and distribute the comprehensive performance report to different users; the audit tracing module is used to record the generation, modification and distribution of the comprehensive performance report as log information, and record and store it through the blockchain.
[0039] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for automatically generating a report for performance indicator scores as described in the first aspect of the present invention.
[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program, when executed by a processor, implements any step of the method for automatically generating a report for performance indicator scores as described in the first aspect of the present invention.
[0041] The beneficial effects of the present invention are as follows: by automatically collecting and preprocessing performance data, combining quantitative models and historical data to predict performance indicator scores, data processing efficiency and prediction accuracy are improved. Through blockchain encryption and evidence storage technology, the immutability and credibility of performance data are ensured, avoiding the risk of data tampering or leakage. Automatically generating and distributing performance reports improves the efficiency of report generation and distribution, while ensuring the timeliness and accuracy of information. By recording all report generation, modification and distribution behaviors through blockchain, the traceability and transparency of operations are achieved, providing reliable audit support for enterprises. By improving the degree of automation, security and transparency, it helps enterprises achieve efficient and reliable performance management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0043] Figure 1 This is a flow chart of the method for automatically generating a report for performance indicator scores in Example 1.
[0044] Figure 2 This is a schematic diagram of a system for automatically generating reports for performance indicator scores in Example 1. DETAILED DESCRIPTION
[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.
[0046] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for automatically generating a report for performance indicator scores, comprising the following steps:
[0047] S1: Collect original performance data from the business platform through the data interface, pre-process the data, and generate pre-processed performance data.
[0048] Specifically, the following steps are included:
[0049] S1.1: Select data interface standards based on the type of business data stored in each business platform.
[0050] It should be noted that the types of business data stored include employee performance data, sales, customer satisfaction and employee attendance data, and the data interface standards include REST API and SOAP API.
[0051] S1.1.1: Business platforms include enterprise resource planning platform, customer relationship management platform, sales data management platform and enterprise attendance management platform.
[0052] S1.2: Perform data interface connection and configuration based on the selected data interface standard.
[0053] Specifically, configure the authentication mechanism for API access to ensure data access rights, configure the interface request type (such as GET, POST) and the data path that needs to be accessed (such as the specific URL for obtaining performance data), and set the frequency of data collection and the time range for acquisition.
[0054] S1.3: Collect original performance data from the business platform through the configured data interface.
[0055] Specifically, specific data fields extracted from the business platform, such as employee ID, name, job information, score, number of completed tasks, etc. or sales indicators, target achievement rate, customer feedback, etc.
[0056] The result of data collection is a structured dataset organized in JSON format.
[0057] S1.4: Fill missing values, remove duplicates and detect outliers on the collected original performance data.
[0058] Specifically, according to the missing situation, use mean filling, zero filling or delete the records with missing values; check and delete duplicate records to ensure the uniqueness of the data; use the box plot method to identify and process abnormal data.
[0059] S1.5: Standardize raw performance data from different sources and formats into a unified data format, and perform data fusion and conversion to generate pre-processed performance data.
[0060] Specifically, format the date and time fields to ensure consistency; label encode or one-hot encode the categorical data to facilitate subsequent processing. For example, the time format is unified as YYYY-MM-DD, and the numeric format is unified as floating type.
[0061] Data fusion and conversion means that the performance data collected from multiple business platforms may contain different data fields, which need to be fused and converted. At this time, business rules or external data sources are used to supplement incomplete fields. For example, employee information from different business platforms can be merged, matched by employee ID, and missing employee data can be supplemented; data from different time periods (such as quarterly data and annual data) can be merged to ensure data consistency.
[0062] S1.6: Pre-process the performance data and store it in the database.
[0063] It should be understood that the pre-processed performance data is stored in the MySQL database to facilitate subsequent processing and analysis.
[0064] Preferably, by selecting appropriate data interface standards, the compatibility and integration of multi-platform data is achieved, while ensuring the security and reliability of data transmission. After data collection, the integrity and accuracy of the data are guaranteed through missing value filling, deduplication and outlier detection. Data standardization and fusion processing ensure the consistency of data from different sources, so that performance data from different platforms can be seamlessly connected and converted into a unified format. The pre-processed data is stored in the database for subsequent analysis and processing, providing a solid data foundation for the calculation of performance indicators and report generation, thereby providing enterprises with more accurate and reliable performance evaluation support.
