Index system evaluation method based on weight design

Abstract

The invention discloses an index system evaluation method based on weight design, and particularly relates to the technical field of index system evaluation, and the method specifically comprises the following steps: obtaining project management information and social participation information through building a data obtaining model after determining an evaluation purpose, an evaluation range and an evaluation index, and after obtaining the project management information and the social participation information, obtaining an evaluation result; respectively processing the project management information and the social participation information; establishing a data analysis model for the processed project management information and social participation information, and generating a hidden danger assessment coefficient; performing comparative analysis on the generated hidden danger evaluation coefficient and a preset hidden danger evaluation coefficient threshold value, judging whether the weight needs to be updated, and correspondingly generating a hidden danger signal and a normal signal; and obtaining a plurality of hidden danger evaluation coefficients generated subsequently when the hidden danger signal is generated, and carrying out comprehensive analysis on the plurality of obtained hidden danger evaluation coefficients. According to the method, disjunction between an evaluation system and college development can be found in advance, and a response can be made in time when the weight needs to be updated.

Description

Technical Field

[0001] The present invention relates to the technical field of index system evaluation, and more specifically, the present invention relates to an index system evaluation method based on weight design. Background Art

[0002] The index system evaluation method based on weight design is a method that determines the relative importance or weight of each evaluation index and comprehensively evaluates these indexes by combining actual data. First, the overall goal of the evaluation is determined, and an index system including multiple evaluation indexes is constructed based on this goal. Then, methods such as expert investigation, analytic hierarchy process, and principal component analysis are used to determine the weight of each index to ensure that the contribution degrees of different indexes in the evaluation process are relatively accurate. Next, relevant data is collected and processed to ensure the accuracy and reliability of the data. In the evaluation process, according to the weights of the indexes, each evaluation index is comprehensively evaluated, and usually, the weighted sum or weighted average method is used to obtain the comprehensive score. Finally, the evaluation results are analyzed, the scores of each index and the comprehensive evaluation results are explained, and improvements and adjustments are made according to the feedback results to improve the accuracy and practicality of the evaluation.

[0003] In universities, the index system evaluation method based on weight design is widely used in many aspects to provide a comprehensive evaluation of the performance and development of universities. First, by clearly defining the core goals and value orientations of universities, the scope and content of evaluation indexes are determined, covering aspects such as teaching quality, scientific research level, faculty strength, student cultivation quality, and social services. Then, for each index, its relative importance is determined through expert evaluation, questionnaires, etc., and weights are assigned to different indexes to reflect their contribution degrees in the evaluation system. Next, data of each index is collected and standardized to ensure comparability between different indexes. Finally, using the weighted sum or other mathematical models, the data of each index is summed up according to its weight to obtain the result of the comprehensive evaluation of the university. This method can evaluate the performance of universities more objectively and comprehensively, provide a basis for scientific decision-making for university managers, and help promote the sustainable development of universities. However, there are still some deficiencies in the existing methods.

[0004] The prior art has the following deficiencies: With the adjustment of the development strategies and goals of universities and the changes in the external environment, the importance and influence of various indicators may change. However, the existing technology cannot update the weights of the evaluation system in real time according to these situations, and cannot reflect these changes in a timely manner, resulting in the evaluation results not conforming to the actual development of universities. The long-term unchanged weights will cause biases and distortions in the evaluation results. If certain indicators are given high weights for a long time while the importance of other indicators is underestimated, then universities may overemphasize the indicators with large weights in the evaluation system and neglect the development of other aspects. This will lead to universities sacrificing the development of other aspects in the process of pursuing certain indicators, affecting the overall development and comprehensive strength of universities. The long-term unchanged weights will also limit the exertion of the development advantages of universities, making universities unable to make full use of their own characteristics and advantages. The long-term unchanged weights will lead to the disconnection between the evaluation system and the development of universities, and the evaluation results may no longer objectively and accurately reflect the true situation of universities, thus affecting the strategic planning and resource allocation of universities.

[0005] To address the above deficiencies, a technical solution is provided herein. Summary of the Invention

[0006] To overcome the above deficiencies of the prior art, the present invention provides an evaluation method for an index system based on weight design to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solution:

[0008] An evaluation method for an index system based on weight design specifically includes the following steps:

[0009] S1. After determining the evaluation purpose, scope, and evaluation indicators, obtain project management information and social participation information through establishing a data acquisition model. After obtaining, process the project management information and social participation information respectively;

[0010] S2. Establish a data analysis model for the processed project management information and social participation information to generate a hidden danger evaluation coefficient;

[0011] S3. Compare and analyze the generated hidden danger evaluation coefficient with a pre-set hidden danger evaluation coefficient threshold to determine whether the weight needs to be updated, and correspondingly generate a hidden danger signal and a normal signal;

[0012] S4. Obtain a number of subsequent generated hidden danger evaluation coefficients when generating a hidden danger signal, comprehensively analyze the obtained number of hidden danger evaluation coefficients, generate a corresponding risk level signal, and give a corresponding early warning prompt.

