Cadre examination system based on big data

Abstract

The present application relates to the technical field of cadre examination, specifically to a cadre examination system based on big data, which comprises a data integrity verification module, an intelligent weight adjustment module, a communication efficiency analysis module and a result integration and feedback module.In the present application, the security and accuracy of cadre examination are enhanced through data processing, the security and traceability of examination information are ensured through data integrity state verification, the processing efficiency and accuracy of examination data are optimized, the evaluation is more closely connected with organizational goals through dynamic adjustment of examination index weight, the scientificity and goal orientation of examination are enhanced, the depth analysis of communication efficiency directly improves the accurate evaluation of cadre potential and leadership by accurately capturing key information in cadre communication, making cadre selection and training more accurate, greatly improving the efficiency and effect of management layer in formulating personnel strategy, and improving the overall performance of cadre team and the management efficiency of organization.

Description

Technical Field

[0001] This invention relates to the field of cadre assessment technology, and in particular to a cadre assessment system based on big data. Background Technology

[0002] The field of cadre performance evaluation technology involves systematic assessment methods and tools used to monitor and evaluate the work performance, professional behavior, and contributions to organizational goals of civil servants or corporate cadres. It integrates various principles and practices of human resource management, such as performance management, goal setting and feedback, and career development planning. With the development of information technology, especially the application of big data and artificial intelligence, the field of cadre performance evaluation technology has transformed from traditional manual recording and evaluation methods into more automated, quantitative, and systematic modern management tools. This not only improves the efficiency and transparency of evaluation but also makes the selection, training, and promotion processes for cadres more fair and precise.

[0003] Among them, the cadre assessment system based on big data technology is a system that uses big data analysis and processing technology to evaluate the work performance and potential of cadres. Its main purpose is to provide a scientific and objective cadre evaluation mechanism. By collecting and analyzing cadres' work data (such as project completion, leadership performance, team interaction, etc.), it helps management make more accurate personnel decisions, such as promotion, training needs analysis, and performance improvement, which helps to improve the organization's management efficiency and the overall quality of the cadre team.

[0004] Existing technologies have significant shortcomings in automation and real-time data processing. Reliance on manual operations leads to slow and error-prone data collection and analysis processes, limiting the timeliness and accuracy of performance evaluation results. The lack of effective controls over data security and traceability results in data leaks or tampering, affecting the fairness and transparency of performance evaluations, causing delays in personnel decisions, and hindering the full recognition of cadres' potential and performance. This ultimately impacts the organization's overall operational efficiency and the rational allocation of human resources. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cadre assessment system based on big data.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a cadre assessment system based on big data includes:

[0007] The data integrity verification module collects cadre assessment data, performs hash processing on the data, calculates and combines the hash values ​​of data nodes, and synchronously records timestamps and digital signatures based on the hash value calculation results to verify the security and traceability of the data and obtain data integrity status records.

[0008] The intelligent weight adjustment module extracts assessment indicators based on the data integrity status record, calculates the correlation between assessment indicators and organizational goals, dynamically adjusts the weight settings according to the correlation calculation results, determines the consistency between assessment standards and organizational goals, and obtains the adjusted assessment indicator weights.

[0009] The communication effectiveness analysis module analyzes the communication texts of cadres simultaneously based on the adjusted assessment indicator weights, assesses emotional tendencies and thematic logic, extracts key information based on the assessment results, analyzes the communication effectiveness of cadres, and generates communication effectiveness assessment results.

[0010] Based on the communication effectiveness evaluation results, the results integration and feedback module conducts a comprehensive evaluation of cadres, automatically pushes assessment information to cadres, and establishes a comprehensive assessment and feedback result.

[0011] As a further aspect of the present invention, the calculation steps for the data node hash value are as follows:

[0012] Extract annual performance data, project contributions, and team feedback information from cadres, standardize the data format, verify data continuity, and generate the original assessment dataset;

[0013] Based on the original assessment dataset, each data item is encrypted and converted to verify the security and integrity of the data during processing and transmission, resulting in encrypted assessment data.

[0014] The encrypted assessment data is processed using the following formula:

[0015] H(T) = H(H(L1)⊕H(L2))

[0016] Calculate the top-level hash value H(T) of the dataset to obtain the hash value of the data node, where ⊕ represents the binary XOR operation, H represents the hash function, and L1 and L2 represent the hash values ​​of any two adjacent data nodes.

[0017] As a further aspect of the present invention, the step of obtaining the data integrity status record is as follows:

[0018] Based on the hash value of the data node, the current timestamp is recorded synchronously and digitally signed to confirm the immutability of the data and generate signed data with a timestamp.