[0065] S2: Input the preprocessed performance data into the quantitative model, combine it with historical performance data, and predict and generate performance indicator scores.
[0066] Specifically, the following steps are included:
[0067] S2.1: Extract preprocessed performance data from the database, use the principal component analysis algorithm for feature screening, and generate combined features through feature interaction.
[0068] Specifically, the principal component analysis algorithm is used to screen all performance data features and remove redundant or low-relevance features. Different business features are combined through feature interaction. For example, the product of sales and customer satisfaction can be used as a new combined feature.
[0069] S2.2: Standardize and normalize the features through the Z-score algorithm and min-max algorithm, format the processed features, and generate a performance feature matrix.
[0070] Specifically, in order to ensure that each feature of the data is processed at the same scale, the features need to be standardized and normalized. The Z-score algorithm converts each feature into features of the same scale by standardizing it to zero mean and unit variance. The min-max algorithm scales the data to [0,1], which helps prevent certain features from dominating the model's learning process due to their large scale.
[0071] S2.3: Based on the deep neural network architecture and introducing the adaptive weighting mechanism, combined with the performance feature matrix and historical performance data, a performance indicator score quantitative model is constructed, expressed as:
[0072]
[0073] in, is the predicted performance indicator score, f is the Sigmoid output layer activation function, n is the number of input features, i is the index of the number of input features, and w i is the weight coefficient of the i-th input feature, x i is the i-th input feature, m is the number of historical performance data, j is the index of the number of historical performance data, α j is the weighting coefficient of historical performance data, is the Gaussian kernel function, a is the integral lower bound of the Gaussian kernel function, b is the integral upper bound of the Gaussian kernel function, e is a natural constant, β is the smoothing factor of the Gaussian function, z is the time gap of historical data, and dz is the integral differential element.
[0074] Preferably, a multi-level adaptive weighted quantization model is introduced, which introduces trainable weighting coefficients in the feature processing of each layer and has the ability to adaptively learn feature weights. The specific architecture is:
[0075] Input layer: processes the preprocessed performance feature matrix;
[0076] Hidden layer: It consists of multiple fully connected layers. The activation function of each layer is ReLU (Rectified Linear Unit) to increase the nonlinear fitting ability.
[0077] Output layer: predict the performance indicator score through the regression model.
[0078] The output of each hidden layer is not only the traditional weighted sum (linear combination), but a weighted nonlinear combination, which can model the relationship between more complex business rules and data.
[0079] S2.4: Based on the constructed performance indicator score quantification model, the mean square error is selected as the loss function, and the gradient descent method is used for model training.
[0080] Specifically, the training process is optimized by gradient descent method (such as Adam optimizer) to minimize the mean square error (MSE) between the predicted performance score and the actual score. During the training process, the optimization goal is to make the model adaptively adjust the weight coefficient w of each input feature. i and historical performance data weighting coefficient α j , to ensure that the predicted performance indicator scores are as accurate as possible.
[0081] S2.5: Input the preprocessed performance data into the trained performance indicator score quantification model to predict the performance indicator score.
[0082] Specifically, for each set of input data, the prediction result will be output. The score will be a value between [0,1], indicating the relative score of performance completion. The closer the score is to 1, the better the performance; the closer it is to 0, the worse the performance.
[0083] Preferably, an optimized performance feature matrix is generated through principal component analysis and feature interaction, and then standardization and normalization are used to ensure that the data is processed at the same scale. In the quantitative model, an adaptive weighting mechanism is introduced so that the model can dynamically adjust the weights of features and historical data according to data changes, thereby improving the accuracy and adaptability of the prediction. The Gaussian kernel function is used to deal with the nonlinear effects of historical performance data, further enhancing the accuracy of the model. Through the multi-layer fully connected architecture of the deep neural network, the model can effectively capture the nonlinear relationship between complex business rules and data, and provide more accurate performance score predictions. Compared with traditional methods, this method not only improves the accuracy of the model, but also enhances its flexibility and scalability, and can better adapt to dynamically changing business needs.