[0013] Preferably, in step S1, the project management information obtained by establishing a data acquisition model includes the change rate of scientific research project completion. After acquisition, the change rate of scientific research project completion is calibrated as KY bhl ,The social participation information obtained by establishing a data acquisition model includes the trend index of the number of participants in campus activities. After acquisition, the trend index of the number of participants in campus activities is calibrated as HP qs .

[0014] Preferably, the logic for obtaining the change rate of completion of the scientific research project is as follows:

[0015] S111. Obtain the number of scientific research projects completed at different times within T time, and mark the number of scientific research projects completed at different times within T time as XJ m , m represents the number of scientific research projects completed at different times within time T, m = 1, 2, 3, ..., k, k is a positive integer;

[0016] S112. The number of scientific research projects completed at different times within the time T is obtained. m Modeling is done through exponential smoothing model, and the smoothing coefficient is calibrated as α, 0≤α≤1;

[0017] S113. Use the exponential smoothing model to fit the number of scientific research project completions at different times within the obtained time T, and predict the number of scientific research project completions at different times after time T, using the exponential smoothing formula:

[0018]

[0019] in, is the number of scientific research projects completed at different times after time T, n is the number of scientific research projects completed at different times after time T, n = k+1, k+2, k+3, ..., 2k, k is a positive integer;

[0020] S114. Calculate the change rate of scientific research project completion. The specific calculation is as follows:

[0021]

[0022] Among them, KY bhl It is the change rate of scientific research project completion.

[0023] Preferably, the logic for obtaining the campus activity participant trend index is as follows:

[0024] S121. Obtain the number of participants in campus activities at different times within a time period T, and mark the number of participants in campus activities at different times within a time period T as CP x , x represents the number of participants in campus activities at different times in time T, x = 1, 2, 3, ..., j, j is a positive integer;

[0025] S122. Establish a Holt-Winters seasonal exponential smoothing model to obtain the level smoothing coefficient, trend smoothing coefficient, seasonal smoothing coefficient, and seasonal period. Denote the level smoothing coefficient, trend smoothing coefficient, seasonal smoothing coefficient, and seasonal period as β, γ, δ, and τ respectively. The value ranges of β, γ, and δ are all [0, 1].

[0026] S123. Establish a time series for the number of campus activity participants at different times within time T, and obtain the level, trend, and seasonal values of the number of campus activity participants at the initial time within time T. Denote the level, trend, and seasonal values of the number of campus activity participants at the initial time within time T as l 1 , b 1 and s 1 , then:

[0027] S124. For each new time point x after time T, based on the currently obtained number of campus activity participants CP x , use the following formula to update the level l x , trend b x and seasonality s x :

[0028]

[0029] b x = γ * (l x - l x-1 ) + (1 - γ) * b x-1

[0030]

[0031] where β, γ, and δ are the level smoothing coefficient, trend smoothing coefficient, and seasonal smoothing coefficient respectively, and τ is the length of the seasonal period;

[0032] S125. Combine the values of the latest level l x , trend b x and seasonality s x , and use the following formula to calculate the campus activity participation number trend index after time T. The specific calculation formula is as follows:

[0033]

[0034] where h is the time step after time T, and % represents the modulo operation.

[0035] Preferably, a data analysis model is established for the processed project management information and social participation information, that is, the completion rate change rate KY of scientific research projects bhl and the trend index HP of the number of participants in campus activities qs to establish a data analysis model, generate a hidden danger assessment coefficient, and label the hidden danger assessment coefficient as PG yh , according to the formula:

[0036]

[0037] In the formula, r 1 and r 2 are the preset proportionality coefficients of the completion rate change rate KY of scientific research projects bhl and the trend index HP of the number of participants in campus activities qs , and both r 1 and r 2 are greater than 0.

[0038] Preferably, the preset hidden danger assessment coefficient threshold is labeled as PG ysyz , and the generated hidden danger assessment coefficient PG yh is compared and analyzed with the preset hidden danger assessment coefficient threshold PG ysyz to determine whether the weight needs to be updated, and corresponding hidden danger signals and normal signals are generated. The specific comparison and analysis are as follows:

[0039] If PG yh ≤PG ysyz , the weight does not need to be updated, and a normal signal is generated;

[0040] If PG yh >PG ysyz , the weight needs to be updated, and a hidden danger signal is generated.