[0019] The integrity of the timestamped signature data is verified by using a public key to confirm that the data has not been tampered with from the signing time to the present, thus verifying the authenticity and integrity of the data and obtaining the verified signature data.

[0020] Based on the verified signature data, the signature verification time and status of each data node are recorded to confirm the integrity and security status of the data and generate a data integrity status record.

[0021] As a further aspect of the present invention, the steps for calculating the correlation degree are as follows:

[0022] Based on the data integrity status record, data query and filtering are performed to extract key assessment indicators and generate preliminary screening results for assessment indicators.

[0023] Based on the initial screening results of the assessment indicators, the frequency and importance of the assessment indicators are analyzed, and the first-order correlation coefficient between each assessment indicator and the organizational goal is calculated to obtain the preliminary correlation analysis results between the assessment indicators and the organizational goal.

[0024] Based on the preliminary correlation analysis results between the aforementioned performance indicators and organizational goals, the following formula is used:

[0025]

[0026] Calculate the correlation R between the i-th performance indicator and the organizational goal. i The correlation analysis records between performance indicators and organizational goals were obtained, where p ij It is the first-order correlation coefficient of performance indicator i with organizational goal j, o j Let represent the operational complexity of organizational goal j, and n be the total number of organizational goals.

[0027] As a further aspect of the present invention, the step of obtaining the adjusted assessment indicator weights is as follows:

[0028] Based on the correlation analysis records between the assessment indicators and organizational goals, the weight of each assessment indicator is initially adjusted to maximize the probability of achieving organizational goals, and a preliminary adjusted weight configuration is generated.

[0029] Applying the initially adjusted weight configuration, a sensitivity analysis is conducted. The impact of the new weight settings on the achievement of the target is confirmed through simulation analysis. The performance impact before and after the indicator weight adjustment is compared, and the weight adjustment impact analysis results are generated.

[0030] Based on the weight adjustment impact analysis results, the effects of adjusting the weights of all assessment indicators are revealed, aligned with organizational goals, and the adjusted assessment indicator weights are generated.

[0031] As a further aspect of the present invention, the evaluation steps for sentiment tendency and thematic logic are as follows:

[0032] Using the adjusted assessment indicator weights, a comprehensive analysis of cadre communication texts is conducted to extract keywords and phrases from the texts and generate preliminary text analysis results.

[0033] Based on the preliminary text analysis results, sentiment analysis was performed using the following formula:

[0034]

[0035] Calculate the sentiment score of the communication texts of cadres and generate sentiment assessment results, where Q represents positive sentiment score and N represents negative sentiment score;

[0036] Based on the aforementioned sentiment assessment results, textual logic analysis is conducted to evaluate the thematic logic of the communication texts of cadres, confirm the consistency between the content and organizational goals, and generate a comprehensive assessment result.

[0037] As a further aspect of the present invention, the step of obtaining the communication effectiveness evaluation result is as follows:

[0038] Based on the comprehensive evaluation results, key information is extracted, key factors affecting communication effectiveness are identified, and key factor analysis and determination results are generated.

[0039] Based on the analysis of the key factors, the effectiveness of cadre communication was analyzed using the following formula:

[0040]

[0041] Calculate the communication effectiveness index E eff Generate analysis results on the effectiveness of cadre communication, including I i The standardized value representing key information, where m is the number of key information items;

[0042] Based on the analysis results of the cadre communication effectiveness, combined with the communication frequency and the frequency of key information occurrence, and compared with the average performance within the organization, the strengths and weaknesses of cadres in communication are identified, and communication effectiveness evaluation results are generated.

[0043] As a further aspect of the present invention, the step of obtaining the comprehensive assessment and feedback results is as follows:

[0044] Based on the communication effectiveness assessment results, combined with work performance and leadership performance, the following formula is used:

[0045]

[0046] Calculate the comprehensive evaluation index CI for cadres to obtain the comprehensive evaluation record for cadres. Among them, CEI represents the communication effectiveness index, WP represents the work performance score, and LP represents the leadership performance score.

[0047] Based on the comprehensive evaluation records of cadres, a grading standard is set. If the comprehensive evaluation index of several departments exceeds the set standard, they are marked as excellent, and the cadre evaluation level is obtained.