[0084] S3: Through the blockchain algorithm, the performance indicator scores and pre-processed performance data are verified and encrypted to obtain blockchain encrypted evidence data.
[0085] Specifically, the following steps are included:
[0086] S3.1: Use the SHA-256 hash algorithm to verify the performance indicator score and pre-processed performance data and calculate the data hash value.
[0087] Specifically, the SHA-256 hash algorithm is used to verify the preprocessed performance data and performance indicator scores to generate the hash value of the data. For example, a unique data fingerprint is generated using the hash algorithm for the performance data and score information. This ensures that the data has not been tampered with before being stored in the blockchain. The hash value calculation expression is:
[0088] H(D) = SHA-256(D);
[0089] Wherein, H(D) is the hash value of data D, D is the performance indicator score and preprocessed performance data, and SHA-256(D) is the SHA-256 hash algorithm applied to data D to generate a hash value of 256 bits (32 bytes).
[0090] S3.2: Use the RSA asymmetric encryption algorithm to encrypt the performance indicator scores and pre-processed performance data.
[0091] Specifically, to ensure the confidentiality of data when it is stored in the blockchain, it is encrypted using the RSA asymmetric encryption algorithm. The data is encrypted using the recipient's public key, and only the recipient's private key can decrypt it. The encryption process is expressed as:
[0092] C=E public (D);
[0093] Among them, C is the encrypted data, D is the performance index score and pre-processed performance data, and E is public The process of using a public key to perform encryption operations.
[0094] S3.3: Create a new transaction in the blockchain through a smart contract, packaging the data hash value, encrypted data and other related information into the transaction.
[0095] Specifically, each transaction will generate a block on the blockchain, which contains the following content:
[0096] Transaction data: encrypted performance data and score information.
[0097] Data hash value: ensures data integrity and consistency.
[0098] Timestamp: records the time point of data storage to ensure the time chain of data.
[0099] Signature information: The digital signature of the data sender confirms the legitimacy of the data source.
[0100] S3.4: Use the blockchain data interface to send the transaction to the blockchain node in the network for verification and obtain the block hash value.
[0101] Specifically, the blockchain data interface refers to the API of the blockchain platform (such as the Fabric SDK provided by Hyperledger Fabric). Once a transaction reaches consensus in the blockchain network and is officially written into the blockchain, the blockchain will return confirmation information of the transaction, including the block hash value. Through the block hash value, users and systems can trace the authenticity and time point of data evidence at any time.
[0102] S3.5: Based on the block hash value, the encrypted data and the data hash value are combined to generate a blockchain encrypted evidence data record.
[0103] Specifically, the generated evidence data record will include the following information:
[0104] Encrypted data: encrypted performance data and indicator scores;
[0105] Data hash value: ensures data integrity;
[0106] Digital signature: ensure the legitimacy of data source;
[0107] Block hash value: The hash value of the block to which the data belongs when it is stored, which is used to trace the blockchain location of the data;
[0108] Timestamp: Ensures data traceability.
[0109] Generate an encrypted evidence data record for each piece of data, and save the evidence data record in the database for subsequent verification, query and audit.
[0110] Preferably, the SHA-256 hash algorithm is used to verify the performance data and generate a unique hash value to ensure data integrity. The RSA asymmetric encryption algorithm is used to encrypt the data to ensure data confidentiality and security. The encrypted data and hash value are packaged into transactions and written into the blockchain through smart contracts, and the credibility of the data is guaranteed with the help of a decentralized verification mechanism. The data storage process is recorded through block hash values and timestamps, providing transparent traceability and supporting subsequent verification and auditing. The solution improves the security, transparency and reliability of data processing, and meets the high standards of modern enterprises for data security.
[0111] S4: Based on blockchain encrypted evidence data, comprehensive performance reports are automatically generated and distributed to different users.
[0112] Specifically, the following steps are included:
[0113] S4.1: Use the query function of the blockchain smart contract to retrieve the stored data hash value and encrypted data through the block hash value.
[0114] Specifically, based on the transaction data pre-stored on the blockchain, each transaction contains a unique block hash value, which serves as a key identifier for data access. By querying the interface in the blockchain smart contract, enter the hash value of the target block. The smart contract will return the relevant encrypted data and data hash value based on the hash value. When retrieving data, it is necessary to ensure the legitimacy and authority of the query request, and use the public key infrastructure (PKI) mechanism to authenticate the request.