[0041] Preferably, several subsequent hidden danger assessment coefficients generated when generating a hidden danger signal are obtained and re-labeled as where z represents the number of several subsequent hidden danger assessment coefficients generated when generating a hidden danger signal, z = 1, 2, 3,..., g, and g is a positive integer;

[0042] The average value of several hidden danger assessment coefficients is labeled as PG - , and the specific calculation formula is:

[0043] The standard deviation of several hidden danger assessment coefficients is labeled as PG σ , and the specific calculation formula is:

[0044] Preferably, obtain the preset average value and preset standard deviation of several subsequent generated hidden danger evaluation coefficients when generating a hidden danger signal, and calibrate the preset average value and preset standard deviation of the several hidden danger evaluation coefficients as and Let the average value PG of several hidden danger evaluation coefficients - and the standard deviation PG σ be compared and analyzed with the preset average value and the preset standard deviation respectively to generate corresponding risk level signals and corresponding early warning prompts. The specific analysis is as follows:

[0045] If Generate a high-risk signal and transmit the high-risk signal to the mobile terminal for high-risk early warning prompt through the mobile terminal;

[0046] If and Generate a medium-risk signal and transmit the medium-risk signal to the mobile terminal for medium-risk early warning prompt through the mobile terminal;

[0047] If and Generate a low-risk signal and transmit the low-risk signal to the mobile terminal for low-risk early warning prompt through the mobile terminal.

[0048] The technical effects and advantages of the present invention:

[0049] 1. By combining the obtained research project completion rate change rate and the campus activity participation number trend index, the present invention conducts comprehensive analysis and further judges the necessity of weight update, providing accurate reference for university administrators, effectively reducing the bias and distortion caused by long-term unchanged weights, improving the objectivity and accuracy of the evaluation system, timely discovering the need for weight update, helping to detect in advance the disconnection between the evaluation system and the university development, and being able to react in time when the weight needs to be updated, helping the university to adjust the evaluation system more flexibly to make it closer to the actual development needs, promoting the overall development of the university and enhancing the comprehensive competitiveness.

[0050] 2. The present invention can also improve the strategic planning and resource allocation efficiency of the university, help to allocate resources more reasonably, achieve the sustainable development of the university. At the same time, it can also improve the transparency and management efficiency within the university, promote communication and cooperation among departments, strengthen the internal integrated management of the school, and thus promote the improvement of the overall governance level of the university. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings;

[0052] Figure 1This is a schematic flowchart of an evaluation method for an index system based on weight design in the present invention. Detailed implementation manners

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment

[0055] As Figure 1 shown, the present invention provides an evaluation method for an index system based on weight design, which specifically includes the following steps:

[0056] S1. After determining the evaluation purpose, scope and evaluation indexes, obtain project management information and social participation information through establishing a data acquisition model. After obtaining, process the project management information and social participation information respectively;

[0057] The evaluation purpose, scope and evaluation indexes can be determined in various ways: First, relevant literature reviews and investigations can be carried out to understand the common purposes and scopes of university evaluations at home and abroad, and learn from the evaluation experiences and practices of other universities; Second, expert seminars or expert interviews can be organized, inviting experts, scholars and industry practitioners in the education field to jointly discuss and determine the evaluation purpose and scope, as well as the key points and key areas that should be concerned; In addition, internal school surveys and questionnaires can be carried out to collect the opinions and suggestions of all parties of stakeholders, understand their expectations and concerns about the evaluation, so as to provide a reference for determining the evaluation purpose and scope; For the determination of evaluation indexes, methods such as expert opinion surveys, index screening and weight assignment can be used.

[0058] Determining the evaluation purpose and scope can clarify the evaluation objectives and scope, help the evaluators clarify the content to be evaluated and the key points to be concerned, so as to ensure the pertinence and effectiveness of the evaluation activities. Determining the evaluation indexes is to quantify all aspects of the evaluation object for objective and comprehensive evaluation. The selection of evaluation indexes should fully consider factors such as the development strategy of the university, teaching and research conditions, and social needs to ensure that the evaluation indexes are representative, operable and comparable. Therefore, by determining the evaluation purpose and scope and determining the evaluation indexes, a clear direction and specific basis can be provided for the evaluation activities, which helps to ensure the accuracy and reliability of the evaluation results and provide effective support and guidance for the development and improvement of the university.