[0048] Based on the cadre's comprehensive evaluation record and the cadre's evaluation level, the data is uploaded to the personal information platform to establish a feedback mechanism, trigger training and improvement suggestions, and generate comprehensive assessment and feedback results.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0050] In this invention, data processing enhances the security and accuracy of cadre assessment, data integrity verification ensures the security and traceability of assessment information, optimizes the processing efficiency and accuracy of assessment data, dynamically adjusts the weights of assessment indicators to more closely link the evaluation with organizational goals, enhances the scientific nature and goal orientation of the assessment, and in-depth analysis of communication effectiveness directly improves the accurate assessment of cadre potential and leadership by accurately capturing key information in cadre communication, making cadre selection and training more precise, greatly improving the efficiency and effectiveness of management in formulating personnel strategies, and improving the overall performance of the cadre team and the management efficiency of the organization. Attached Figure Description

[0051] Figure 1 This is a system flowchart of the present invention;

[0052] Figure 2 This is a flowchart illustrating the calculation process of the data node hash value in this invention.

[0053] Figure 3 This is a flowchart illustrating the process of obtaining data integrity status records according to the present invention.

[0054] Figure 4 This is a flowchart illustrating the calculation of the correlation degree in this invention;

[0055] Figure 5 This is a flowchart illustrating the process of obtaining the adjusted assessment indicator weights according to the present invention.

[0056] Figure 6 This is a flowchart illustrating the evaluation process for the emotional bias and thematic logic of this invention.

[0057] Figure 7 This is a flowchart illustrating the process of obtaining the communication effectiveness evaluation results of this invention.

[0058] Figure 8 This is a flowchart illustrating the process of obtaining comprehensive assessment and feedback results for this invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0060] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0061] Please see Figure 1 A big data-based cadre evaluation system includes:

[0062] The data integrity verification module collects cadre assessment data, performs hash processing on the data, calculates and combines the hash values ​​of data nodes, and synchronously records timestamps and digital signatures based on the hash value calculation results to verify the security and traceability of the data and obtain data integrity status records.

[0063] The intelligent weight adjustment module extracts assessment indicators based on data integrity status records, calculates the correlation between assessment indicators and organizational goals, dynamically adjusts weight settings based on the correlation calculation results, determines the consistency between assessment standards and organizational goals, and obtains the adjusted assessment indicator weights.

[0064] The communication effectiveness analysis module analyzes the communication texts of cadres based on the adjusted assessment indicator weights, assesses emotional tendencies and thematic logic, extracts key information based on the assessment results, analyzes the communication effectiveness of cadres, and generates communication effectiveness assessment results.

[0065] The results integration and feedback module evaluates cadres based on communication effectiveness assessment results, automatically pushes assessment information to cadres, and establishes comprehensive assessment and feedback results.

[0066] Data integrity status records include hash verification results, timestamp results, and secure signatures; adjusted assessment indicator weights include weight adjustment records, strategy consistency analysis results, and target relevance calculation results; communication effectiveness evaluation results include sentiment analysis results and logical coherence analysis results; comprehensive assessment and feedback results include the assessment and evaluation system, feedback notification information, and assessment report output records.

[0067] Please see Figure 2 The steps for calculating the hash value of a data node are as follows:

[0068] Extract annual performance data, project contributions, and team feedback information from cadres, standardize the data format, verify data continuity, and generate the original assessment dataset;

[0069] The performance evaluation data of cadres is exported from the performance management system. This data includes, but is not limited to, annual performance scores, project contribution records, and peer feedback information. This data is extracted through a standardized interface, and its integrity and accuracy are ensured through real-time data verification. During the data extraction process, specific operations include verifying the data format, checking for missing data, and confirming the continuity of time-series data. The purpose of this stage is to ensure that the collected dataset can reflect the true and comprehensive performance situation, thereby laying a solid foundation for subsequent data processing. The generated raw evaluation dataset will be used for further data analysis and encryption processing.

[0070] Based on the original assessment dataset, each piece of data is encrypted and converted to verify the security and integrity of the data during processing and transmission, resulting in encrypted assessment data.

[0071] The raw data obtained from the performance management system is encrypted to ensure its security and privacy during storage and transmission. The specific encryption operation includes applying a high-strength encryption algorithm to transform the data and generate an irreversible encrypted hash value. This encryption process also includes uniquely marking each piece of data to facilitate subsequent data tracking and auditing. The encrypted data is transmitted to the central database through a secure channel, ensuring the immutability and unidentifiable nature of the data during transmission. The generated encrypted assessment data provides dual protection for data integrity and security.

[0072] The encrypted assessment data is processed using the following formula:

[0073]

[0074] Calculate the top-level hash value H(T) of the dataset to obtain the hash values ​​of the data nodes, where, This represents the binary XOR operation, H represents the hash function, and L1 and L2 represent the hash values ​​of any two adjacent data nodes.