[0115] S4.2: The performance indicator score and pre-processed performance data are decrypted using the RSA asymmetric decryption algorithm, and the integrity and authenticity of the decrypted data are verified using the data hash value.
[0116] Specifically, after obtaining the encrypted data, the RSA asymmetric decryption algorithm is used to decrypt the data. The decryption operation requires a private key, which corresponds to the public key used in the previous encryption. After decryption, the performance indicator score, pre-processed performance data and its corresponding data hash value are obtained. To verify the integrity and authenticity of the data, the system performs a hash operation on the decrypted data and compares it with the hash value stored in the blockchain. If the two are consistent, it means that the data has not been tampered with during transmission and its integrity has been verified. If the data verification fails, a warning is triggered to prevent incomplete or tampered data from affecting subsequent analysis.
[0117] S4.3: Based on the decrypted data, calculate the comprehensive performance index, the expression is:
[0118]
[0119] Among them, X is the comprehensive performance index, l is the number of performance dimensions, k is the index of the number of performance dimensions, S k is the performance indicator score of the kth performance dimension, F k is the adjustment factor of the kth performance dimension, is the growth rate of the kth performance dimension in the time interval between t2 and t1, x k,t2 is the performance data of the kth performance dimension at time t2, x k,t1 is the performance data of the kth performance dimension at time t1, q k is the entropy value of the kth performance indicator, calculated by the entropy method, 1-q k is the stability score of the kth performance indicator.
[0120] Preferably, traditional evaluation methods often rely on static data or periodic checks, but do not dynamically track the evolution of performance. The expression better reflects the long-term trend and changes of performance by introducing growth rate and time dimensions, making the evaluation results more flexible and accurate. At the same time, by combining the entropy method to calculate the entropy value, it is possible to identify and reduce the impact of indicators with greater volatility, making the final comprehensive performance index more stable and reliable.
[0121] S4.4: Generate a comprehensive performance report based on the calculated comprehensive performance index.
[0122] Specifically, after calculating the comprehensive performance index, a detailed performance report is automatically generated based on the scores, stability, growth rate and other information of different dimensions. The report content includes the specific score, growth situation and total score of the comprehensive performance index of each performance dimension, and is saved in Excel format.
[0123] S4.5: Use smart contracts to allocate user rights and distribute comprehensive performance reports to different users based on user rights and needs.
[0124] Specifically, user rights management is performed through blockchain smart contracts, which define the scope of rights for different roles (such as administrators, department heads, and ordinary employees). Based on user roles and needs, smart contracts will dynamically assign appropriate report access rights. For example, some sensitive information is only available to administrators, some basic performance data can be provided to department heads, and ordinary employees can only view their personal performance. Based on the permission settings, reports will be sent to designated users through blockchain technology to ensure data security and privacy and avoid unauthorized access.
[0125] Preferably, blockchain smart contracts and RSA decryption ensure the secure storage and transmission of performance data, and hash verification ensures the integrity and authenticity of the data. The evaluation formula combining the time dimension and growth rate enables the performance evaluation to dynamically reflect the trend of change and improve the accuracy of the evaluation. The entropy method is used to optimize the stability of indicators, reduce the impact of volatility, and improve the robustness of the comprehensive performance index. The automatically generated comprehensive performance report is assigned permissions through smart contracts to ensure that different users access appropriate data according to their roles, thereby effectively improving the security and privacy of the data.
[0126] S5: The generation, modification and distribution of the comprehensive performance report is recorded as log information and stored through the blockchain.
[0127] Specifically, the following steps are included:
[0128] S5.1: Define the log information recording structure, record the behavior when generating, modifying and distributing behaviors, and generate log information.
[0129] Specifically, define the specific log information format, including but not limited to: event type (generation, modification, distribution), timestamp, related personnel (such as operator ID, role, etc.), operation object (such as the specific identifier of the performance report), operation content (such as the changed field, the generated report version, etc.) and operation result (such as success, failure, etc.). In addition, you can also add the operation source (such as application, user terminal, etc.) and operation type (add, delete, modify, check, etc.).