[0059] It should be noted that establishing a data acquisition model can help universities systematically collect various indicator data and ensure the timeliness and accuracy of the data. By establishing a data acquisition model, the data collection process can be automated, reducing the time and cost of manual operations and improving the quality and consistency of the data. Such a model can be based on internal university systems and external data sources, such as student information management systems, scientific research achievement databases, etc., to achieve automatic data scraping and integration. In addition, the data acquisition model can also set up a data quality control mechanism to detect and correct abnormal data, ensuring that the collected data meets the requirements and standards of the evaluation. Therefore, establishing a data acquisition model can improve the efficiency and reliability of parameter collection and provide a reliable data basis for subsequent evaluation work;

[0060] Furthermore, it should be noted that the data acquisition model can be established in the following ways:

[0061] First, existing data scraping tools or data integration platforms can be used, such as web crawlers, data collection software, or data integration APIs, to scrape project management information, social participation information, and academic research information from various data sources, such as internal university systems, official websites, teaching management systems, etc. These tools can automatically and regularly obtain data according to the set rules and conditions and save it to a database or data warehouse;

[0062] Second, a data warehouse or data lake can be established to integrate data from various data sources and establish corresponding data models and relationships. Through the data warehouse or data lake, data can be uniformly managed and stored, facilitating subsequent data processing and analysis;

[0063] In addition, attention needs to be paid to data quality control when establishing a data acquisition model, including steps such as data cleaning, deduplication, and format conversion, to ensure high-quality and accurate collected data. At the same time, data security and privacy protection also need to be considered, and necessary measures should be taken to protect the security and privacy of the data;

[0064] In summary, establishing a data acquisition model requires the comprehensive utilization of existing data scraping tools and data integration platforms, combined with technical means such as data warehouses or data lakes, to achieve automatic acquisition and unified management of project management information, social participation information, and academic research information, thereby providing a reliable data basis for the evaluation work.

[0065] S2. Establish a data analysis model for the processed project management information and social participation information to generate a hidden danger assessment coefficient;

[0066] S3. Compare and analyze the generated hidden danger assessment coefficient with the pre-set hidden danger assessment coefficient threshold to determine whether the weight needs to be updated, and correspondingly generate a hidden danger signal and a normal signal;

[0067] S4. Obtain a number of hidden danger assessment coefficients generated subsequently when generating hidden danger signals, comprehensively analyze the obtained number of hidden danger assessment coefficients, generate corresponding risk level signals, and perform corresponding early warning prompts.

[0068] In this embodiment, in step S1, the project management information obtained by establishing a data acquisition model includes the completion rate change of scientific research projects. After obtaining it, the completion rate change of scientific research projects is calibrated as KY. bhl The social participation information obtained by establishing a data acquisition model includes the trend index of the number of participants in campus activities. After obtaining it, the trend index of the number of participants in campus activities is calibrated as HP. qs ;

[0069] In this embodiment, the acquisition logic of the completion rate change of scientific research projects is as follows:

[0070] S111. Obtain the number of completed scientific research projects at different times within T time, and calibrate the number of completed scientific research projects at different times within T time as XJ. m Here, m represents the number of the number of completed scientific research projects at different times within T time, m = 1, 2, 3,..., k, and k is a positive integer.

[0071] S112. Model the obtained number of completed scientific research projects XJ at different times within T time m using an exponential smoothing model, and calibrate the smoothing coefficient as α, 0 ≤ α ≤ 1. The exponential smoothing model can capture the trend and seasonal changes of the number of completed scientific research projects at different times within T time.

[0072] It should be noted that when modeling the obtained number of completed scientific research projects at different times within T time through an exponential smoothing model, first, appropriate parameters need to be selected, such as the smoothing coefficient in the exponential smoothing model; then, the exponential smoothing model is applied for modeling, and recursive calculations are performed using historical data and the smoothing coefficient to obtain the smoothed value at each moment. Finally, the model can be used to fit historical data and predict the number of completed scientific research projects in the future for a period of time. The significance of establishing an exponential smoothing model lies in smoothing the number of completed scientific research projects and predicting trends, so as to better understand the change trend and long-term development trend of the data, be more flexible in adapting to data changes, have a faster response to trend changes, and can provide relatively accurate short-term predictions. Therefore, by establishing such a model, more reliable data support can be provided for decision-making, helping universities reasonably plan scientific research projects and resource allocation, and improving scientific research management efficiency and decision-making accuracy.

[0073] It should be further noted that the smoothing coefficient α in the exponential smoothing model is a parameter between 0 and 1, which is used to control the influence degree of historical observations on the predicted value. When α is small, the model attaches less weight to historical observations and pays more attention to recent observations, which is suitable for rapidly changing data; when α is large, the model attaches more weight to historical observations and adapts to long-term trends more smoothly, which is suitable for relatively stable data. Therefore, choosing an appropriate value of α has an important impact on the prediction accuracy and stability of the model. The smoothing coefficient α is not specifically limited here and needs to be adjusted according to the characteristics of the data and the requirements of the prediction.