[0075] H stands for hash function, used to convert data into fixed-size values ​​to ensure security and consistency. The XOR operation, representing binary hashes, is used to merge information from two hash values, enhancing data security. L1 and L2 represent the hash values ​​of any two adjacent data nodes. If the value of L1 is 'abc' and the value of L2 is 'def', after processing by the hash function, H( ′ abc ′ ) = 1a2b3c and H( ′ def ′If ) = 4d5e6f, then the value after the XOR operation is 5f7d0b. Further applying the hash function, we obtain H(5f7d0b) = 7g8h9i, which is the hash value of the top-level node T, and thus the hash value of the data node. This result shows that by using hashing and XOR operations, the security characteristics of two data nodes can be combined, improving the overall security of the data structure.

[0076] Please see Figure 3 The steps for obtaining the data integrity status record are as follows:

[0077] Based on the hash value of the data node, the current timestamp is recorded synchronously and digitally signed to confirm the immutability of the data and generate signed data with a timestamp.

[0078] Employing Advanced Encryption Standard (AES) and digital signature technology, the system automatically retrieves the hash value of the data node from previous steps and combines it with the current system timestamp. Using a private key, the system encrypts the hash value and timestamp of the data node to form an irreversible digital signature, ensuring the immutability of the time and content of every data record. This process not only enhances the data security layer but also ensures that the source and status of the data can be accurately traced at any time. The generated timestamp signature data provides crucial information for subsequent auditing and verification, and is an important step in maintaining data integrity and security.

[0079] The integrity of the timestamped signature data is verified by using a public key to confirm that the data has not been tampered with since the signing time, thus verifying the authenticity and integrity of the data and obtaining the verified signature data.

[0080] The generated timestamp signature data is verified using a public-key cryptography framework to ensure it has not been tampered with since it was signed. The verification operation uses RSA public-key decryption technology, confirming data integrity by comparing the decrypted hash value with the stored original hash value. This rigorous process not only ensures the security of data transmission and storage but also guarantees that the entire data processing flow meets high security and compliance standards, strengthens the trust mechanism, ensures that enterprise data is protected at all times, and reduces potential security risks.

[0081] Based on the verified signature data, record the signature verification time and status of each data node, confirm the integrity and security status of the data, and generate a data integrity status record;

[0082] After successfully verifying the authenticity and integrity of the data, the system automatically records the verification time and status of each data node. This recording operation is executed through a log management system, detailing the verification time, operator, verification status, and result for each data node, forming a comprehensive data status log. This not only provides a clear historical record of every data access and change but also ensures data integrity and traceability. The generated data integrity status records provide strong data support for enterprises, enabling them to rely on these detailed records to ensure the correctness and legality of every operation during data audits, compliance checks, or security analyses.

[0083] Please see Figure 4 The steps for calculating the correlation degree are as follows:

[0084] Based on the data integrity status record, data query and filtering are performed to extract key assessment indicators and generate preliminary screening results for assessment indicators.

[0085] Based on the data integrity status records, a data integrity score threshold of 0.8 is set. Data filtering is performed using an SQL query, such as:

[0086] SELECT*FROMdata_recordsWHEREintegrity_score>=0.8

[0087] During the process, considering that the integrity standards for different data types such as text, numbers, and timestamps may differ, different scoring criteria were set for each type. Then, the data records that meet the criteria are assessed for quality, including checking for missing data, outliers, and consistency errors. Through these meticulous steps, it can be ensured that the selected data is of high quality and highly reliable. Subsequently, key performance indicators, such as sales revenue and customer satisfaction, are extracted from the high-quality data records and used for further data analysis and decision support, generating preliminary screening results for the performance indicators.

[0088] Based on the initial screening results of the assessment indicators, the frequency and importance of the assessment indicators are analyzed, and the first-order correlation coefficient between each assessment indicator and the organizational goals is calculated to obtain the preliminary correlation analysis results between the assessment indicators and the organizational goals.

[0089] After obtaining the initial screening results of the performance indicators, an in-depth analysis is conducted on the selected indicators. First, the frequency and importance of each indicator in historical data are calculated using basic statistical descriptions such as frequency analysis, mean, and standard deviation. Then, Pearson correlation analysis is applied to determine the strength of the linear relationship between each performance indicator and organizational goals. For example, the correlation coefficient obtained by calculation can determine the direct relationship between a certain performance indicator and the organization's success rate. In addition, Spearman's rank correlation analysis is also considered to assess the correlation of nonparametric data. Statistical analysis not only helps to identify indicators that are highly correlated with organizational goals, but also reveals those performance indicators that may need to be re-evaluated or adjusted. The final correlation analysis results will directly support the organization's strategic decision-making, improving the data-driven nature and accuracy of decision-making.