[0130] S5.2: Based on the generated log information, a hash value is calculated by a hash algorithm, and the log information is encrypted.
[0131] Specifically, each record is hashed using the SHA-256 hash algorithm to output a unique data hash value. The entire log information is encrypted using the RSA encryption algorithm. The encrypted information will be securely transmitted to the blockchain storage system.
[0132] S5.3: Through the smart contract, the encrypted log information and hash value are stored in the blockchain network to obtain the transaction hash value.
[0133] Specifically, through the smart contract function, the encrypted log information and hash value are passed as parameters to the blockchain node for storage. When the transaction is successfully submitted and packaged into the block, the blockchain node will return a transaction hash value, which can be used for subsequent query and verification.
[0134] Preferably, by recording the generation, modification and distribution of the comprehensive performance report as log information and storing it on blockchain, the traceability and transparency of the data can be effectively improved. Each operation is recorded in detail, including the event type, operator, content and results, to ensure the integrity of the data. The SHA-256 hash algorithm and RSA encryption ensure that the log information is not tampered with or leaked during storage and transmission, while ensuring the security of the data. The encrypted log is stored in the blockchain, and the immutability and decentralization of the blockchain are used to ensure the persistence and reliability of the data, and facilitate subsequent query and verification.
[0135] The present embodiment also provides a system for automatically generating reports for performance indicator scores, including: a data collection module, a quantitative model module, a block verification module, a report processing module and an audit tracing module; the data collection module is used to collect original performance data from the business platform through a data interface, and pre-process the data to generate pre-processed performance data; the quantitative model module is used to input the pre-processed performance data into the quantitative model, and combine it with historical performance data to predict and generate performance indicator scores; the block verification module is used to verify and encrypt the performance indicator scores and pre-processed performance data through a blockchain algorithm, and obtain blockchain encrypted evidence data; the report processing module is used to automatically generate a comprehensive performance report based on the blockchain encrypted evidence data, and distribute the comprehensive performance report to different users; the audit tracing module is used to record the generation, modification and distribution of the comprehensive performance report as log information, and record and store it through the blockchain.
[0136] This embodiment also provides a computer device, which is suitable for the method of automatically generating reports for performance indicator scores, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of automatically generating reports for performance indicator scores as proposed in the above embodiment.
[0137] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.
[0138] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for automatically generating a report for achieving performance indicator scores as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.
[0139] In summary, the present invention improves data processing efficiency and prediction accuracy by: automatically collecting and preprocessing performance data, combining quantitative models and historical data to predict performance indicator scores. Through blockchain encryption and evidence storage technology, the immutability and credibility of performance data are ensured, avoiding the risk of data tampering or leakage. Automatically generate and distribute performance reports, improve the efficiency of report generation and distribution, and ensure the timeliness and accuracy of information. By recording all report generation, modification and distribution behaviors through blockchain, the traceability and transparency of operations are achieved, providing reliable audit support for enterprises. By improving the degree of automation, security and transparency, it helps enterprises achieve efficient and reliable performance management and decision-making.
[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.
Claims
1. A method for automatically generating a report for performance indicator scores, characterized in that ,include: Collect original performance data from the business platform through the data interface, pre-process the original performance data, and generate pre-processed performance data; Based on historical performance data and pre-processed performance data, the performance indicator scores are predicted through quantitative models; Through the blockchain algorithm, the performance indicator scores and pre-processed performance data are verified and encrypted to obtain blockchain encrypted evidence data; Generate comprehensive performance reports based on blockchain encrypted evidence data and distribute them to different users; The generation, modification and distribution of the comprehensive performance report are recorded as log information and stored through the blockchain.
2. The method for automatically generating a report for performance indicator scores according to claim 1, characterized in that: The business platform includes an enterprise resource planning platform, a customer relationship management platform, a sales data management platform and an enterprise attendance management platform.