[0074] S113. Use the exponential smoothing model to fit the number of completed scientific research projects at different times within T time, and predict the number of completed scientific research projects at different times after T time. Through the exponential smoothing formula:

[0075]

[0076] Where is the number of completed scientific research projects at different times after T time, n is the number of the number of completed scientific research projects at different times after T time, n = k + 1, k + 2, k + 3,..., 2k, and k is a positive integer;

[0077] S114. Calculate the change rate of the completion of scientific research projects. The specific calculation formula is as follows:

[0078]

[0079] Where KY bhl is the change rate of the completion of scientific research projects;

[0080] By obtaining the number of completed scientific research projects at different times within T time and processing and predicting the change rate of the completion of scientific research projects in the future for a period of time through the exponential smoothing model, it is of great significance for whether the prediction weight needs to be updated. The change rate of the completion of scientific research projects can reflect the development trend and changes of scientific research activities. When the number of completed scientific research projects shows a stable growth or decline trend, it may be necessary to adjust the weights of corresponding indicators to better reflect the actual development of the university. If the change rate of the completion of scientific research projects is large, it indicates that the importance and influence of scientific research activities have changed greatly, which may require updating the weights of relevant indicators to ensure that the evaluation system is consistent with the actual situation of the university's development. Therefore, there is a close relationship between the magnitude of the change rate of the completion of scientific research projects and whether the weight needs to be updated. By monitoring the change rate of the completion of scientific research projects, the weights of evaluation indicators can be adjusted in a timely manner to ensure the accuracy and effectiveness of the evaluation system and provide a scientific basis for the strategic planning and resource allocation of the university.

[0081] In this embodiment, the acquisition logic of the campus activity participation number trend index is as follows:

[0082] S121. Obtain the campus activity participation numbers at different times within time period T, and label the campus activity participation numbers at different times within time period T as CP x , where x represents the serial number of the campus activity participation number at different times within time period T, x = 1, 2, 3, …, j, and j is a positive integer;

[0083] It should be noted that the campus activity participation numbers at different times within time period T can be obtained by using the registration, check-in or participation records of campus activities, and obtaining the participation number data of the activities through the database or online platform of the school or activity organizer. These data may include the participation numbers of each activity, the identity information of the participants, and the participation time, etc.; or the number of people can be counted physically or electronically. For example, a manual or automatic counter can be used to count the number of people at the activity site, or the participation numbers can be recorded through the online registration system of the network platform. The detailed information of the campus activity participation numbers at different times within time period T can be obtained, or it can also be obtained through other methods. The specific acquisition method is not specifically limited here and can be selected according to actual needs;

[0084] S122. Establish a Holt-Winters seasonal exponential smoothing model, obtain the level smoothing coefficient, trend smoothing coefficient, seasonal smoothing coefficient and seasonal period, and label the level smoothing coefficient, trend smoothing coefficient, seasonal smoothing coefficient and seasonal period as β, γ, δ and τ respectively. The value ranges of β, γ and δ are all [0, 1];

[0085] It should be noted that the level smoothing coefficient β controls the weight of the latest observation value. A larger β value makes the model adapt to new observation values more quickly, while a smaller β value makes the model more stable and rely more on historical observation values. The value range is usually [0, 1]; the trend smoothing coefficient γ controls the smoothing degree of the trend. A larger γ value means that the trend change is smoother, while a smaller γ value means that the trend change is more sensitive. The value range is usually [0, 1]; the seasonal smoothing coefficient δ controls the smoothing degree of the seasonal change. A larger δ value means that the seasonal change is smoother, while a smaller δ value means that the seasonal change is more sensitive. The value range is usually [0, 1]; the seasonal period τ refers to the length of each season and is used to determine the periodicity of the seasonal change. For the campus activity participation number trend index, the seasonal period can be one week, one month or one semester, etc. It is not specifically limited here and can be selected according to actual needs;

[0086] The establishment process of the Holt-Winters seasonal exponential smoothing model is relatively complex and requires multiple steps. First, the model needs to initialize parameters, including smoothing coefficients, trend smoothing coefficients, and seasonal smoothing coefficients, etc. Then, based on historical data, the initial state is determined, namely the initial level, initial trend, and initial values of the seasonal components. Next, recurrence formulas are used to predict data for a period of time in the future, including predictions of future trends and seasonal changes. During the prediction process, the model parameters need to be continuously updated to make the prediction results more accurate. This involves adjusting the smoothing coefficients and the length of the seasonal cycle by observing the actual data. After completing the parameter update, iterative repetition is required until the stopping condition is met, usually reaching a certain degree of convergence or a threshold of prediction error. Finally, through model evaluation, including comparison with actual observed values and error analysis, the accuracy and reliability of the model are determined. After completion, the model can be used to predict the trend of the number of campus activity participants in the future for a period of time, thereby guiding decision-making and planning.