[0090] Based on the preliminary correlation analysis results between performance indicators and organizational goals, the following formula is used:

[0091]

[0092] Calculate the correlation R between the i-th performance indicator and the organizational goal. i The correlation analysis records between performance indicators and organizational goals were obtained, where p ij It is the first-order correlation coefficient of performance indicator i with organizational goal j, o j This represents the operational complexity of organizational goal j, where n is the total number of organizational goals.

[0093] Collect the correlation coefficient p between each performance indicator i and each organizational goal j. ij Assuming there is a correlation coefficient matrix for the objectives, then the operational complexity of each organizational objective is evaluated. j For example, complexity can be quantified by the difficulty of implementing past projects and the resources required. Finally, the square root and absolute value operations in the formula are used to synthesize factors and calculate the overall correlation. For example, if there are three organizational goals with correlation coefficients of 0.8, 0.5, and 0.9, and operational complexities of 1.2, 1.5, and 1.1, the calculation process is as follows:

[0094]

[0095] The results show that the overall correlation between performance indicator i and organizational goals is 0.61, indicating that the correlation between indicator i and organizational goals is moderate. This value can be used to evaluate and optimize strategies for achieving organizational goals.

[0096] Please see Figure 5 The steps to obtain the adjusted assessment indicator weights are as follows:

[0097] Based on the correlation analysis records between performance indicators and organizational goals, the weight of each performance indicator is initially adjusted to maximize the probability of achieving organizational goals, and a preliminary adjusted weight configuration is generated.

[0098] When adjusting the weights of performance indicators, the basic importance of each indicator must first be determined based on the correlation report with organizational goals. A detailed analysis of the data in the report is then conducted to ensure that the weight of each indicator reflects its actual impact on organizational goals. Next, a linear programming model is used to optimize the weights. This model dynamically adjusts the weights based on the specific values ​​of the correlation to improve overall organizational efficiency. During the model's computation, the correlation data is transformed into the main input for weight adjustment. This transformation involves data standardization and the application of optimization algorithms, such as using Lagrangian relaxation techniques to address potential nonlinear issues. This ensures that the weight adjustment is not only based on actual data but also possesses operational flexibility and accuracy, generating a preliminary adjusted weight configuration.

[0099] Sensitivity analysis was conducted using the initially adjusted weight configuration. The impact of the new weight settings on the achievement of the objectives was confirmed through simulation analysis. The performance impact before and after the adjustment of the indicator weights was compared, and the results of the weight adjustment impact analysis were generated.

[0100] The effectiveness of the newly adjusted weighting configuration is evaluated by simulating and comparing organizational performance before and after the weighting adjustment. Sensitivity analysis is used to explore the specific impact of weight changes on performance. The analysis methods include, but are not limited to, multi-scenario simulations based on expected performance data under different weighting configurations, thereby assessing the effectiveness of the weighting adjustment. This method clearly shows the promoting effect of the weighting adjustment on the achievement of organizational goals. During the analysis, statistical tools such as SPSS or R are used for data processing to ensure the accuracy and reliability of the analysis results, thereby determining the consistency between the assessment criteria and organizational goals.

[0101] Based on the results of the weight adjustment impact analysis, the effects of the weight adjustment of all assessment indicators are revealed, aligned with organizational goals, and the adjusted assessment indicator weights are generated.

[0102] The above content uses the following formula:

[0103] Z = ∑(w i ×P i )

[0104] Calculate the weights Z, w of the comprehensively adjusted performance indicators. i It is the adjusted weight of indicator i, P iThis represents the contribution of indicator i to the organizational goals. It integrates the weights and contributions of all performance indicators through a weighted summation method. There are three performance indicators, with weights adjusted to 0.3, 0.4, and 0.3 respectively, and their contributions to the organizational goals being 50%, 60%, and 70% respectively. The calculation is as follows:

[0105] Z = 0.3 × 50 + 0.4 × 60 + 0.3 × 70

[0106] =15+24+21=60

[0107] The results show that the overall weight is 60, which indicates that after weight adjustment, the entire assessment system provides more balanced and effective support for organizational goals. This further guides how to adjust strategies or maintain the current weight configuration to maximize the achievement of organizational goals.

[0108] Please see Figure 6 The steps for assessing sentiment and thematic logic are as follows:

[0109] Using the adjusted assessment indicator weights, a comprehensive analysis of cadre communication texts was conducted to extract keywords and phrases from the texts and generate preliminary text analysis results.