3. The method for automatically generating a report for performance indicator scores according to claim 2, characterized in that: The method of collecting original performance data from the business platform through the data interface, preprocessing the original performance data, and generating preprocessed performance data comprises the following specific steps: Select data interface standards based on the type of business data stored in each business platform; Based on data interface standards, perform data interface connection and configuration; Collect original performance data from the business platform through the configured data interface; Fill missing values, remove duplicates and detect outliers on the collected original performance data; Standardize raw performance data from different sources and formats into a unified data format, perform data fusion and conversion, and generate pre-processed performance data; The pre-processed performance data is stored in a database.
4. The method for automatically generating a report for performance indicator scores according to claim 3, characterized in that: The specific steps of predicting the performance indicator score through a quantitative model based on historical performance data and pre-processed performance data are as follows: Extract preprocessed performance data from the database, use principal component analysis algorithm to screen features, and generate combined features through feature interaction; The combined features are standardized and normalized by using the Z-score algorithm and the min-max algorithm, and the processed combined features are formatted to generate a performance feature matrix; Based on the performance feature matrix and historical performance data, a performance indicator scoring quantitative model is constructed through a deep neural network architecture and an adaptive weighting mechanism; Based on the performance indicator score quantification model, the mean square error is used as the loss function and the gradient descent method is used for model training; The preprocessed performance data is input into the trained performance indicator score quantification model to predict the performance indicator score.
5. The method for automatically generating a report for performance indicator scores according to claim 4, characterized in that: The performance indicator score and pre-processed performance data are verified and encrypted by the blockchain algorithm to obtain blockchain encrypted evidence data. The specific steps are: Use the SHA-256 hash algorithm to verify the performance indicator score and pre-processed performance data and calculate the data hash value; Use RSA asymmetric encryption algorithm to encrypt the performance indicator score and pre-processed performance data to obtain encrypted performance data; Create a new transaction in the blockchain through a smart contract, packaging the data hash value and encrypted performance data into a transaction; Use the blockchain data interface to send the transaction to the blockchain node in the network for verification and obtain the block hash value; Based on the block hash value, encrypted data and data hash value, blockchain encrypted evidence data is generated.
6. The method for automatically generating a report for performance indicator scores according to claim 5, characterized in that: The specific steps of generating a comprehensive performance report based on blockchain encrypted evidence data and distributing the comprehensive performance report to different users are as follows: Use the query function of the blockchain smart contract to retrieve the data hash value and encrypted performance data stored in the blockchain based on the block hash value; The encrypted performance data is decrypted through the RSA asymmetric decryption algorithm to obtain the performance indicator score and pre-processed performance data, and the integrity and authenticity of the decrypted data are verified through the data hash value; Calculate the comprehensive performance index based on the decrypted performance indicator scores and pre-processed performance data; Generate comprehensive performance report based on comprehensive performance index; Use smart contracts to allocate user rights and distribute comprehensive performance reports to different users based on user rights and needs.
7. The method for automatically generating a report for performance indicator scores according to claim 6, characterized in that: The generation, modification and distribution of the comprehensive performance report is recorded as log information and recorded and stored through the blockchain. The specific steps are: Define the log information recording structure, record the behavior and generate log information when generating, modifying and distributing behaviors; Based on the generated log information, a hash value is calculated by a hash algorithm, and the log information is encrypted; Through smart contracts, encrypted log information and hash values are stored in the blockchain network to obtain transaction hash values.
8. A system for automatically generating a report for performance indicator scores, based on the method for automatically generating a report for performance indicator scores according to any one of claims 1 to 7, characterized in that: Including data collection module, quantitative model module, block verification module, report processing module and audit tracing module; The data collection module is used to collect original performance data from the business platform through the data interface, and pre-process the data to generate pre-processed performance data; The quantitative model module is used to input the pre-processed performance data into the quantitative model, combine the historical performance data, and predict and generate the performance indicator score; The block verification module is used to verify and encrypt the performance indicator score and pre-processed performance data through the blockchain algorithm to obtain blockchain encrypted evidence data; The report processing module is used to automatically generate a comprehensive performance report based on the encrypted evidence data of the blockchain, and distribute the comprehensive performance report to different users; The audit tracing module is used to record the generation, modification and distribution of the comprehensive performance report as log information, and record and store it through the blockchain.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of the method for automatically generating a report for performance indicator scores as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for automatically generating a report for performance indicator scores according to any one of claims 1 to 7 are implemented.