[0087] S123. Establish a time series for the number of campus activity participants at different times within the T time period, obtain the level, trend, and seasonal values of the number of campus activity participants at the initial moment within the T time period, and label the level, trend, and seasonal values of the number of campus activity participants at the initial moment within the T time period as l 1 , b 1 and s 1 , then:

[0088] S124. For each new time point x after the T time period, based on the currently obtained number of campus activity participants CP x , use the following formulas to update the level l x , trend b x and seasonality s x :

[0089]

[0090] b x =γ*(l x -l x-1 )+(1 - γ)*b x-1

[0091]

[0092] where β, γ, and δ are the level smoothing coefficient, trend smoothing coefficient, and seasonal smoothing coefficient respectively, and τ is the length of the seasonal cycle;

[0093] S125. Combine the latest level l x , trend b xand seasonality s x To calculate the trend index of the number of campus activity participants after time T, the following formula is used. The specific calculation formula is as follows:

[0094]

[0095] where h is the time step after time T, and % represents the modulo operation;

[0096] It should be noted that the time step h after time T represents the time span to be predicted, that is, the time interval between the future time point to be predicted and the current time point; and % represents the modulo operation, which is used when predicting seasonal changes to ensure that the predicted time point falls at the correct position within the seasonal cycle. For example, if the length of the seasonal cycle is τ and h is greater than τ, then the result of h % τ is the remainder of h modulo τ, which can ensure that the predicted time point changes cyclically within the seasonal cycle.

[0097] Obtaining the trend index of the number of campus activity participants through the Holt-Winters seasonal exponential smoothing model helps to consider the seasonality and trend changes of time series data in the model, thus more accurately predicting the future trend of the number of activity participants. Such a model can capture the periodic changes and long-term trends in the data, improving the accuracy and stability of the prediction. For evaluating whether the weights need to be updated, the trend index of the number of campus activity participants can be an important reference indicator. If the trend index of the number of campus activity participants shows a continuous upward trend, it indicates that the attractiveness and influence of campus activities are increasing, and it may be necessary to consider increasing the weights of campus activity-related indicators to better reflect the contribution of campus activities to the comprehensive evaluation. On the contrary, if the trend index of the number of campus activity participants shows a downward trend, it may be necessary to appropriately reduce the weights of campus activity-related indicators to better balance the evaluation system and ensure the objectivity and accuracy of the evaluation results. Therefore, there is a close relationship between the magnitude of the trend index of the number of campus activity participants and whether the evaluation weights need to be updated, and it can provide an important reference basis for the adjustment of the evaluation system to ensure that the evaluation results are closer to the actual situation of the campus.

[0098] In this embodiment, a data analysis model is established for the processed project management information and social participation information, that is, the completion rate change KY of scientific research projects bhl and the trend index HP of the number of campus activity participants qs to establish a data analysis model to generate a hidden danger assessment coefficient, and the hidden danger assessment coefficient is calibrated as PG yh , according to the formula:

[0099]

[0100] In the formula, r 1 and r2 are respectively the completion rate change KY of scientific research projects bhl and the trend index HP of the number of participants in campus activities qs of the preset proportionality coefficients, and r 1 and r 2 are both greater than 0;

[0101] It should be noted that the completion rate change KY of scientific research projects bhl and the trend index HP of the number of participants in campus activities qs of the preset proportionality coefficients r 1 and r 2 are for more flexible adaptation to different working conditions and environmental changes in actual monitoring. These proportionality coefficients can be adjusted according to specific situations to improve the performance and applicability of the monitoring system.

[0102] In this embodiment, the preset hidden danger assessment coefficient threshold is calibrated as PG ysyz , and the generated hidden danger assessment coefficient PG yh is compared and analyzed with the preset hidden danger assessment coefficient threshold PG ysyz to determine whether the weight needs to be updated, and corresponding hidden danger signals and normal signals are generated. The specific comparison and analysis are as follows:

[0103] If PG yh ≤PG ysyz , the weight does not need to be updated, and a normal signal is generated;

[0104] If PG yh >PG ysyz , the weight needs to be updated, and a hidden danger signal is generated.