[0110] A comprehensive analysis of cadres' communication texts was conducted using adjusted assessment indicator weights. Natural language processing technology was used to extract keywords and phrases from a large number of communication records to ensure that keywords accurately captured the themes and sentiments in the communication texts. This analysis was based on a deep learning model, which was trained using a large corpus to ensure it could understand industry-specific terminology and expressions. The analysis tool identified key elements of the text through word frequency, part-of-speech tagging, and semantic relevance analysis. The preliminary text analysis results included a quantitative assessment of cadres' communication efficiency and style. The extraction of these keywords and phrases provided the foundational data for subsequent sentiment analysis and the initial input data for accurately evaluating cadres' communication texts, thereby constructing a basic sentiment framework for the communication texts.

[0111] Based on the preliminary text analysis results, sentiment analysis was performed using the following formula:

[0112]

[0113] Calculate the sentiment score E of the communication texts of cadres to generate sentiment assessment results, where Q represents positive sentiment score and N represents negative sentiment score;

[0114] Based on text analysis, Q=20 is determined to be the score for positive expression, and N=10 is determined to be the score for negative expression. The calculation is as follows:

[0115]

[0116] The result indicates that 0.471 is close to 0.5, suggesting that the cadre's communication text has a moderately positive sentiment tendency.

[0117] Based on the results of the affective sentiment assessment, textual logic analysis is conducted to evaluate the thematic logic of the communication texts of cadres, confirm the consistency between the content and organizational goals, and generate comprehensive evaluation results.

[0118] Building upon previously obtained sentiment assessment results, a text logic analysis model was used to further evaluate the thematic logic of the communication text. By analyzing sentence structure, logical connectors, and contextual depth, the coherence and logical consistency of the information were assessed to ensure alignment between the communication content and organizational goals. This analysis was performed using advanced text analysis tools that utilize machine learning algorithms to analyze the structure and content of the text, detect logical connections and structural integration between sentences, and thus assess the text's logic. The evaluation model scored the contribution and relevance of each piece of information. The comprehensive evaluation results not only demonstrated the logical clarity of the cadre's communication but also reflected its ability to support responsibilities and organizational goals, thereby generating an indicator reflecting the degree to which the communication effectiveness aligned with organizational objectives.

[0119] Please see Figure 7 The steps for obtaining communication effectiveness evaluation results are as follows:

[0120] Based on the comprehensive evaluation results, key information is extracted, key factors affecting communication effectiveness are identified, and key factor analysis results are generated.

[0121] First, data clustering is used to analyze the assessment results of sentiment and thematic logic to extract key information influencing the effectiveness of cadre communication. This process begins by comprehensively considering keywords and phrases in the communication text, analyzing their frequency of occurrence and contextual relevance. In this way, the model can identify the vocabulary and expressions that best represent communication style and efficiency. Next, the data is fed into a clustering algorithm that groups the data based on information similarity to identify themes that play a key role in communication. Each cluster represents a group of keywords sharing the same or similar characteristics, collectively constituting factors influencing cadre communication effectiveness. This stage of analysis is based not only on semantic analysis but also incorporates the sentiment of words to ensure a comprehensive understanding of cadre communication styles from every perspective. Through this detailed data processing and analysis, a key information extraction result is generated, accurately reflecting the main factors influencing cadre communication effectiveness.

[0122] Based on the results determined by key factor analysis, the effectiveness of cadre communication is analyzed using the following formula:

[0123]

[0124] Calculate the communication effectiveness index E effGenerate analysis results on the effectiveness of cadre communication, including I i The standardized value representing key information, where m is the number of key information items;

[0125] I i The values ​​are 5, 3, and 7 respectively, and the total number m is 3. The calculation process is as follows:

[0126]

[0127] The results show that, based on the extracted key information, the communication effectiveness index of cadres is 5.26, indicating high communication effectiveness.

[0128] Based on the analysis results of cadre communication effectiveness, combined with the frequency of communication and the frequency of key information, and compared with the average performance within the organization, the strengths and weaknesses of cadres in communication are identified, and communication effectiveness evaluation results are generated.

[0129] The results of the quantitative model's analysis of managerial communication effectiveness are synthesized and evaluated to generate a final communication effectiveness assessment report. In this step, the communication effectiveness index, previously calculated using formulas, is compared with other data within the organization, such as historical performance records and effectiveness indicators of other managers at the same level. This comparison allows for a more accurate assessment of any manager's relative position in communication effectiveness within the organization. Furthermore, the analysis process includes a reassessment of the impact of each key message, confirming the actual influence of these factors within the current organizational environment and communication requirements. This comprehensive analysis process ensures the practicality and strategic value of the assessment results, enabling the organization to develop further human resource strategies and managerial development plans based on detailed data analysis. In this way, the final communication effectiveness assessment results are not only based on the output of the quantitative model but also incorporate organizational-level strategic considerations, providing decision-makers with a comprehensive reference.

[0130] Please see Figure 8 The steps for obtaining comprehensive assessment and feedback results are as follows:

[0131] Based on the communication effectiveness assessment results, combined with job performance and leadership performance, the following formula is used:

[0132]

[0133] Calculate the comprehensive evaluation index CI for cadres to obtain the comprehensive evaluation record for cadres. Among them, CEI represents the communication effectiveness index, WP represents the work performance score, and LP represents the leadership performance score.

[0134] The specific values ​​obtained are CEI=80, WP=75, and LP=85. These values ​​are based on the daily work evaluation of cadres.

[0135] Calculate the square value:

[0136] CEI 2 =80 2 =6400

[0137] WP 2 =75 2 =5625

[0138] LP 2 =85 2 =7225

[0139] Summation:

[0140] 6400 + 5625 + 7225 = 19250

[0141] Calculate the square root:

[0142]

[0143] Overall Index:

[0144]

[0145] The resulting composite index (CI) is 46.25, quantifying the overall performance of the manager across three dimensions: communication effectiveness, work performance, and leadership. A higher composite index indicates that the manager excels in these three key competencies. This result suggests that the manager's overall capabilities are balanced and high, making them a potential candidate for internal management or leadership roles. Through the composite index, the Human Resources Department can more systematically understand each manager's strengths and areas for improvement, providing data support for targeted training and promotion. The index can also be used as a basis for year-end evaluations and incentive measures.

[0146] Based on the comprehensive evaluation records of cadres, a grading standard is set. If the comprehensive evaluation index of a certain department exceeds the set standard, it is marked as excellent and a cadre evaluation level is obtained.

[0147] When setting the threshold for excellence in cadre evaluation, historical performance data is first exported from the performance management database. This includes detailed records of each cadre's communication effectiveness index, work performance, and leadership performance. By analyzing the distribution of this data, thresholds for different performance levels are determined. For example, the threshold for excellence is set as the upper quartile of the comprehensive evaluation index, i.e., a value of 75 or higher is considered excellent. This ensures that only the top cadres can be classified as excellent. If the index is between 60 and 75, it is rated as good. Those scoring below 60 need to receive further training or guidance. This classification method is both scientific and fair, dynamically reflects the actual performance of cadres, and provides accurate data support for the human resources department to formulate more effective talent development and growth strategies.

[0148] Based on the comprehensive evaluation records and evaluation levels of cadres, the data is uploaded to the personal information platform to establish a feedback mechanism, trigger training and improvement suggestions, and generate comprehensive assessment and feedback results.

[0149] Once a manager's evaluation rating and overall evaluation index are automatically uploaded to the personal information management system, the system will automatically trigger corresponding training or improvement suggestions based on the data. This mechanism uses advanced algorithms to match the manager's evaluation results with the internal training resource library, ensuring that each manager can receive personalized development plans according to their specific needs. For example, for managers with lower ratings, the system will recommend basic skills enhancement and leadership training, while for high-performing managers, it will recommend advanced management courses or external learning opportunities. The automated feedback mechanism greatly improves the efficiency and effectiveness of human resource management, enabling each manager to receive the most appropriate support and guidance at the most suitable time, thereby continuously promoting the common growth of the organization and individuals.

[0150] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A cadre assessment system based on big data, characterized in that: The system includes: The data integrity verification module collects cadre assessment data, performs hash processing on the data, calculates and combines the hash values ​​of data nodes, and synchronously records timestamps and digital signatures based on the hash value calculation results to verify the security and traceability of the data and obtain data integrity status records. The intelligent weight adjustment module extracts assessment indicators based on the data integrity status record, calculates the correlation between assessment indicators and organizational goals, dynamically adjusts the weight settings according to the correlation calculation results, determines the consistency between assessment standards and organizational goals, and obtains the adjusted assessment indicator weights. The communication effectiveness analysis module analyzes the communication texts of cadres simultaneously based on the adjusted assessment indicator weights, assesses emotional tendencies and thematic logic, extracts key information based on the assessment results, analyzes the communication effectiveness of cadres, and generates communication effectiveness assessment results. Based on the communication effectiveness evaluation results, the results integration and feedback module conducts a comprehensive evaluation of cadres, automatically pushes assessment information to cadres, and establishes a comprehensive assessment and feedback result. The steps for assessing sentiment tendency and thematic logic are as follows: Using the adjusted assessment indicator weights, a comprehensive analysis of cadre communication texts is conducted to extract keywords and phrases from the texts and generate preliminary text analysis results. Based on the preliminary text analysis results, sentiment analysis was performed using the following formula: ； Calculate the sentiment score of the communication texts of cadres Generate sentiment tendency assessment results, among which, Represents a positive emotional score. This represents a score indicating negative emotions; Based on the aforementioned sentiment assessment results, text logic analysis is performed to evaluate the thematic logic of the communication texts of cadres, confirm the consistency between the content and organizational goals, and generate a comprehensive assessment result. The steps for obtaining the communication effectiveness evaluation results are as follows: Based on the comprehensive evaluation results, key information is extracted, key factors affecting communication effectiveness are identified, and key factor analysis and determination results are generated. Based on the analysis of the key factors, the effectiveness of cadre communication was analyzed using the following formula: ； Calculate the communication effectiveness index Generate analysis results on the effectiveness of cadre communication, including Standardized values ​​representing key information The quantity of key information; Based on the analysis results of the cadre communication effectiveness, combined with the communication frequency and the frequency of key information occurrence, and compared with the average performance within the organization, the strengths and weaknesses of cadres in communication are identified, and communication effectiveness evaluation results are generated.