[0105] In this embodiment, when generating a hidden danger signal, several subsequent generated hidden danger assessment coefficients are obtained and recalibrated as where z represents the number of several subsequent generated hidden danger assessment coefficients when generating a hidden danger signal, z = 1, 2, 3,..., g, and g is a positive integer;

[0106] The average value of several hidden danger assessment coefficients is calibrated as PG - , and the specific calculation formula is:

[0107] The standard deviation of several hidden danger assessment coefficients is calibrated as PG σ , and the specific calculation formula is:

[0108] In this embodiment, the preset average value and preset standard deviation of a number of subsequent generated hidden danger evaluation coefficients when generating a hidden danger signal are obtained, and the preset average value and preset standard deviation of the number of hidden danger evaluation coefficients are respectively calibrated as and The average value PG of a number of hidden danger evaluation coefficients - and the standard deviation PG σ are respectively compared and analyzed with the preset average value and the preset standard deviation to generate corresponding risk level signals and corresponding early warning prompts. The specific analysis is as follows:

[0109] If a high-risk signal is generated and the high-risk signal is transmitted to the mobile terminal, and a high-risk early warning prompt is given through the mobile terminal, indicating that the current risk level has exceeded the expectation, and the urgency of weight update needs to be increased. High risk means there are serious problems or challenges, and timely actions need to be taken to address them to avoid further losses or adverse effects. Therefore, the urgency of weight update may increase with the appearance of the high-risk signal, which means that the evaluation system needs to be adjusted more quickly to better reflect the current situation and guide future decisions and actions;

[0110] If and a medium-risk signal is generated and the medium-risk signal is transmitted to the mobile terminal, and a medium-risk early warning prompt is given through the mobile terminal, indicating that the current risk level is in a state that is not very ideal but not particularly serious. In this case, the urgency of weight update may decrease compared to the high-risk signal, but there is still a certain degree of urgency. The medium-risk signal prompts that the current situation needs to be further monitored and analyzed to determine whether more measures need to be taken to reduce the risk or improve the situation. Therefore, the urgency of weight update may increase with the appearance of the medium-risk signal, although not as urgent as the high-risk signal;

[0111] If and a low-risk signal is generated and the low-risk signal is transmitted to the mobile terminal, and a low-risk early warning prompt is given through the mobile terminal, indicating that the current risk level is relatively low and the situation is relatively stable, but there are still some potential risks or problems. For the low-risk signal, the urgency of updating the weight may be low because the current situation may still be acceptable or within the controllable range. However, the appearance of the low-risk signal also prompts the need for continuous attention and monitoring to ensure that the risk remains at an acceptable level and to take necessary measures in a timely manner to prevent potential risk escalation. Therefore, the existence of the low-risk signal may prompt some adjustments or improvements, but it does not require immediate emergency actions like the high-risk or medium-risk signals.

[0112] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0114] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0115] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0116] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0117] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical, or other forms.

[0118] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, and other various media that can store program codes.

[0119] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An index system evaluation method based on weight design, characterized in that: The specific steps include: S1. After determining the purpose, scope and evaluation indicators of the assessment, obtain project management information and social participation information by establishing a data acquisition model. After obtaining, process the project management information and social participation information respectively; S2. Establish a data analysis model with the processed project management information and social participation information to generate a hidden danger assessment coefficient; S3, comparing and analyzing the generated hidden danger assessment coefficient with the pre-set hidden danger assessment coefficient threshold, determining whether the weight needs to be updated, and generating hidden danger signals and normal signals accordingly; S4. Acquire several hidden danger assessment coefficients that are subsequently generated when the hidden danger signal is generated, conduct a comprehensive analysis on the acquired several hidden danger assessment coefficients, generate corresponding risk level signals, and issue corresponding early warning prompts.

2. The index system evaluation method based on weight design according to claim 1 is characterized in that: In step S1, the project management information obtained by establishing a data acquisition model includes the change rate of scientific research project completion. After acquisition, the change rate of scientific research project completion is calibrated as KY bhl ,The social participation information obtained by establishing a data acquisition model includes the trend index of the number of participants in campus activities. After acquisition, the trend index of the number of participants in campus activities is calibrated as HP qs .

3. The index system evaluation method based on weight design according to claim 2 is characterized in that: The logic for obtaining the change rate of the scientific research project completion is as follows: S111. Obtain the number of scientific research projects completed at different times within T time, and mark the number of scientific research projects completed at different times within T time as XJ m , m represents the number of scientific research projects completed at different times within time T, m = 1, 2, 3, ..., k, k is a positive integer; S112. The number of scientific research projects completed at different times within the time T is obtained. m Modeling is done through exponential smoothing model, and the smoothing coefficient is calibrated as α, 0≤α≤1; S113. Use the exponential smoothing model to fit the number of scientific research project completions at different times within the obtained time T, and predict the number of scientific research project completions at different times after time T, using the exponential smoothing formula: in, is the number of scientific research projects completed at different times after time T, n is the number of scientific research projects completed at different times after time T, n = k+1, k+2, k+3, ..., 2k, k is a positive integer; S114. Calculate the change rate of scientific research project completion. The specific calculation is as follows: Among them, KY bhl It is the change rate of scientific research project completion.