2. The cadre assessment system based on big data according to claim 1, characterized in that, The steps for calculating the hash value of the data node are as follows: Extract annual performance data, project contributions, and team feedback information from cadres, standardize the data format, verify data continuity, and generate the original assessment dataset; Based on the original assessment dataset, each data item is encrypted and converted to verify the security and integrity of the data during processing and transmission, resulting in encrypted assessment data. The encrypted assessment data is processed using the following formula: ； Calculate the top-level hash value of the dataset The hash value of the data node is obtained, where, This represents the binary XOR operation. Represents a hash function. and This represents the hash value of any two adjacent data nodes.

3. The cadre assessment system based on big data according to claim 2, characterized in that, The steps for obtaining the data integrity status record are as follows: Based on the hash value of the data node, the current timestamp is recorded synchronously and digitally signed to confirm the immutability of the data and generate signed data with a timestamp. The integrity of the timestamped signature data is verified by using a public key to confirm that the data has not been tampered with from the signing time to the present, thus verifying the authenticity and integrity of the data and obtaining the verified signature data. Based on the verified signature data, the signature verification time and status of each data node are recorded to confirm the integrity and security status of the data and generate a data integrity status record.

4. The cadre assessment system based on big data according to claim 3, characterized in that, The steps for calculating the correlation degree are as follows: Based on the data integrity status record, data query and filtering are performed to extract key assessment indicators and generate preliminary screening results for assessment indicators. Based on the initial screening results of the assessment indicators, the frequency and importance of the assessment indicators are analyzed, and the first-order correlation coefficient between each assessment indicator and the organizational goal is calculated to obtain the preliminary correlation analysis results between the assessment indicators and the organizational goal. Based on the preliminary correlation analysis results between the aforementioned performance indicators and organizational goals, the following formula is used: ； Calculate the first The correlation between each performance indicator and organizational goals This yields a record of the correlation analysis between performance indicators and organizational goals, including... Performance indicators For organizational goals The first-order correlation coefficient, Indicate organizational goals The operational complexity, It represents the total number of organizational goals.

5. The cadre assessment system based on big data according to claim 4, characterized in that, The steps for obtaining the adjusted assessment indicator weights are as follows: Based on the correlation analysis records between the assessment indicators and organizational goals, the weight of each assessment indicator is initially adjusted to maximize the probability of achieving organizational goals, and a preliminary adjusted weight configuration is generated. Applying the initially adjusted weight configuration, a sensitivity analysis is conducted. The impact of the new weight settings on the achievement of the target is confirmed through simulation analysis. The performance impact before and after the indicator weight adjustment is compared, and the weight adjustment impact analysis results are generated. Based on the weight adjustment impact analysis results, the effects of adjusting the weights of all assessment indicators are revealed, aligned with organizational goals, and the adjusted assessment indicator weights are generated.

6. The cadre assessment system based on big data according to claim 1, characterized in that, The steps for obtaining the comprehensive assessment and feedback results are as follows: Based on the communication effectiveness assessment results, combined with work performance and leadership performance, the following formula is used: ； Calculate the comprehensive evaluation index of cadres The comprehensive evaluation record of cadres is obtained, including Represents the communication effectiveness index. Represents job performance rating. Representative leadership performance score; Based on the comprehensive evaluation records of cadres, a grading standard is set. If the comprehensive evaluation index of several departments exceeds the set standard, they are marked as excellent, and the cadre evaluation level is obtained. Based on the cadre's comprehensive evaluation record and the cadre's evaluation level, the data is uploaded to the personal information platform to establish a feedback mechanism, trigger training and improvement suggestions, and generate comprehensive assessment and feedback results.

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