4. The index system evaluation method based on weight design according to claim 3 is characterized in that: The logic for obtaining the trend index of the number of participants in campus activities is as follows: S121. Obtain the number of participants in campus activities at different times within a time period T, and mark the number of participants in campus activities at different times within a time period T as CP x , x represents the number of participants in campus activities at different times in time T, x = 1, 2, 3, ..., j, j is a positive integer; S122, establish the Holt-Winters seasonal exponential smoothing model, obtain the level smoothing coefficient, trend smoothing coefficient, seasonal smoothing coefficient and seasonal cycle, and calibrate the level smoothing coefficient, trend smoothing coefficient, seasonal smoothing coefficient and seasonal cycle to β, γ, δ and τ respectively, and the value ranges of β, γ and δ are all [0, 1]; S123. Establish a time series with the number of participants in campus activities at different times within the time T, obtain the level, trend and seasonality of the number of participants in campus activities at the initial time within the time T, and mark the level, trend and seasonality of the number of participants in campus activities at the initial time within the time T as l1, b1 and s1 respectively, then: S124: For each new time point x after time T, according to the number of participants in the campus activity CP currently obtained x , update level l using the following formula x 、Trend b x and seasonality x : b x =γ*(l x -l x-1 )+(1-c)*b x-1 Among them, β, γ and δ are the level smoothing coefficient, trend smoothing coefficient and seasonal smoothing coefficient respectively, and τ is the length of the seasonal cycle; S125, combined with the latest level l x 、Trend b x and seasonality x The value of the campus activity participation trend index after time T is calculated using the following formula. The specific calculation is shown below: Where h is the time step after T time, and % represents the modulo operation.

5. The index system evaluation method based on weight design according to claim 4 is characterized in that: The processed project management information and social participation information are used to establish a data analysis model, that is, the change rate of scientific research project completion KY bhl and Campus Activity Participation Trend Index HP qs Establish a data analysis model, generate a hidden danger assessment coefficient, and calibrate the hidden danger assessment coefficient as PG yh , according to the formula: In the formula, r1 and r2 are the change rates of scientific research project completion KY bhl and Campus Activity Participation Trend Index HP qs The preset proportional coefficient, and r1 and r2 are both greater than 0.

6. The index system evaluation method based on weight design according to claim 5 is characterized in that: The pre-set threshold of the hidden danger assessment coefficient is calibrated as PG ysyz , the generated hidden danger assessment coefficient PG yh The pre-set hidden danger assessment coefficient threshold PG ysyz Perform comparative analysis to determine whether the weights need to be updated, and generate potential danger signals and normal signals accordingly. The specific comparative analysis is as follows: If PG yh ≤PG ysyz , the weights do not need to be updated, generating normal signals; If PG yh >PG ysyz , the weights need to be updated to generate hidden danger signals.

7. The index system evaluation method based on weight design according to claim 6 is characterized in that: Obtain several hidden danger assessment coefficients generated subsequently when the hidden danger signal is generated, and recalibrate them as z represents the numbers of several hidden danger assessment coefficients subsequently generated when the hidden danger signal is generated, z=1, 2, 3, ..., g, where g is a positive integer; The average value of several hidden danger assessment coefficients is calibrated as PG - , the specific calculation formula is: The standard deviation of several hidden danger assessment coefficients is calibrated as PG σ , the specific calculation formula is:

8. The index system evaluation method based on weight design according to claim 7 is characterized in that: Obtain the preset mean value and preset standard deviation of several hidden danger assessment coefficients generated subsequently when the hidden danger signal is generated, and calibrate the preset mean value and preset standard deviation of several hidden danger assessment coefficients as and The average value of several hidden danger assessment coefficients PG - and standard deviation PG σ Respectively with the preset average and preset standard deviation Compare and analyze, generate corresponding risk level signals, and issue corresponding early warning prompts. The specific analysis is as follows: like Generate high-risk signals and transmit them to the mobile terminal, which will provide high-risk early warning prompts; like and Generate a medium-risk signal and transmit it to the mobile terminal, which will issue a medium-risk warning prompt; like and Generate a low-risk signal and transmit it to the mobile terminal, and issue a low-risk early warning prompt through the mobile terminal.

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